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0e4b0c4a-2a5f-4c91-bb0a-6f6b23aee9ad | automation-of-mathematical-induction-as-part | 1309.6226 | null | http://arxiv.org/abs/1309.6226v5 | http://arxiv.org/pdf/1309.6226v5.pdf | Automation of Mathematical Induction as part of the History of Logic | We review the history of the automation of mathematical induction | ['Claus-Peter Wirth', 'J Strother Moore'] | 2013-09-24 | null | null | null | null | ['mathematical-induction'] | ['reasoning'] | [ 1.81574300e-01 4.88026500e-01 -4.71003801e-01 -7.09474802e-01
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e972dac2-964e-41da-8fd5-628594444e41 | phrase-grounding-by-soft-label-chain | 1909.00301 | null | https://arxiv.org/abs/1909.00301v1 | https://arxiv.org/pdf/1909.00301v1.pdf | Phrase Grounding by Soft-Label Chain Conditional Random Field | The phrase grounding task aims to ground each entity mention in a given caption of an image to a corresponding region in that image. Although there are clear dependencies between how different mentions of the same caption should be grounded, previous structured prediction methods that aim to capture such dependencies need to resort to approximate inference or non-differentiable losses. In this paper, we formulate phrase grounding as a sequence labeling task where we treat candidate regions as potential labels, and use neural chain Conditional Random Fields (CRFs) to model dependencies among regions for adjacent mentions. In contrast to standard sequence labeling tasks, the phrase grounding task is defined such that there may be multiple correct candidate regions. To address this multiplicity of gold labels, we define so-called Soft-Label Chain CRFs, and present an algorithm that enables convenient end-to-end training. Our method establishes a new state-of-the-art on phrase grounding on the Flickr30k Entities dataset. Analysis shows that our model benefits both from the entity dependencies captured by the CRF and from the soft-label training regime. Our code is available at \url{github.com/liujch1998/SoftLabelCCRF} | ['Julia Hockenmaier', 'Jiacheng Liu'] | 2019-09-01 | phrase-grounding-by-soft-label-chain-1 | https://aclanthology.org/D19-1515 | https://aclanthology.org/D19-1515.pdf | ijcnlp-2019-11 | ['phrase-grounding'] | ['natural-language-processing'] | [ 3.63139391e-01 6.40954792e-01 -4.70963269e-01 -6.98557436e-01
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fad69a98-fd04-4d20-bfa4-b5f43aa64726 | compositional-questions-do-not-necessitate | 1906.02900 | null | https://arxiv.org/abs/1906.02900v1 | https://arxiv.org/pdf/1906.02900v1.pdf | Compositional Questions Do Not Necessitate Multi-hop Reasoning | Multi-hop reading comprehension (RC) questions are challenging because they require reading and reasoning over multiple paragraphs. We argue that it can be difficult to construct large multi-hop RC datasets. For example, even highly compositional questions can be answered with a single hop if they target specific entity types, or the facts needed to answer them are redundant. Our analysis is centered on HotpotQA, where we show that single-hop reasoning can solve much more of the dataset than previously thought. We introduce a single-hop BERT-based RC model that achieves 67 F1---comparable to state-of-the-art multi-hop models. We also design an evaluation setting where humans are not shown all of the necessary paragraphs for the intended multi-hop reasoning but can still answer over 80% of questions. Together with detailed error analysis, these results suggest there should be an increasing focus on the role of evidence in multi-hop reasoning and possibly even a shift towards information retrieval style evaluations with large and diverse evidence collections. | ['Sewon Min', 'Luke Zettlemoyer', 'Sameer Singh', 'Hannaneh Hajishirzi', 'Matt Gardner', 'Eric Wallace'] | 2019-06-07 | compositional-questions-do-not-necessitate-1 | https://aclanthology.org/P19-1416 | https://aclanthology.org/P19-1416.pdf | acl-2019-7 | ['multi-hop-reading-comprehension'] | ['natural-language-processing'] | [ 4.47226129e-02 8.69895101e-01 1.38316363e-01 -4.07185972e-01
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0d1404b6-f3c7-46d4-b6b2-3b012c7110ed | explicit-graph-reasoning-fusing-knowledge-and | null | null | https://aclanthology.org/2022.dlg4nlp-1.8 | https://aclanthology.org/2022.dlg4nlp-1.8.pdf | Explicit Graph Reasoning Fusing Knowledge and Contextual Information for Multi-hop Question Answering | Current graph-neural-network-based (GNN-based) approaches to multi-hop questions integrate clues from scattered paragraphs in an entity graph, achieving implicit reasoning by synchronous update of graph node representations using information from neighbours; this is poorly suited for explaining how clues are passed through the graph in hops. In this paper, we describe a structured Knowledge and contextual Information Fusion GNN (KIFGraph) whose explicit multi-hop graph reasoning mimics human step by step reasoning. Specifically, we first integrate clues at multiple levels of granularity (question, paragraph, sentence, entity) as nodes in the graph, connected by edges derived using structured semantic knowledge, then use a contextual encoder to obtain the initial node representations, followed by step-by-step two-stage graph reasoning that asynchronously updates node representations. Each node can be related to its neighbour nodes through fused structured knowledge and contextual information, reliably integrating their answer clues. Moreover, a masked attention mechanism (MAM) filters out noisy or redundant nodes and edges, to avoid ineffective clue propagation in graph reasoning. Experimental results show performance competitive with published models on the HotpotQA dataset. | ['Patricia Riddle', 'Michael Witbrock', 'Qianqian Qi', 'Yonghua Zhu', 'Zhenyun Deng'] | null | null | null | null | naacl-dlg4nlp-2022-7 | ['multi-hop-question-answering'] | ['knowledge-base'] | [ 6.88972697e-02 1.21487725e+00 -4.21999730e-02 -2.64847130e-01
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6914bd1a-07a3-48e3-a6b6-679b021d6baf | a-generic-approach-for-statistical-stability | 2211.12631 | null | https://arxiv.org/abs/2211.12631v3 | https://arxiv.org/pdf/2211.12631v3.pdf | A Generic Approach for Reproducible Model Distillation | Model distillation has been a popular method for producing interpretable machine learning. It uses an interpretable "student" model to mimic the predictions made by the black box "teacher" model. However, when the student model is sensitive to the variability of the data sets used for training even when keeping the teacher fixed, the corresponded interpretation is not reliable. Existing strategies stabilize model distillation by checking whether a large enough corpus of pseudo-data is generated to reliably reproduce student models, but methods to do so have so far been developed for a specific student model. In this paper, we develop a generic approach for stable model distillation based on central limit theorem for the average loss. We start with a collection of candidate student models and search for candidates that reasonably agree with the teacher. Then we construct a multiple testing framework to select a corpus size such that the consistent student model would be selected under different pseudo samples. We demonstrate the application of our proposed approach on three commonly used intelligible models: decision trees, falling rule lists and symbolic regression. Finally, we conduct simulation experiments on Mammographic Mass and Breast Cancer datasets and illustrate the testing procedure throughout a theoretical analysis with Markov process. The code is publicly available at https://github.com/yunzhe-zhou/GenericDistillation. | ['Giles Hooker', 'Peiru Xu', 'Yunzhe Zhou'] | 2022-11-22 | null | null | null | null | ['interpretable-machine-learning'] | ['methodology'] | [ 4.2726478e-01 6.1358017e-01 -3.1768736e-01 -7.2339624e-01
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46e4a1ad-aa2f-49a8-b7de-0c21f44b2bb6 | does-unsupervised-grammar-induction-need | 2212.10564 | null | https://arxiv.org/abs/2212.10564v1 | https://arxiv.org/pdf/2212.10564v1.pdf | Does unsupervised grammar induction need pixels? | Are extralinguistic signals such as image pixels crucial for inducing constituency grammars? While past work has shown substantial gains from multimodal cues, we investigate whether such gains persist in the presence of rich information from large language models (LLMs). We find that our approach, LLM-based C-PCFG (LC-PCFG), outperforms previous multi-modal methods on the task of unsupervised constituency parsing, achieving state-of-the-art performance on a variety of datasets. Moreover, LC-PCFG results in an over 50% reduction in parameter count, and speedups in training time of 1.7x for image-aided models and more than 5x for video-aided models, respectively. These results challenge the notion that extralinguistic signals such as image pixels are needed for unsupervised grammar induction, and point to the need for better text-only baselines in evaluating the need of multi-modality for the task. | ['Dan Klein', 'Trevor Darrell', 'Jitendra Malik', 'Kilian Q. Weinberger', 'Serge Belongie', 'Daniel Flaherty', 'Catherine Chen', 'Karttikeya Mangalam', 'Rodolfo Corona', 'Boyi Li'] | 2022-12-20 | null | null | null | null | ['constituency-parsing'] | ['natural-language-processing'] | [ 6.51857257e-01 4.15338427e-01 -2.70732373e-01 -5.06287694e-01
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cdb2c6a3-6851-4166-b052-3d98e35c21a6 | matching-options-to-tasks-using-option | 2206.05750 | null | https://arxiv.org/abs/2206.05750v1 | https://arxiv.org/pdf/2206.05750v1.pdf | Matching options to tasks using Option-Indexed Hierarchical Reinforcement Learning | The options framework in Hierarchical Reinforcement Learning breaks down overall goals into a combination of options or simpler tasks and associated policies, allowing for abstraction in the action space. Ideally, these options can be reused across different higher-level goals; indeed, such reuse is necessary to realize the vision of a continual learning agent that can effectively leverage its prior experience. Previous approaches have only proposed limited forms of transfer of prelearned options to new task settings. We propose a novel option indexing approach to hierarchical learning (OI-HRL), where we learn an affinity function between options and the items present in the environment. This allows us to effectively reuse a large library of pretrained options, in zero-shot generalization at test time, by restricting goal-directed learning to only those options relevant to the task at hand. We develop a meta-training loop that learns the representations of options and environments over a series of HRL problems, by incorporating feedback about the relevance of retrieved options to the higher-level goal. We evaluate OI-HRL in two simulated settings - the CraftWorld and AI2THOR environments - and show that we achieve performance competitive with oracular baselines, and substantial gains over a baseline that has the entire option pool available for learning the hierarchical policy. | ['Pradeep Shenoy', 'Balaraman Ravindran', 'Akash Reddy', 'Soumya Chatterjee', 'Kushal Chauhan'] | 2022-06-12 | null | null | null | null | ['hierarchical-reinforcement-learning'] | ['methodology'] | [ 1.57419100e-01 1.87491067e-02 -2.68368244e-01 -2.03550503e-01
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8f5a3d60-2144-48d0-b91e-4e0946c9184a | individual-health-disease-phase-diagrams-for | 2205.15598 | null | https://arxiv.org/abs/2205.15598v2 | https://arxiv.org/pdf/2205.15598v2.pdf | Individual health-disease phase diagrams for disease prevention based on machine learning | Early disease detection and prevention methods based on effective interventions are gaining attention. Machine learning technology has enabled precise disease prediction by capturing individual differences in multivariate data. Progress in precision medicine has revealed that substantial heterogeneity exists in health data at the individual level and that complex health factors are involved in the development of chronic diseases. However, it remains a challenge to identify individual physiological state changes in cross-disease onset processes because of the complex relationships among multiple biomarkers. Here, we present the health-disease phase diagram (HDPD), which represents a personal health state by visualizing the boundary values of multiple biomarkers that fluctuate early in the disease progression process. In HDPDs, future onset predictions are represented by perturbing multiple biomarker values while accounting for dependencies among variables. We constructed HDPDs for 11 non-communicable diseases (NCDs) from a longitudinal health checkup cohort of 3,238 individuals, comprising 3,215 measurement items and genetic data. Improvement of biomarker values to the non-onset region in HDPD significantly prevented future disease onset in 7 out of 11 NCDs. Our results demonstrate that HDPDs can represent individual physiological states in the onset process and be used as intervention goals for disease prevention. | ['Yasushi Okuno', 'Yoshinori Tamada', 'Tatsuya Mikami', 'Ken Itoh', 'Koichi Murashita', 'Ryosuke Kojima', 'Kei Terayama', 'Ayano Araki', 'Noriaki Sato', 'Eiichiro Uchino', 'Kazuki Nakamura'] | 2022-05-31 | null | null | null | null | ['disease-prediction'] | ['medical'] | [ 3.01671147e-01 -4.24059212e-01 -7.01976657e-01 -2.85230041e-01
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1fbf2be7-bd36-4245-b64a-e2c87df958f8 | tackling-background-distraction-in-video | 2207.06953 | null | https://arxiv.org/abs/2207.06953v3 | https://arxiv.org/pdf/2207.06953v3.pdf | Tackling Background Distraction in Video Object Segmentation | Semi-supervised video object segmentation (VOS) aims to densely track certain designated objects in videos. One of the main challenges in this task is the existence of background distractors that appear similar to the target objects. We propose three novel strategies to suppress such distractors: 1) a spatio-temporally diversified template construction scheme to obtain generalized properties of the target objects; 2) a learnable distance-scoring function to exclude spatially-distant distractors by exploiting the temporal consistency between two consecutive frames; 3) swap-and-attach augmentation to force each object to have unique features by providing training samples containing entangled objects. On all public benchmark datasets, our model achieves a comparable performance to contemporary state-of-the-art approaches, even with real-time performance. Qualitative results also demonstrate the superiority of our approach over existing methods. We believe our approach will be widely used for future VOS research. | ['Sangyoun Lee', 'Minjung Kim', 'Sungjun Jang', 'Chaewon Park', 'Minhyeok Lee', 'Heansung Lee', 'Suhwan Cho'] | 2022-07-14 | null | null | null | null | ['semi-supervised-video-object-segmentation'] | ['computer-vision'] | [ 2.23917678e-01 -1.29059538e-01 -3.53636980e-01 -7.33527765e-02
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22336dad-6930-4ee5-afd9-acd35061db30 | exploiting-coreference-and-schema-structure | null | null | https://openreview.net/forum?id=yQvObmRwUAP | https://openreview.net/pdf?id=yQvObmRwUAP | Exploiting Coreference and Schema Structure for Document-level Event Extraction | Document-level event extraction (DEE) extracts structured information of events from a document. Previous studies focus on improving the model architecture. We argue that exploiting data characteristics is also important. We propose to utilize coreference information to obtain better document-level entity representations, and propose the concept of core roles to adjust the schema structure to alleviate error propagation. Experiments demonstrate that our data exploitation methods significantly improve the performance of existing models on both the role-level and record-level metrics. | ['Anonymous'] | 2022-01-16 | null | null | null | acl-arr-january-2022-1 | ['document-level-event-extraction'] | ['natural-language-processing'] | [ 2.26113692e-01 3.65374357e-01 -7.53537416e-01 -6.87472463e-01
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47643992-fea6-4e2b-a16b-bcc5f2ef83e2 | foon-creation-and-traversal-for-recipe | 2210.07335 | null | https://arxiv.org/abs/2210.07335v2 | https://arxiv.org/pdf/2210.07335v2.pdf | FOON Creation and Traversal for Recipe Generation | Task competition by robots is still off from being completely dependable and usable. One way a robot may decipher information given to it and accomplish tasks is by utilizing FOON, which stands for functional object-oriented network. The network first needs to be created by having a human creates action nodes as well as input and output nodes in a .txt file. After the network is sizeable, utilization of this network allows for traversal of the network in a variety of ways such as choosing steps via iterative deepening searching by using the first seen valid option. Another mechanism is heuristics, such as choosing steps based on the highest success rate or lowest amount of input ingredients. Via any of these methods, a program can traverse the network given an output product, and derive the series of steps that need to be taken to produce the output. | ['Raj Patel'] | 2022-10-13 | null | null | null | null | ['recipe-generation'] | ['miscellaneous'] | [ 4.84088272e-01 4.88691688e-01 -2.73257494e-01 -2.84930229e-01
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01dc8ce9-ac7c-49cf-a1c0-0f94e39b9575 | 190602010 | 1906.02010 | null | https://arxiv.org/abs/1906.02010v1 | https://arxiv.org/pdf/1906.02010v1.pdf | A Hybrid Algorithm for Metaheuristic Optimization | We propose a novel, flexible algorithm for combining together metaheuristicoptimizers for non-convex optimization problems. Our approach treatsthe constituent optimizers as a team of complex agents that communicateinformation amongst each other at various intervals during the simulationprocess. The information produced by each individual agent can be combinedin various ways via higher-level operators. In our experiments on keybenchmark functions, we investigate how the performance of our algorithmvaries with respect to several of its key modifiable properties. Finally,we apply our proposed algorithm to classification problems involving theoptimization of support-vector machine classifiers. | ['Sujit Pramod Khanna', 'Alexander Ororbia II'] | 2019-05-26 | null | null | null | null | ['metaheuristic-optimization'] | ['methodology'] | [-5.45082875e-02 2.33510062e-02 -3.00172329e-01 -6.41245320e-02
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edac73fe-38d8-402b-aa75-2125836947d7 | questioning-the-validity-of-summarization | 2210.17378 | null | https://arxiv.org/abs/2210.17378v1 | https://arxiv.org/pdf/2210.17378v1.pdf | Questioning the Validity of Summarization Datasets and Improving Their Factual Consistency | The topic of summarization evaluation has recently attracted a surge of attention due to the rapid development of abstractive summarization systems. However, the formulation of the task is rather ambiguous, neither the linguistic nor the natural language processing community has succeeded in giving a mutually agreed-upon definition. Due to this lack of well-defined formulation, a large number of popular abstractive summarization datasets are constructed in a manner that neither guarantees validity nor meets one of the most essential criteria of summarization: factual consistency. In this paper, we address this issue by combining state-of-the-art factual consistency models to identify the problematic instances present in popular summarization datasets. We release SummFC, a filtered summarization dataset with improved factual consistency, and demonstrate that models trained on this dataset achieve improved performance in nearly all quality aspects. We argue that our dataset should become a valid benchmark for developing and evaluating summarization systems. | ['Michalis Vazirgiannis', 'Moussa Kamal Eddine', 'Chloé Clavel', 'Yanzhu Guo'] | 2022-10-31 | null | null | null | null | ['abstractive-text-summarization'] | ['natural-language-processing'] | [ 3.61446112e-01 1.67563304e-01 -2.63929248e-01 -2.68745631e-01
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8151dbaf-2e2f-4051-b7ba-36e4cb48809e | barycenters-of-natural-images-constrained | 1912.11545 | null | https://arxiv.org/abs/1912.11545v1 | https://arxiv.org/pdf/1912.11545v1.pdf | Barycenters of Natural Images -- Constrained Wasserstein Barycenters for Image Morphing | Image interpolation, or image morphing, refers to a visual transition between two (or more) input images. For such a transition to look visually appealing, its desirable properties are (i) to be smooth; (ii) to apply the minimal required change in the image; and (iii) to seem "real", avoiding unnatural artifacts in each image in the transition. To obtain a smooth and straightforward transition, one may adopt the well-known Wasserstein Barycenter Problem (WBP). While this approach guarantees minimal changes under the Wasserstein metric, the resulting images might seem unnatural. In this work, we propose a novel approach for image morphing that possesses all three desired properties. To this end, we define a constrained variant of the WBP that enforces the intermediate images to satisfy an image prior. We describe an algorithm that solves this problem and demonstrate it using the sparse prior and generative adversarial networks. | ['Dror Simon', 'Aviad Aberdam'] | 2019-12-24 | null | null | null | null | ['image-morphing'] | ['computer-vision'] | [ 5.69100916e-01 3.77516121e-01 2.51802385e-01 -2.33149186e-01
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dc690198-a84f-4c33-80ea-3607055fca6c | distributionally-robust-multi-output | 2109.12803 | null | https://arxiv.org/abs/2109.12803v1 | https://arxiv.org/pdf/2109.12803v1.pdf | Distributionally Robust Multi-Output Regression Ranking | Despite 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. | ['Ioannis Paschalidis', 'Ruidi Chen', 'Shahabeddin Sotudian'] | 2021-09-27 | null | null | null | null | ['drug-response-prediction'] | ['medical'] | [ 4.43157017e-01 -4.12702411e-01 -3.68933350e-01 -3.82745981e-01
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89344e93-216a-472b-b761-10b6d0d093e3 | online-invariance-selection-for-local-feature | 2007.08988 | null | https://arxiv.org/abs/2007.08988v3 | https://arxiv.org/pdf/2007.08988v3.pdf | Online Invariance Selection for Local Feature Descriptors | To be invariant, or not to be invariant: that is the question formulated in this work about local descriptors. A limitation of current feature descriptors is the trade-off between generalization and discriminative power: more invariance means less informative descriptors. We propose to overcome this limitation with a disentanglement of invariance in local descriptors and with an online selection of the most appropriate invariance given the context. Our framework consists in a joint learning of multiple local descriptors with different levels of invariance and of meta descriptors encoding the regional variations of an image. The similarity of these meta descriptors across images is used to select the right invariance when matching the local descriptors. Our approach, named Local Invariance Selection at Runtime for Descriptors (LISRD), enables descriptors to adapt to adverse changes in images, while remaining discriminative when invariance is not required. We demonstrate that our method can boost the performance of current descriptors and outperforms state-of-the-art descriptors in several matching tasks, when evaluated on challenging datasets with day-night illumination as well as viewpoint changes. | ['Marc Pollefeys', 'Rémi Pautrat', 'Viktor Larsson', 'Martin R. Oswald'] | 2020-07-17 | null | https://www.ecva.net/papers/eccv_2020/papers_ECCV/html/4158_ECCV_2020_paper.php | https://www.ecva.net/papers/eccv_2020/papers_ECCV/papers/123470698.pdf | eccv-2020-8 | ['homography-estimation'] | ['computer-vision'] | [ 2.16346160e-01 -8.77841413e-01 -3.01446021e-01 -6.72449648e-01
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89bde8e8-0286-4f5b-aae4-551e8488f8fe | skin-lesion-classification-using-deep-multi | 1703.01402 | null | http://arxiv.org/abs/1703.01402v1 | http://arxiv.org/pdf/1703.01402v1.pdf | Skin Lesion Classification Using Deep Multi-scale Convolutional Neural Networks | We 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. | ['Terrance DeVries', 'Dhanesh Ramachandram'] | 2017-03-04 | null | null | null | null | ['skin-lesion-classification'] | ['medical'] | [ 7.79246151e-01 -1.56791255e-01 -4.23917443e-01 -6.07718974e-02
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3c81d849-318f-4cd0-971b-90656ca62dcc | contextual-dialogue-act-classification-for | 2005.13804 | null | https://arxiv.org/abs/2005.13804v1 | https://arxiv.org/pdf/2005.13804v1.pdf | Contextual Dialogue Act Classification for Open-Domain Conversational Agents | Classifying the general intent of the user utterance in a conversation, also known as Dialogue Act (DA), e.g., open-ended question, statement of opinion, or request for an opinion, is a key step in Natural Language Understanding (NLU) for conversational agents. While DA classification has been extensively studied in human-human conversations, it has not been sufficiently explored for the emerging open-domain automated conversational agents. Moreover, despite significant advances in utterance-level DA classification, full understanding of dialogue utterances requires conversational context. Another challenge is the lack of available labeled data for open-domain human-machine conversations. To address these problems, we propose a novel method, CDAC (Contextual Dialogue Act Classifier), a simple yet effective deep learning approach for contextual dialogue act classification. Specifically, we use transfer learning to adapt models trained on human-human conversations to predict dialogue acts in human-machine dialogues. To investigate the effectiveness of our method, we train our model on the well-known Switchboard human-human dialogue dataset, and fine-tune it for predicting dialogue acts in human-machine conversation data, collected as part of the Amazon Alexa Prize 2018 competition. The results show that the CDAC model outperforms an utterance-level state of the art baseline by 8.0% on the Switchboard dataset, and is comparable to the latest reported state-of-the-art contextual DA classification results. Furthermore, our results show that fine-tuning the CDAC model on a small sample of manually labeled human-machine conversations allows CDAC to more accurately predict dialogue acts in real users' conversations, suggesting a promising direction for future improvements. | ['Eugene Agichtein', 'Ali Ahmadvand', 'Jason Ingyu Choi'] | 2020-05-28 | null | null | null | null | ['dialogue-act-classification'] | ['natural-language-processing'] | [ 2.39491776e-01 4.62834775e-01 -6.28150553e-02 -6.74126863e-01
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5de8ee27-1e65-4ad8-a73d-5939c9760c66 | filteraugment-an-acoustic-environmental-data | 2110.03282 | null | https://arxiv.org/abs/2110.03282v4 | https://arxiv.org/pdf/2110.03282v4.pdf | FilterAugment: An Acoustic Environmental Data Augmentation Method | Acoustic environments affect acoustic characteristics of sound to be recognized by physically interacting with sound wave propagation. Thus, training acoustic models for audio and speech tasks requires regularization on various acoustic environments in order to achieve robust performance in real life applications. We propose FilterAugment, a data augmentation method for regularization of acoustic models on various acoustic environments. FilterAugment mimics acoustic filters by applying different weights on frequency bands, therefore enables model to extract relevant information from wider frequency region. It is an improved version of frequency masking which masks information on random frequency bands. FilterAugment improved sound event detection (SED) model performance by 6.50% while frequency masking only improved 2.13% in terms of polyphonic sound detection score (PSDS). It achieved equal error rate (EER) of 1.22% when applied to a text-independent speaker verification model, outperforming model used frequency masking with EER of 1.26%. Prototype of FilterAugment was applied in our participation in DCASE 2021 challenge task 4, and played a major role in achieving the 3rd rank. | ['Seong-Hu Kim', 'Yong-Hwa Park', 'Hyeonuk Nam'] | 2021-10-07 | null | null | null | null | ['text-independent-speaker-verification'] | ['speech'] | [ 2.02981874e-01 -1.13777094e-01 5.32194197e-01 -1.40407845e-01
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9d333587-5ab2-49d2-ad4e-7608a1f5c4c9 | constrained-optimization-under-uncertainty | 1901.00942 | null | http://arxiv.org/abs/1901.00942v3 | http://arxiv.org/pdf/1901.00942v3.pdf | Constrained optimization under uncertainty for decision-making problems: Application to Real-Time Strategy games | Decision-making problems can be modeled as combinatorial optimization
problems with Constraint Programming formalisms such as Constrained
Optimization Problems. However, few Constraint Programming formalisms can deal
with both optimization and uncertainty at the same time, and none of them are
convenient to model problems we tackle in this paper.
Here, we propose a way to deal with combinatorial optimization problems under
uncertainty within the classical Constrained Optimization Problems formalism by
injecting the Rank Dependent Utility from decision theory. We also propose a
proof of concept of our method to show it is implementable and can solve
concrete decision-making problems using a regular constraint solver, and
propose a bot that won the partially observable track of the 2018 {\mu}RTS AI
competition.
Our result shows it is possible to handle uncertainty with regular Constraint
Programming solvers, without having to define a new formalism neither to
develop dedicated solvers. This brings new perspective to tackle uncertainty in
Constraint Programming. | ['Florian Richoux', 'Valentin Antuori'] | 2019-01-03 | null | null | null | null | ['real-time-strategy-games'] | ['playing-games'] | [ 1.10573478e-01 8.21595073e-01 -7.13111311e-02 -4.66454625e-01
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57e62322-6af9-42ea-9ea3-9c7bda735823 | higher-order-motif-based-time-series | 2306.13397 | null | https://arxiv.org/abs/2306.13397v1 | https://arxiv.org/pdf/2306.13397v1.pdf | Higher-order Motif-based Time Series Classification for Forced Oscillation Source Location in Power Grids | Time series motifs are used for discovering higher-order structures of time series data. Based on time series motifs, the motif embedding correlation field (MECF) is proposed to characterize higher-order temporal structures of dynamical system time series. A MECF-based unsupervised learning approach is applied in locating the source of the forced oscillation (FO), a periodic disturbance that detrimentally impacts power grids. Locating the FO source is imperative for system stability. Compared with the Fourier analysis, the MECF-based unsupervised learning is applicable under various FO situations, including the single FO, FO with resonance, and multiple sources FOs. The MECF-based unsupervised learning is a data-driven approach without any prior knowledge requirement of system models or typologies. Tests on the UK high-voltage transmission grid illustrate the effectiveness of MECF-based unsupervised learning. In addition, the impacts of coupling strength and measurement noise on locating the FO source by the MECF-based unsupervised learning are investigated. | ['Xin Chen', 'Long Huo'] | 2023-06-23 | null | null | null | null | ['time-series-classification'] | ['time-series'] | [ 1.26496702e-01 -3.57975662e-01 1.51033074e-01 3.56935591e-01
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e7248bd0-c0d7-4e88-85cd-25e0c1ba7874 | unleashing-potential-of-unsupervised-pre | null | null | http://openaccess.thecvf.com//content/CVPR2022/html/Yang_Unleashing_Potential_of_Unsupervised_Pre-Training_With_Intra-Identity_Regularization_for_Person_CVPR_2022_paper.html | http://openaccess.thecvf.com//content/CVPR2022/papers/Yang_Unleashing_Potential_of_Unsupervised_Pre-Training_With_Intra-Identity_Regularization_for_Person_CVPR_2022_paper.pdf | Unleashing Potential of Unsupervised Pre-Training With Intra-Identity Regularization for Person Re-Identification | Existing person re-identification (ReID) methods typically directly load the pre-trained ImageNet weights for initialization. However, as a fine-grained classification task, ReID is more challenging and exists a large domain gap between ImageNet classification. Inspired by the great success of self-supervised representation learning with contrastive objectives, in this paper, we design an Unsupervised Pre-training framework for ReID based on the contrastive learning (CL) pipeline, dubbed UP-ReID. During the pre-training, we attempt to address two critical issues for learning fine-grained ReID features: (1) the augmentations in CL pipeline may distort the discriminative clues in person images. (2) the fine-grained local features of person images are not fully-explored. Therefore, we introduce an (I^2-)regularization in the UP-ReID, which is instantiated as two constraints coming from global image aspect and local patch aspect: a global consistency is enforced between augmented and original person images to increase robustness to augmentation, while an intrinsic contrastive constraint among local patches of each image is employed to fully explore the local discriminative clues. Extensive experiments on multiple popular Re-ID datasets, including PersonX, Market1501, CUHK03, and MSMT17, demonstrate that our UP-ReID pre-trained model can significantly benefit the downstream ReID fine-tuning and achieve state-of-the-art performance. | ['Feng Zhao', 'Kecheng Zheng', 'Xin Jin', 'Zizheng Yang'] | 2022-01-01 | null | null | null | cvpr-2022-1 | ['unsupervised-pre-training'] | ['methodology'] | [-2.14267354e-02 -2.66439080e-01 -2.03018654e-02 -6.23698592e-01
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a5dc38d2-dac5-4147-8aea-b7754a199bcf | real-time-hybrid-mapping-of-populated-indoor | 2203.02453 | null | https://arxiv.org/abs/2203.02453v1 | https://arxiv.org/pdf/2203.02453v1.pdf | Real-Time Hybrid Mapping of Populated Indoor Scenes using a Low-Cost Monocular UAV | Unmanned aerial vehicles (UAVs) have been used for many applications in recent years, from urban search and rescue, to agricultural surveying, to autonomous underground mine exploration. However, deploying UAVs in tight, indoor spaces, especially close to humans, remains a challenge. One solution, when limited payload is required, is to use micro-UAVs, which pose less risk to humans and typically cost less to replace after a crash. However, micro-UAVs can only carry a limited sensor suite, e.g. a monocular camera instead of a stereo pair or LiDAR, complicating tasks like dense mapping and markerless multi-person 3D human pose estimation, which are needed to operate in tight environments around people. Monocular approaches to such tasks exist, and dense monocular mapping approaches have been successfully deployed for UAV applications. However, despite many recent works on both marker-based and markerless multi-UAV single-person motion capture, markerless single-camera multi-person 3D human pose estimation remains a much earlier-stage technology, and we are not aware of existing attempts to deploy it in an aerial context. In this paper, we present what is thus, to our knowledge, the first system to perform simultaneous mapping and multi-person 3D human pose estimation from a monocular camera mounted on a single UAV. In particular, we show how to loosely couple state-of-the-art monocular depth estimation and monocular 3D human pose estimation approaches to reconstruct a hybrid map of a populated indoor scene in real time. We validate our component-level design choices via extensive experiments on the large-scale ScanNet and GTA-IM datasets. To evaluate our system-level performance, we also construct a new Oxford Hybrid Mapping dataset of populated indoor scenes. | ['Niki Trigoni', 'Andrew Markham', 'Sangyun Shin', 'Aluna Everitt', 'Madhu Vankadari', 'Stuart Golodetz'] | 2022-03-04 | null | null | null | null | ['monocular-3d-human-pose-estimation'] | ['computer-vision'] | [-1.34349167e-02 -2.12770432e-01 3.63036811e-01 -1.87003195e-01
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4b799ffa-fa79-4c79-800e-882b04d555c6 | temporal-driver-action-localization-using | null | null | https://openaccess.thecvf.com/content/CVPR2022W/AICity/html/Alyahya_Temporal_Driver_Action_Localization_Using_Action_Classification_Methods_CVPRW_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022W/AICity/papers/Alyahya_Temporal_Driver_Action_Localization_Using_Action_Classification_Methods_CVPRW_2022_paper.pdf | temporal driver action Localization using action classifications method | Driver distraction recognition is an essential computer vision task that can play a key role in increasing traffic safety and reducing traffic accidents. In this paper, we propose a temporal driver action localization (TDAL) framework for classifying driver distraction actions, as well as identifying the start and end time of a given driver action. The TDAL framework consists of three stages: preprocessing, which takes untrimmed video as input and generates multiple clips; action classification, which classifies the clips; and finally, the classifier output is sent to the temporal action localization to generate the start and end times of the distracted actions. The proposed framework achieves an F1 score of 27.06% on Track 3 A2 dataset of NVIDIA AI City 2022 Challenge. The findings show that the TDAL framework contributes to fine-grained driver distraction recognition and paves the way for the development of smart and safe transportation. | ['Taghreed Alhussan', 'Shahad Alghannam', 'Munirah Alyahya'] | 2022-06-11 | null | null | null | cvpr-2022-6 | ['action-classification', 'action-localization'] | ['computer-vision', 'computer-vision'] | [ 1.71824157e-01 -4.85216260e-01 -2.89400190e-01 -3.77796710e-01
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1bc7edf1-5f26-494c-beb9-bd286244d227 | safe-exploration-method-for-reinforcement | 2209.15452 | null | https://arxiv.org/abs/2209.15452v2 | https://arxiv.org/pdf/2209.15452v2.pdf | Safe Exploration Method for Reinforcement Learning under Existence of Disturbance | Recent rapid developments in reinforcement learning algorithms have been giving us novel possibilities in many fields. However, due to their exploring property, we have to take the risk into consideration when we apply those algorithms to safety-critical problems especially in real environments. In this study, we deal with a safe exploration problem in reinforcement learning under the existence of disturbance. We define the safety during learning as satisfaction of the constraint conditions explicitly defined in terms of the state and propose a safe exploration method that uses partial prior knowledge of a controlled object and disturbance. The proposed method assures the satisfaction of the explicit state constraints with a pre-specified probability even if the controlled object is exposed to a stochastic disturbance following a normal distribution. As theoretical results, we introduce sufficient conditions to construct conservative inputs not containing an exploring aspect used in the proposed method and prove that the safety in the above explained sense is guaranteed with the proposed method. Furthermore, we illustrate the validity and effectiveness of the proposed method through numerical simulations of an inverted pendulum and a four-bar parallel link robot manipulator. | ['Toru Namerikawa', 'Hitoshi Yanami', 'Tomotake Sasaki', 'Yoshihiro Okawa'] | 2022-09-30 | null | null | null | null | ['safe-exploration'] | ['robots'] | [ 3.61526519e-01 5.01149833e-01 -2.13478193e-01 1.60938844e-01
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9f65906d-225b-458c-b0a5-a6736b8aa5a9 | fast-classification-of-small-x-ray | 1811.08425 | null | http://arxiv.org/abs/1811.08425v2 | http://arxiv.org/pdf/1811.08425v2.pdf | Fast and interpretable classification of small X-ray diffraction datasets using data augmentation and deep neural networks | X-ray diffraction (XRD) data acquisition and analysis is among the most
time-consuming steps in the development cycle of novel thin-film materials. We
propose a machine-learning-enabled approach to predict crystallographic
dimensionality and space group from a limited number of thin-film XRD patterns.
We overcome the scarce-data problem intrinsic to novel materials development by
coupling a supervised machine learning approach with a model agnostic,
physics-informed data augmentation strategy using simulated data from the
Inorganic Crystal Structure Database (ICSD) and experimental data. As a test
case, 115 thin-film metal halides spanning 3 dimensionalities and 7
space-groups are synthesized and classified. After testing various algorithms,
we develop and implement an all convolutional neural network, with cross
validated accuracies for dimensionality and space-group classification of 93%
and 89%, respectively. We propose average class activation maps, computed from
a global average pooling layer, to allow high model interpretability by human
experimentalists, elucidating the root causes of misclassification. Finally, we
systematically evaluate the maximum XRD pattern step size (data acquisition
rate) before loss of predictive accuracy occurs, and determine it to be
0.16{\deg}, which enables an XRD pattern to be obtained and classified in 5.5
minutes or less. | ['Noor Titan Putri Hartono', 'Zhe Liu', 'Charlie Settens', 'Siyu I. P. Tian', 'Aaron Gilad Kusne', 'Tonio Buonassisi', 'Felipe Oviedo', 'Brian L. DeCost', 'Ramasamy Savitha', 'Zekun Ren', 'Shijing Sun', 'Giuseppe Romano'] | 2018-11-20 | null | null | null | null | ['x-ray-diffraction'] | ['miscellaneous'] | [ 4.60212678e-01 1.55269980e-01 -1.41939491e-01 -5.17777085e-01
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aec931ca-cb08-4c1c-93ef-df75e0dcf149 | federated-learning-over-harmonized-data-silos | 2305.08985 | null | https://arxiv.org/abs/2305.08985v1 | https://arxiv.org/pdf/2305.08985v1.pdf | Federated Learning over Harmonized Data Silos | Federated Learning is a distributed machine learning approach that enables geographically distributed data silos to collaboratively learn a joint machine learning model without sharing data. Most of the existing work operates on unstructured data, such as images or text, or on structured data assumed to be consistent across the different sites. However, sites often have different schemata, data formats, data values, and access patterns. The field of data integration has developed many methods to address these challenges, including techniques for data exchange and query rewriting using declarative schema mappings, and for entity linkage. Therefore, we propose an architectural vision for an end-to-end Federated Learning and Integration system, incorporating the critical steps of data harmonization and data imputation, to spur further research on the intersection of data management information systems and machine learning. | ['Jose Luis Ambite', 'Dimitris Stripelis'] | 2023-05-15 | null | null | null | null | ['data-integration'] | ['knowledge-base'] | [-4.10796642e-01 9.73389894e-02 -6.78174794e-01 -8.42852235e-01
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f32efef0-df17-4077-a9d0-92d392fab672 | e2timt-efficient-and-effective-modal-adapter | 2305.05166 | null | https://arxiv.org/abs/2305.05166v2 | https://arxiv.org/pdf/2305.05166v2.pdf | E2TIMT: Efficient and Effective Modal Adapter for Text Image Machine Translation | Text image machine translation (TIMT) aims to translate texts embedded in images from one source language to another target language. Existing methods, both two-stage cascade and one-stage end-to-end architectures, suffer from different issues. The cascade models can benefit from the large-scale optical character recognition (OCR) and MT datasets but the two-stage architecture is redundant. The end-to-end models are efficient but suffer from training data deficiency. To this end, in our paper, we propose an end-to-end TIMT model fully making use of the knowledge from existing OCR and MT datasets to pursue both an effective and efficient framework. More specifically, we build a novel modal adapter effectively bridging the OCR encoder and MT decoder. End-to-end TIMT loss and cross-modal contrastive loss are utilized jointly to align the feature distribution of the OCR and MT tasks. Extensive experiments show that the proposed method outperforms the existing two-stage cascade models and one-stage end-to-end models with a lighter and faster architecture. Furthermore, the ablation studies verify the generalization of our method, where the proposed modal adapter is effective to bridge various OCR and MT models. | ['Chengqing Zong', 'Yu Zhou', 'Yang Zhao', 'Mei Tu', 'Yaping Zhang', 'Cong Ma'] | 2023-05-09 | null | null | null | null | ['optical-character-recognition'] | ['computer-vision'] | [ 3.69036555e-01 -3.54284197e-01 -1.56019241e-01 -4.10533994e-01
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8f93bf55-38d9-48cf-9179-298d6c34cfab | explainable-predictive-process-monitoring-a | 2202.07760 | null | https://arxiv.org/abs/2202.07760v1 | https://arxiv.org/pdf/2202.07760v1.pdf | Explainable Predictive Process Monitoring: A User Evaluation | Explainability is motivated by the lack of transparency of black-box Machine Learning approaches, which do not foster trust and acceptance of Machine Learning algorithms. This also happens in the Predictive Process Monitoring field, where predictions, obtained by applying Machine Learning techniques, need to be explained to users, so as to gain their trust and acceptance. In this work, we carry on a user evaluation on explanation approaches for Predictive Process Monitoring aiming at investigating whether and how the explanations provided (i) are understandable; (ii) are useful in decision making tasks;(iii) can be further improved for process analysts, with different Machine Learning expertise levels. The results of the user evaluation show that, although explanation plots are overall understandable and useful for decision making tasks for Business Process Management users -- with and without experience in Machine Learning -- differences exist in the comprehension and usage of different plots, as well as in the way users with different Machine Learning expertise understand and use them. | ['Alexander Nolte', 'Fabrizio Maria Maggi', 'Suhwan Lee', 'Chiara Ghidini', 'Chiara Di Francescomarino', 'Marco Comuzzi', 'Williams Rizzi'] | 2022-02-15 | null | null | null | null | ['predictive-process-monitoring'] | ['time-series'] | [ 1.18969046e-01 9.46109235e-01 4.83009703e-02 -4.90486294e-01
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91499e99-83b9-4a9d-ab0e-10a647b3b49a | improving-online-continual-learning | 2306.16817 | null | https://arxiv.org/abs/2306.16817v2 | https://arxiv.org/pdf/2306.16817v2.pdf | Improving Online Continual Learning Performance and Stability with Temporal Ensembles | Neural networks are very effective when trained on large datasets for a large number of iterations. However, when they are trained on non-stationary streams of data and in an online fashion, their performance is reduced (1) by the online setup, which limits the availability of data, (2) due to catastrophic forgetting because of the non-stationary nature of the data. Furthermore, several recent works (Caccia et al., 2022; Lange et al., 2023) arXiv:2205.13452 showed that replay methods used in continual learning suffer from the stability gap, encountered when evaluating the model continually (rather than only on task boundaries). In this article, we study the effect of model ensembling as a way to improve performance and stability in online continual learning. We notice that naively ensembling models coming from a variety of training tasks increases the performance in online continual learning considerably. Starting from this observation, and drawing inspirations from semi-supervised learning ensembling methods, we use a lightweight temporal ensemble that computes the exponential moving average of the weights (EMA) at test time, and show that it can drastically increase the performance and stability when used in combination with several methods from the literature. | ['Joost Van de Weijer', 'Antonio Carta', 'Albin Soutif--Cormerais'] | 2023-06-29 | null | null | null | null | ['continual-learning'] | ['methodology'] | [ 4.41406332e-02 -5.70455529e-02 2.05827475e-01 -8.43129903e-02
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3608828d-2669-410d-b7f8-8e3ce11738ff | joint-learning-of-multiple-image-restoration | 1907.04508 | null | https://arxiv.org/abs/1907.04508v2 | https://arxiv.org/pdf/1907.04508v2.pdf | Restoring Images with Unknown Degradation Factors by Recurrent Use of a Multi-branch Network | The employment of convolutional neural networks has achieved unprecedented performance in the task of image restoration for a variety of degradation factors. However, high-performance networks have been specifically designed for a single degradation factor. In this paper, we tackle a harder problem, restoring a clean image from its degraded version with an unknown degradation factor, subject to the condition that it is one of the known factors. Toward this end, we design a network having multiple pairs of input and output branches and use it in a recurrent fashion such that a different branch pair is used at each of the recurrent paths. We reinforce the shared part of the network with improved components so that it can handle different degradation factors. We also propose a two-step training method for the network, which consists of multi-task learning and finetuning. The experimental results show that the proposed network yields at least comparable or sometimes even better performance on four degradation factors as compared with the best dedicated network for each of the four. We also test it on a further harder task where the input image contains multiple degradation factors that are mixed with unknown mixture ratios, showing that it achieves better performance than the previous state-of-the-art method designed for the task. | ['Takayuki Okatani', 'Masanori Suganuma', 'Xiyang Luo', 'Xing Liu'] | 2019-07-10 | null | null | null | null | ['jpeg-artifact-removal'] | ['computer-vision'] | [ 4.33201998e-01 -1.90035462e-01 1.35198891e-01 -8.98604617e-02
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2e2e3e51-9a38-4fe9-aa86-747d571c95b7 | dropmae-masked-autoencoders-with-spatial | 2304.00571 | null | https://arxiv.org/abs/2304.00571v2 | https://arxiv.org/pdf/2304.00571v2.pdf | DropMAE: Masked Autoencoders with Spatial-Attention Dropout for Tracking Tasks | In this paper, we study masked autoencoder (MAE) pretraining on videos for matching-based downstream tasks, including visual object tracking (VOT) and video object segmentation (VOS). A simple extension of MAE is to randomly mask out frame patches in videos and reconstruct the frame pixels. However, we find that this simple baseline heavily relies on spatial cues while ignoring temporal relations for frame reconstruction, thus leading to sub-optimal temporal matching representations for VOT and VOS. To alleviate this problem, we propose DropMAE, which adaptively performs spatial-attention dropout in the frame reconstruction to facilitate temporal correspondence learning in videos. We show that our DropMAE is a strong and efficient temporal matching learner, which achieves better finetuning results on matching-based tasks than the ImageNetbased MAE with 2X faster pre-training speed. Moreover, we also find that motion diversity in pre-training videos is more important than scene diversity for improving the performance on VOT and VOS. Our pre-trained DropMAE model can be directly loaded in existing ViT-based trackers for fine-tuning without further modifications. Notably, DropMAE sets new state-of-the-art performance on 8 out of 9 highly competitive video tracking and segmentation datasets. Our code and pre-trained models are available at https://github.com/jimmy-dq/DropMAE.git. | ['Antoni B. Chan', 'Ying Shan', 'Baoyuan Wu', 'Ziquan Liu', 'Tianyu Yang', 'Qiangqiang Wu'] | 2023-04-02 | null | http://openaccess.thecvf.com//content/CVPR2023/html/Wu_DropMAE_Masked_Autoencoders_With_Spatial-Attention_Dropout_for_Tracking_Tasks_CVPR_2023_paper.html | http://openaccess.thecvf.com//content/CVPR2023/papers/Wu_DropMAE_Masked_Autoencoders_With_Spatial-Attention_Dropout_for_Tracking_Tasks_CVPR_2023_paper.pdf | cvpr-2023-1 | ['video-object-segmentation', 'video-semantic-segmentation', 'visual-object-tracking'] | ['computer-vision', 'computer-vision', 'computer-vision'] | [-1.56283259e-01 -3.33050489e-01 -4.95275050e-01 -1.88263401e-01
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0d4e7cfa-cb03-41c9-affe-d1dae0a2bf4c | active-teacher-for-semi-supervised-object-1 | 2303.08348 | null | https://arxiv.org/abs/2303.08348v1 | https://arxiv.org/pdf/2303.08348v1.pdf | Active Teacher for Semi-Supervised Object Detection | In this paper, we study teacher-student learning from the perspective of data initialization and propose a novel algorithm called Active Teacher(Source code are available at: \url{https://github.com/HunterJ-Lin/ActiveTeacher}) for semi-supervised object detection (SSOD). Active Teacher extends the teacher-student framework to an iterative version, where the label set is partially initialized and gradually augmented by evaluating three key factors of unlabeled examples, including difficulty, information and diversity. With this design, Active Teacher can maximize the effect of limited label information while improving the quality of pseudo-labels. To validate our approach, we conduct extensive experiments on the MS-COCO benchmark and compare Active Teacher with a set of recently proposed SSOD methods. The experimental results not only validate the superior performance gain of Active Teacher over the compared methods, but also show that it enables the baseline network, ie, Faster-RCNN, to achieve 100% supervised performance with much less label expenditure, ie 40% labeled examples on MS-COCO. More importantly, we believe that the experimental analyses in this paper can provide useful empirical knowledge for data annotation in practical applications. | ['Rongrong Ji', 'Qiang Xu', 'Rongrong Fu', 'Liujuan Cao', 'Xiaoshuai Sun', 'Gen Luo', 'Yunhang Shen', 'Yiyi Zhou', 'Jianghang Lin', 'Peng Mi'] | 2023-03-15 | active-teacher-for-semi-supervised-object | http://openaccess.thecvf.com//content/CVPR2022/html/Mi_Active_Teacher_for_Semi-Supervised_Object_Detection_CVPR_2022_paper.html | http://openaccess.thecvf.com//content/CVPR2022/papers/Mi_Active_Teacher_for_Semi-Supervised_Object_Detection_CVPR_2022_paper.pdf | cvpr-2022-1 | ['semi-supervised-object-detection'] | ['computer-vision'] | [ 3.36036086e-02 2.73214936e-01 -6.25593483e-01 -4.69869047e-01
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83bc8f37-16d8-40a8-837c-457aa449a080 | a-group-symmetric-stochastic-differential | 2305.18407 | null | https://arxiv.org/abs/2305.18407v1 | https://arxiv.org/pdf/2305.18407v1.pdf | A Group Symmetric Stochastic Differential Equation Model for Molecule Multi-modal Pretraining | Molecule pretraining has quickly become the go-to schema to boost the performance of AI-based drug discovery. Naturally, molecules can be represented as 2D topological graphs or 3D geometric point clouds. Although most existing pertaining methods focus on merely the single modality, recent research has shown that maximizing the mutual information (MI) between such two modalities enhances the molecule representation ability. Meanwhile, existing molecule multi-modal pretraining approaches approximate MI based on the representation space encoded from the topology and geometry, thus resulting in the loss of critical structural information of molecules. To address this issue, we propose MoleculeSDE. MoleculeSDE leverages group symmetric (e.g., SE(3)-equivariant and reflection-antisymmetric) stochastic differential equation models to generate the 3D geometries from 2D topologies, and vice versa, directly in the input space. It not only obtains tighter MI bound but also enables prosperous downstream tasks than the previous work. By comparing with 17 pretraining baselines, we empirically verify that MoleculeSDE can learn an expressive representation with state-of-the-art performance on 26 out of 32 downstream tasks. | ['Jian Tang', 'Hongyu Guo', 'ZhiMing Ma', 'Weitao Du', 'Shengchao Liu'] | 2023-05-28 | null | null | null | null | ['drug-discovery'] | ['medical'] | [ 3.94546568e-01 1.29056843e-02 -5.38888574e-01 -3.36537182e-01
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49b8b1e1-27e5-4367-99b0-2ac5005443a8 | medical-imaging-with-deep-learning-for-covid | 2107.09602 | null | https://arxiv.org/abs/2107.09602v1 | https://arxiv.org/pdf/2107.09602v1.pdf | Medical Imaging with Deep Learning for COVID- 19 Diagnosis: A Comprehensive Review | The outbreak of novel coronavirus disease (COVID- 19) has claimed millions of lives and has affected all aspects of human life. This paper focuses on the application of deep learning (DL) models to medical imaging and drug discovery for managing COVID-19 disease. In this article, we detail various medical imaging-based studies such as X-rays and computed tomography (CT) images along with DL methods for classifying COVID-19 affected versus pneumonia. The applications of DL techniques to medical images are further described in terms of image localization, segmentation, registration, and classification leading to COVID-19 detection. The reviews of recent papers indicate that the highest classification accuracy of 99.80% is obtained when InstaCovNet-19 DL method is applied to an X-ray dataset of 361 COVID-19 patients, 362 pneumonia patients and 365 normal people. Furthermore, it can be seen that the best classification accuracy of 99.054% can be achieved when EDL_COVID DL method is applied to a CT image dataset of 7500 samples where COVID-19 patients, lung tumor patients and normal people are equal in number. Moreover, we illustrate the potential DL techniques in drug or vaccine discovery in combating the coronavirus. Finally, we address a number of problems, concerns and future research directions relevant to DL applications for COVID-19. | ['V. B. Surya Prasath', 'M. Rubaiyat Hossain Mondal', 'Prajoy Podder', 'Subrato Bharati'] | 2021-07-13 | null | null | null | null | ['covid-19-detection'] | ['medical'] | [ 8.14126879e-02 -4.00550604e-01 -1.52639896e-01 9.63297859e-02
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acb58f68-257d-4abc-b828-75d2fa9f35d7 | an-empirical-analysis-of-topic-models | null | null | https://aclanthology.org/2021.ranlp-main.157 | https://aclanthology.org/2021.ranlp-main.157.pdf | An Empirical Analysis of Topic Models: Uncovering the Relationships between Hyperparameters, Document Length and Performance Measures | Neural Topic Models are recent neural models that aim at extracting the main themes from a collection of documents. The comparison of these models is usually limited because the hyperparameters are held fixed. In this paper, we present an empirical analysis and comparison of Neural Topic Models by finding the optimal hyperparameters of each model for four different performance measures adopting a single-objective Bayesian optimization. This allows us to determine the robustness of a topic model for several evaluation metrics. We also empirically show the effect of the length of the documents on different optimized metrics and discover which evaluation metrics are in conflict or agreement with each other. | ['Elisabetta Fersini', 'Silvia Terragni'] | null | null | https://aclanthology.org/2021.ranlp-1.157 | https://aclanthology.org/2021.ranlp-1.157.pdf | ranlp-2021-9 | ['topic-models'] | ['natural-language-processing'] | [-1.86248302e-01 1.72744721e-01 -4.19095665e-01 -5.64530671e-01
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aaa2227f-6969-4e66-95bc-dbad65b981cd | neural-based-context-representation-learning | 1708.02561 | null | http://arxiv.org/abs/1708.02561v1 | http://arxiv.org/pdf/1708.02561v1.pdf | Neural-based Context Representation Learning for Dialog Act Classification | We explore context representation learning methods in neural-based models for
dialog act classification. We propose and compare extensively different methods
which combine recurrent neural network architectures and attention mechanisms
(AMs) at different context levels. Our experimental results on two benchmark
datasets show consistent improvements compared to the models without contextual
information and reveal that the most suitable AM in the architecture depends on
the nature of the dataset. | ['Ngoc Thang Vu', 'Daniel Ortega'] | 2017-08-08 | neural-based-context-representation-learning-1 | https://aclanthology.org/W17-5530 | https://aclanthology.org/W17-5530.pdf | ws-2017-8 | ['dialog-act-classification'] | ['natural-language-processing'] | [ 1.73297837e-01 3.80813912e-03 -5.76782711e-02 -5.80352366e-01
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00f1e21c-1f59-4c1a-84c1-e25985abef36 | deep-image-harmonization | 1703.00069 | null | http://arxiv.org/abs/1703.00069v1 | http://arxiv.org/pdf/1703.00069v1.pdf | Deep Image Harmonization | Compositing is one of the most common operations in photo editing. To
generate realistic composites, the appearances of foreground and background
need to be adjusted to make them compatible. Previous approaches to harmonize
composites have focused on learning statistical relationships between
hand-crafted appearance features of the foreground and background, which is
unreliable especially when the contents in the two layers are vastly different.
In this work, we propose an end-to-end deep convolutional neural network for
image harmonization, which can capture both the context and semantic
information of the composite images during harmonization. We also introduce an
efficient way to collect large-scale and high-quality training data that can
facilitate the training process. Experiments on the synthesized dataset and
real composite images show that the proposed network outperforms previous
state-of-the-art methods. | ['Ming-Hsuan Yang', 'Kalyan Sunkavalli', 'Yi-Hsuan Tsai', 'Xin Lu', 'Xiaohui Shen', 'Zhe Lin'] | 2017-02-28 | deep-image-harmonization-1 | http://openaccess.thecvf.com/content_cvpr_2017/html/Tsai_Deep_Image_Harmonization_CVPR_2017_paper.html | http://openaccess.thecvf.com/content_cvpr_2017/papers/Tsai_Deep_Image_Harmonization_CVPR_2017_paper.pdf | cvpr-2017-7 | ['image-harmonization'] | ['computer-vision'] | [ 3.37967932e-01 -4.05529171e-01 3.39058071e-01 -2.84409225e-01
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063d8839-f34d-4a02-855c-c1ac9cca8e08 | query-specific-knowledge-graphs-for-complex | 2211.04142 | null | https://arxiv.org/abs/2211.04142v1 | https://arxiv.org/pdf/2211.04142v1.pdf | Query-Specific Knowledge Graphs for Complex Finance Topics | Across the financial domain, researchers answer complex questions by extensively "searching" for relevant information to generate long-form reports. This workshop paper discusses automating the construction of query-specific document and entity knowledge graphs (KGs) for complex research topics. We focus on the CODEC dataset, where domain experts (1) create challenging questions, (2) construct long natural language narratives, and (3) iteratively search and assess the relevance of documents and entities. For the construction of query-specific KGs, we show that state-of-the-art ranking systems have headroom for improvement, with specific failings due to a lack of context or explicit knowledge representation. We demonstrate that entity and document relevance are positively correlated, and that entity-based query feedback improves document ranking effectiveness. Furthermore, we construct query-specific KGs using retrieval and evaluate using CODEC's "ground-truth graphs", showing the precision and recall trade-offs. Lastly, we point to future work, including adaptive KG retrieval algorithms and GNN-based weighting methods, while highlighting key challenges such as high-quality data, information extraction recall, and the size and sparsity of complex topic graphs. | ['Jeffrey Dalton', 'Iain Mackie'] | 2022-11-08 | null | null | null | null | ['document-ranking'] | ['natural-language-processing'] | [-1.55261323e-01 1.85826004e-01 -3.64419878e-01 -9.58365425e-02
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7202600e-c439-4e2f-8242-1a58dc9d37ac | a-mood-based-genre-classification-of | 1508.01571 | null | http://arxiv.org/abs/1508.01571v1 | http://arxiv.org/pdf/1508.01571v1.pdf | A Mood-based Genre Classification of Television Content | The classification of television content helps users organise and navigate
through the large list of channels and programs now available. In this paper,
we address the problem of television content classification by exploiting text
information extracted from program transcriptions. We present an analysis which
adapts a model for sentiment that has been widely and successfully applied in
other fields such as music or blog posts. We use a real-world dataset obtained
from the Boxfish API to compare the performance of classifiers trained on a
number of different feature sets. Our experiments show that, over a large
collection of television content, program genres can be represented in a
three-dimensional space of valence, arousal and dominance, and that promising
classification results can be achieved using features based on this
representation. This finding supports the use of the proposed representation of
television content as a feature space for similarity computation and
recommendation generation. | ["Michael P. O'Mahony", 'Humberto Corona'] | 2015-08-06 | null | null | null | null | ['genre-classification'] | ['computer-vision'] | [ 3.31227243e-01 -3.51480454e-01 -4.47191209e-01 -7.51923144e-01
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4edc8c16-f51a-4610-9e4b-b6c57b7adcea | sustainable-palm-tree-farming-leveraging-iot | 2306.16862 | null | https://arxiv.org/abs/2306.16862v1 | https://arxiv.org/pdf/2306.16862v1.pdf | Sustainable Palm Tree Farming: Leveraging IoT and Multi-Modal Data for Early Detection and Mapping of Red Palm Weevil | The Red Palm Weevil (RPW) is a highly destructive insect causing economic losses and impacting palm tree farming worldwide. This paper proposes an innovative approach for sustainable palm tree farming by utilizing advanced technologies for the early detection and management of RPW. Our approach combines computer vision, deep learning (DL), the Internet of Things (IoT), and geospatial data to detect and classify RPW-infested palm trees effectively. The main phases include; (1) DL classification using sound data from IoT devices, (2) palm tree detection using YOLOv8 on UAV images, and (3) RPW mapping using geospatial data. Our custom DL model achieves 100% precision and recall in detecting and localizing infested palm trees. Integrating geospatial data enables the creation of a comprehensive RPW distribution map for efficient monitoring and targeted management strategies. This technology-driven approach benefits agricultural authorities, farmers, and researchers in managing RPW infestations and safeguarding palm tree plantations' productivity. | ['Anis Koubaa', 'Imed Riadh Farah', 'Wadii Boulila', 'Ayyub Alzahem', 'Yosra Hajjaji'] | 2023-06-29 | null | null | null | null | ['management'] | ['miscellaneous'] | [ 2.40227699e-01 -6.46885335e-01 -3.61841559e-01 2.06422508e-01
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21b163ce-2897-4864-adf1-56fdd01c4a09 | shape-driven-kernel-adaptation-in | null | null | http://openaccess.thecvf.com/content_cvpr_2015/html/Li_Shape_Driven_Kernel_2015_CVPR_paper.html | http://openaccess.thecvf.com/content_cvpr_2015/papers/Li_Shape_Driven_Kernel_2015_CVPR_paper.pdf | Shape Driven Kernel Adaptation in Convolutional Neural Network for Robust Facial Traits Recognition | One key challenge of facial traits recognition is the large non-rigid appearance variations due to irrelevant real world factors, such as viewpoint and expression changes. In this paper, we explore how the shape information, i.e. facial landmark positions, can be explicitly deployed into the popular Convolutional Neural Network (CNN) architecture to disentangling such irrelevant non-rigid appearance variations. First, instead of using fixed kernels, we propose a kernel adaptation method to dynamically determine the convolutional kernels according to the distribution of facial landmarks, which helps learning more robust features. Second, motivated by the intuition that different local facial regions may demand different adaptation functions, we further propose a tree-structured convolutional architecture to hierarchically fuse multiple local adaptive CNN subnetworks. Comprehensive experiments on WebFace, Morph II and MultiPIE databases well validate the effectiveness of the proposed kernel adaptation method and tree-structured convolutional architecture for facial traits recognition tasks, including identity, age and gender classification. For all the tasks, the proposed architecture consistently achieves the state-of-the-art performances. | ['Zhiheng Niu', 'Junliang Xing', 'Shiguang Shan', 'Shaoxin Li', 'Shuicheng Yan'] | 2015-06-01 | null | null | null | cvpr-2015-6 | ['age-and-gender-classification'] | ['computer-vision'] | [-9.23721939e-02 -4.61139670e-03 -1.70945838e-01 -9.23984885e-01
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7cd015eb-cc01-4c5e-b9fa-01d0f3907da3 | mrl-learning-to-mix-with-attention-and | 2208.13975 | null | https://arxiv.org/abs/2208.13975v1 | https://arxiv.org/pdf/2208.13975v1.pdf | MRL: Learning to Mix with Attention and Convolutions | In this paper, we present a new neural architectural block for the vision domain, named Mixing Regionally and Locally (MRL), developed with the aim of effectively and efficiently mixing the provided input features. We bifurcate the input feature mixing task as mixing at a regional and local scale. To achieve an efficient mix, we exploit the domain-wide receptive field provided by self-attention for regional-scale mixing and convolutional kernels restricted to local scale for local-scale mixing. More specifically, our proposed method mixes regional features associated with local features within a defined region, followed by a local-scale features mix augmented by regional features. Experiments show that this hybridization of self-attention and convolution brings improved capacity, generalization (right inductive bias), and efficiency. Under similar network settings, MRL outperforms or is at par with its counterparts in classification, object detection, and segmentation tasks. We also show that our MRL-based network architecture achieves state-of-the-art performance for H&E histology datasets. We achieved DICE of 0.843, 0.855, and 0.892 for Kumar, CoNSep, and CPM-17 datasets, respectively, while highlighting the versatility offered by the MRL framework by incorporating layers like group convolutions to improve dataset-specific generalization. | ['Yoshiki Tanaka', 'Hisahiro Suganuma', 'Shlok Mohta'] | 2022-08-30 | null | null | null | null | ['histopathological-segmentation', 'multi-tissue-nucleus-segmentation'] | ['computer-vision', 'medical'] | [ 1.28433868e-01 -6.42270073e-02 -6.86420500e-02 -4.76888269e-01
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d0a1b9c6-dd09-4731-b1ee-883973b14251 | 3d-part-assembly-generation-with-instance | 2207.01779 | null | https://arxiv.org/abs/2207.01779v1 | https://arxiv.org/pdf/2207.01779v1.pdf | 3D Part Assembly Generation with Instance Encoded Transformer | It is desirable to enable robots capable of automatic assembly. Structural understanding of object parts plays a crucial role in this task yet remains relatively unexplored. In this paper, we focus on the setting of furniture assembly from a complete set of part geometries, which is essentially a 6-DoF part pose estimation problem. We propose a multi-layer transformer-based framework that involves geometric and relational reasoning between parts to update the part poses iteratively. We carefully design a unique instance encoding to solve the ambiguity between geometrically-similar parts so that all parts can be distinguished. In addition to assembling from scratch, we extend our framework to a new task called in-process part assembly. Analogous to furniture maintenance, it requires robots to continue with unfinished products and assemble the remaining parts into appropriate positions. Our method achieves far more than 10% improvements over the current state-of-the-art in multiple metrics on the public PartNet dataset. Extensive experiments and quantitative comparisons demonstrate the effectiveness of the proposed framework. | ['Mingyu You', 'Xuan Han', 'WeiHao Wang', 'Tao Kong', 'Rufeng Zhang'] | 2022-07-05 | null | null | null | null | ['relational-reasoning'] | ['natural-language-processing'] | [ 1.43459454e-01 1.14666387e-01 6.82996362e-02 -3.28597426e-01
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6f1b7011-abba-4036-a295-56989c6e0565 | select-supplement-and-focus-for-rgb-d | null | null | http://openaccess.thecvf.com/content_CVPR_2020/html/Zhang_Select_Supplement_and_Focus_for_RGB-D_Saliency_Detection_CVPR_2020_paper.html | http://openaccess.thecvf.com/content_CVPR_2020/papers/Zhang_Select_Supplement_and_Focus_for_RGB-D_Saliency_Detection_CVPR_2020_paper.pdf | Select, Supplement and Focus for RGB-D Saliency Detection | Depth data containing a preponderance of discriminative power in location have been proven beneficial for accurate saliency prediction. However, RGB-D saliency detection methods are also negatively influenced by randomly distributed erroneous or missing regions on the depth map or along the object boundaries. This offers the possibility of achieving more effective inference by well designed models. In this paper, we propose a new framework for accurate RGB-D saliency detection taking account of local and global complementarities from two modalities. This is achieved by designing a complimentary interaction model discriminative enough to simultaneously select useful representation from RGB and depth data, and meanwhile to refine the object boundaries. Moreover, we proposed a compensation-aware loss to further process the information not being considered in the complimentary interaction model, leading to improvement of the generalization ability for challenging scenes. Experiments on six public datasets show that our method outperforms18state-of-the-art methods.
| [' Huchuan Lu', ' Zhengkun Rong', ' Yongri Piao', ' Weisong Ren', 'Miao Zhang'] | 2020-06-01 | null | null | null | cvpr-2020-6 | ['rgb-d-salient-object-detection', 'thermal-image-segmentation'] | ['computer-vision', 'computer-vision'] | [ 3.32997203e-01 1.96267128e-01 -3.09787214e-01 -4.98731226e-01
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b9f32dca-7e2a-4795-a8d3-9ee52c7fc0d8 | point-transformer-1 | 2012.09164 | null | https://arxiv.org/abs/2012.09164v2 | https://arxiv.org/pdf/2012.09164v2.pdf | Point Transformer | Self-attention networks have revolutionized natural language processing and are making impressive strides in image analysis tasks such as image classification and object detection. Inspired by this success, we investigate the application of self-attention networks to 3D point cloud processing. We design self-attention layers for point clouds and use these to construct self-attention networks for tasks such as semantic scene segmentation, object part segmentation, and object classification. Our Point Transformer design improves upon prior work across domains and tasks. For example, on the challenging S3DIS dataset for large-scale semantic scene segmentation, the Point Transformer attains an mIoU of 70.4% on Area 5, outperforming the strongest prior model by 3.3 absolute percentage points and crossing the 70% mIoU threshold for the first time. | ['Vladlen Koltun', 'Philip Torr', 'Jiaya Jia', 'Li Jiang', 'Hengshuang Zhao'] | 2020-12-16 | null | http://openaccess.thecvf.com//content/ICCV2021/html/Zhao_Point_Transformer_ICCV_2021_paper.html | http://openaccess.thecvf.com//content/ICCV2021/papers/Zhao_Point_Transformer_ICCV_2021_paper.pdf | iccv-2021-1 | ['3d-part-segmentation'] | ['computer-vision'] | [ 3.19053173e-01 3.79290015e-01 -1.11376181e-01 -4.56127763e-01
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1be35c85-0de1-45ea-b0c5-8f054cdee725 | towards-accurate-localization-by-instance | 2107.05005 | null | https://arxiv.org/abs/2107.05005v2 | https://arxiv.org/pdf/2107.05005v2.pdf | Towards Accurate Localization by Instance Search | Visual object localization is the key step in a series of object detection tasks. In the literature, high localization accuracy is achieved with the mainstream strongly supervised frameworks. However, such methods require object-level annotations and are unable to detect objects of unknown categories. Weakly supervised methods face similar difficulties. In this paper, a self-paced learning framework is proposed to achieve accurate object localization on the rank list returned by instance search. The proposed framework mines the target instance gradually from the queries and their corresponding top-ranked search results. Since a common instance is shared between the query and the images in the rank list, the target visual instance can be accurately localized even without knowing what the object category is. In addition to performing localization on instance search, the issue of few-shot object detection is also addressed under the same framework. Superior performance over state-of-the-art methods is observed on both tasks. | ['Wan-Lei Zhao', 'Hui-Chu Xiao', 'Yi-Geng Hong'] | 2021-07-11 | null | null | null | null | ['instance-search'] | ['computer-vision'] | [ 1.72729045e-01 -2.06243500e-01 -5.78458846e-01 -1.05672210e-01
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f0ad03e0-ba07-44ad-a6ad-0822d2ac6a1b | enriching-epidemiological-thematic-features | null | null | https://aclanthology.org/2022.lrec-1.399 | https://aclanthology.org/2022.lrec-1.399.pdf | Enriching Epidemiological Thematic Features For Disease Surveillance Corpora Classification | We present EpidBioBERT, a biosurveillance epidemiological document tagger for disease surveillance over PADI-Web system. Our model is trained on PADI-Web corpus which contains news articles on Animal Diseases Outbreak extracted from the web. We train a classifier to discriminate between relevant and irrelevant documents based on their epidemiological thematic feature content in preparation for further epidemiology information extraction. Our approach proposes a new way to perform epidemiological document classification by enriching epidemiological thematic features namely disease, host, location and date, which are used as inputs to our epidemiological document classifier. We adopt a pre-trained biomedical language model with a novel fine tuning approach that enriches these epidemiological thematic features. We find these thematic features rich enough to improve epidemiological document classification over a smaller data set than initially used in PADI-Web classifier. This improves the classifiers ability to avoid false positive alerts on disease surveillance systems. To further understand information encoded in EpidBioBERT, we experiment the impact of each epidemiology thematic feature on the classifier under ablation studies. We compare our biomedical pre-trained approach with a general language model based model finding that thematic feature embeddings pre-trained on general English documents are not rich enough for epidemiology classification task. Our model achieves an F1-score of 95.5% over an unseen test set, with an improvement of +5.5 points on F1-Score on the PADI-Web classifier with nearly half the training data set. | ['Dickson Owuor', 'Roberto Interdonato', 'Mathieu Roche', 'Edmond Menya'] | null | null | null | null | lrec-2022-6 | ['epidemiology', 'document-classification'] | ['medical', 'natural-language-processing'] | [ 2.76550919e-01 2.70278513e-01 -1.75150812e-01 -1.78381264e-01
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54252fb4-a8bc-4f7f-9360-a5f63665bfe5 | compact-low-profile-wearable-antennas-for | 1809.07475 | null | http://arxiv.org/abs/1809.07475v1 | http://arxiv.org/pdf/1809.07475v1.pdf | Compact Low-Profile Wearable Antennas For Breast Cancer Detection | Many lives can be saved if tumors are detected in early stages, which can
result in a bigger chance for recovery. Many patients find it irritating to get
regular checkups due to the fact that the majority of the monitoring systems
are complicated, not available everywhere and not mobile. Furthermore, for
medical field applications, micro-strip antennas are efficient and have
flexible properties that are utilized in imaging, diagnosis and treatment. It
is known that breast cancer is the most common type of cancer in the world, and
the earlier its been detected the better. In the early stages of breast cancer,
getting rid of from the tumors is much easier and more guaranteed. Nowadays,
the main method that is used in the hospitals for breast cancer detection is
the Ultra-Wideband method (UWB). However, Many patients find it irritating to
get regular check ups due to the fact that the majority of the monitoring
systems are complicated and not mobile. | [] | 2018-09-20 | null | null | null | null | ['breast-cancer-detection', 'breast-cancer-detection'] | ['knowledge-base', 'medical'] | [-1.63155809e-01 2.04595640e-01 -5.09779334e-01 1.02676474e-01
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d8998105-707b-4dc0-91bf-241b6db570ca | palm-pre-training-an-autoencoding-1 | null | null | https://aclanthology.org/2020.emnlp-main.700 | https://aclanthology.org/2020.emnlp-main.700.pdf | PALM: Pre-training an Autoencoding\&Autoregressive Language Model for Context-conditioned Generation | Self-supervised pre-training, such as BERT, MASS and BART, has emerged as a powerful technique for natural language understanding and generation. Existing pre-training techniques employ autoencoding and/or autoregressive objectives to train Transformer-based models by recovering original word tokens from corrupted text with some masked tokens. The training goals of existing techniques are often inconsistent with the goals of many language generation tasks, such as generative question answering and conversational response generation, for producing new text given context. This work presents PALM with a novel scheme that jointly pre-trains an autoencoding and autoregressive language model on a large unlabeled corpus, specifically designed for generating new text conditioned on context. The new scheme alleviates the mismatch introduced by the existing denoising scheme between pre-training and fine-tuning where generation is more than reconstructing original text. An extensive set of experiments show that PALM achieves new state-of-the-art results on a variety of language generation benchmarks covering generative question answering (Rank 1 on the official MARCO leaderboard), abstractive summarization on CNN/DailyMail as well as Gigaword, question generation on SQuAD, and conversational response generation on Cornell Movie Dialogues. | ['Luo Si', 'Fei Huang', 'Songfang Huang', 'Wei Wang', 'Ming Yan', 'Chen Wu', 'Chenliang Li', 'Bin Bi'] | null | null | null | null | emnlp-2020-11 | ['generative-question-answering', 'conversational-response-generation'] | ['natural-language-processing', 'natural-language-processing'] | [ 5.56415737e-01 6.47843003e-01 2.52120495e-01 -5.15659332e-01
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1820da84-d786-47f4-ae01-dbaebb69e05f | query-focused-extractive-summarisation-for-1 | 2209.01815 | null | https://arxiv.org/abs/2209.01815v1 | https://arxiv.org/pdf/2209.01815v1.pdf | Query-focused Extractive Summarisation for Biomedical and COVID-19 Complex Question Answering | This paper presents Macquarie University's participation to the two most recent BioASQ Synergy Tasks (as per June 2022), and to the BioASQ10 Task~B (BioASQ10b), Phase~B. In these tasks, participating systems are expected to generate complex answers to biomedical questions, where the answers may contain more than one sentence. We apply query-focused extractive summarisation techniques. In particular, we follow a sentence classification-based approach that scores each candidate sentence associated to a question, and the $n$ highest-scoring sentences are returned as the answer. The Synergy Task corresponds to an end-to-end system that requires document selection, snippet selection, and finding the final answer, but it has very limited training data. For the Synergy task, we selected the candidate sentences following two phases: document retrieval and snippet retrieval, and the final answer was found by using a DistilBERT/ALBERT classifier that had been trained on the training data of BioASQ9b. Document retrieval was achieved as a standard search over the CORD-19 data using the search API provided by the BioASQ organisers, and snippet retrieval was achieved by re-ranking the sentences of the top retrieved documents, using the cosine similarity of the question and candidate sentence. We observed that vectors represented via sBERT have an edge over tf.idf. BioASQ10b Phase B focuses on finding the specific answers to biomedical questions. For this task, we followed a data-centric approach. We hypothesised that the training data of the first BioASQ years might be biased and we experimented with different subsets of the training data. We observed an improvement of results when the system was trained on the second half of the BioASQ10b training data. | ['Diego Mollá'] | 2022-09-05 | null | null | null | null | ['sentence-classification'] | ['natural-language-processing'] | [ 3.32284778e-01 1.53636888e-01 6.68304786e-02 -2.61436164e-01
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6fd8b654-ac17-4ca6-8298-6b5dc8a36860 | ecrecer-enzyme-commission-number | 2202.03632 | null | https://arxiv.org/abs/2202.03632v1 | https://arxiv.org/pdf/2202.03632v1.pdf | ECRECer: Enzyme Commission Number Recommendation and Benchmarking based on Multiagent Dual-core Learning | Enzyme Commission (EC) numbers, which associate a protein sequence with the biochemical reactions it catalyzes, are essential for the accurate understanding of enzyme functions and cellular metabolism. Many ab-initio computational approaches were proposed to predict EC numbers for given input sequences directly. However, the prediction performance (accuracy, recall, precision), usability, and efficiency of existing methods still have much room to be improved. Here, we report ECRECer, a cloud platform for accurately predicting EC numbers based on novel deep learning techniques. To build ECRECer, we evaluate different protein representation methods and adopt a protein language model for protein sequence embedding. After embedding, we propose a multi-agent hierarchy deep learning-based framework to learn the proposed tasks in a multi-task manner. Specifically, we used an extreme multi-label classifier to perform the EC prediction and employed a greedy strategy to integrate and fine-tune the final model. Comparative analyses against four representative methods demonstrate that ECRECer delivers the highest performance, which improves accuracy and F1 score by 70% and 20% over the state-of-the-the-art, respectively. With ECRECer, we can annotate numerous enzymes in the Swiss-Prot database with incomplete EC numbers to their full fourth level. Take UniPort protein "A0A0U5GJ41" as an example (1.14.-.-), ECRECer annotated it with "1.14.11.38", which supported by further protein structure analysis based on AlphaFold2. Finally, we established a webserver (https://ecrecer.biodesign.ac.cn) and provided an offline bundle to improve usability. | ['Hongwu Ma', 'Xiaoping Liao', 'Hoaran Li', 'Ruoyu Wang', 'Qianqian Yuan', 'Zhenkun Shi'] | 2022-02-08 | null | null | null | null | ['protein-language-model'] | ['medical'] | [ 5.25317006e-02 -2.36175671e-01 -1.16494469e-01 -1.34102941e-01
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ce8d7846-90f1-4ed8-a049-6a9907152371 | an-empirical-evaluation-of-federated | 2303.10218 | null | https://arxiv.org/abs/2303.10218v1 | https://arxiv.org/pdf/2303.10218v1.pdf | An Empirical Evaluation of Federated Contextual Bandit Algorithms | As the adoption of federated learning increases for learning from sensitive data local to user devices, it is natural to ask if the learning can be done using implicit signals generated as users interact with the applications of interest, rather than requiring access to explicit labels which can be difficult to acquire in many tasks. We approach such problems with the framework of federated contextual bandits, and develop variants of prominent contextual bandit algorithms from the centralized seting for the federated setting. We carefully evaluate these algorithms in a range of scenarios simulated using publicly available datasets. Our simulations model typical setups encountered in the real-world, such as various misalignments between an initial pre-trained model and the subsequent user interactions due to non-stationarity in the data and/or heterogeneity across clients. Our experiments reveal the surprising effectiveness of the simple and commonly used softmax heuristic in balancing the well-know exploration-exploitation tradeoff across the breadth of our settings. | ['Zheng Xu', 'H. Brendan McMahan', 'Alekh Agarwal'] | 2023-03-17 | null | null | null | null | ['multi-armed-bandits'] | ['miscellaneous'] | [ 3.66562724e-01 7.51569197e-02 -8.41875374e-01 -5.75558603e-01
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166c62ba-d61b-415a-a5e1-09d904716d02 | fair-representation-learning-using | 2108.00295 | null | https://arxiv.org/abs/2108.00295v2 | https://arxiv.org/pdf/2108.00295v2.pdf | Fair Representation Learning using Interpolation Enabled Disentanglement | With the growing interest in the machine learning community to solve real-world problems, it has become crucial to uncover the hidden reasoning behind their decisions by focusing on the fairness and auditing the predictions made by these black-box models. In this paper, we propose a novel method to address two key issues: (a) Can we simultaneously learn fair disentangled representations while ensuring the utility of the learned representation for downstream tasks, and (b)Can we provide theoretical insights into when the proposed approach will be both fair and accurate. To address the former, we propose the method FRIED, Fair Representation learning using Interpolation Enabled Disentanglement. In our architecture, by imposing a critic-based adversarial framework, we enforce the interpolated points in the latent space to be more realistic. This helps in capturing the data manifold effectively and enhances the utility of the learned representation for downstream prediction tasks. We address the latter question by developing a theory on fairness-accuracy trade-offs using classifier-based conditional mutual information estimation. We demonstrate the effectiveness of FRIED on datasets of different modalities - tabular, text, and image datasets. We observe that the representations learned by FRIED are overall fairer in comparison to existing baselines and also accurate for downstream prediction tasks. Additionally, we evaluate FRIED on a real-world healthcare claims dataset where we conduct an expert aided model auditing study providing useful insights into opioid ad-diction patterns. | ['Chandan K. Reddy', 'Bhanukiran Vinzamuri', 'Akshita Jha'] | 2021-07-31 | null | null | null | null | ['mutual-information-estimation'] | ['methodology'] | [ 2.53932267e-01 5.62822342e-01 -6.58567309e-01 -4.09289867e-01
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91bfb578-59cd-410a-a1f5-3d198e9bc7bb | exclusivity-consistency-regularized-multi | null | null | http://openaccess.thecvf.com/content_cvpr_2017/html/Wang_Exclusivity-Consistency_Regularized_Multi-View_CVPR_2017_paper.html | http://openaccess.thecvf.com/content_cvpr_2017/papers/Wang_Exclusivity-Consistency_Regularized_Multi-View_CVPR_2017_paper.pdf | Exclusivity-Consistency Regularized Multi-View Subspace Clustering | Multi-view subspace clustering aims to partition a set of multi-source data into their underlying groups. To boost the performance of multi-view clustering, numerous subspace learning algorithms have been developed in recent years, but with rare exploitation of the representation complementarity between different views as well as the indicator consistency among the representations, let alone considering them simultaneously. In this paper, we propose a novel multi-view subspace clustering model that attempts to harness the complementary information between different representations by introducing a novel position-aware exclusivity term. Meanwhile, a consistency term is employed to make these complementary representations to further have a common indicator. We formulate the above concerns into a unified optimization framework. Experimental results on several benchmark datasets are conducted to reveal the effectiveness of our algorithm over other state-of-the-arts.
| ['Xiaojie Guo', 'Zhen Lei', 'Stan Z. Li', 'Xiaobo Wang', 'Changqing Zhang'] | 2017-07-01 | null | null | null | cvpr-2017-7 | ['multi-view-subspace-clustering'] | ['computer-vision'] | [-1.38543442e-01 -3.71685445e-01 -3.83863151e-01 -1.23232163e-01
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a46bce26-024b-4318-b666-10f4ccdc6ef2 | k-diag-knowledge-enhanced-disease-diagnosis | 2302.11557 | null | https://arxiv.org/abs/2302.11557v2 | https://arxiv.org/pdf/2302.11557v2.pdf | K-Diag: Knowledge-enhanced Disease Diagnosis in Radiographic Imaging | In this paper, we consider the problem of disease diagnosis. Unlike the conventional learning paradigm that treats labels independently, we propose a knowledge-enhanced framework, that enables training visual representation with the guidance of medical domain knowledge. In particular, we make the following contributions: First, to explicitly incorporate experts' knowledge, we propose to learn a neural representation for the medical knowledge graph via contrastive learning, implicitly establishing relations between different medical concepts. Second, while training the visual encoder, we keep the parameters of the knowledge encoder frozen and propose to learn a set of prompt vectors for efficient adaptation. Third, we adopt a Transformer-based disease-query module for cross-model fusion, which naturally enables explainable diagnosis results via cross attention. To validate the effectiveness of our proposed framework, we conduct thorough experiments on three x-ray imaging datasets across different anatomy structures, showing our model is able to exploit the implicit relations between diseases/findings, thus is beneficial to the commonly encountered problem in the medical domain, namely, long-tailed and zero-shot recognition, which conventional methods either struggle or completely fail to realize. | ['Weidi Xie', 'Ya zhang', 'Yanfeng Wang', 'Xiaoman Zhang', 'Chaoyi Wu'] | 2023-02-22 | null | null | null | null | ['anatomy', 'implicit-relations'] | ['miscellaneous', 'natural-language-processing'] | [ 3.78487289e-01 5.44743240e-01 -3.35789979e-01 -3.17010611e-01
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308de818-2def-4ed4-ba48-ed9c94ee713a | graph-contrastive-topic-model | 2307.02078 | null | https://arxiv.org/abs/2307.02078v1 | https://arxiv.org/pdf/2307.02078v1.pdf | Graph Contrastive Topic Model | Existing NTMs with contrastive learning suffer from the sample bias problem owing to the word frequency-based sampling strategy, which may result in false negative samples with similar semantics to the prototypes. In this paper, we aim to explore the efficient sampling strategy and contrastive learning in NTMs to address the aforementioned issue. We propose a new sampling assumption that negative samples should contain words that are semantically irrelevant to the prototype. Based on it, we propose the graph contrastive topic model (GCTM), which conducts graph contrastive learning (GCL) using informative positive and negative samples that are generated by the graph-based sampling strategy leveraging in-depth correlation and irrelevance among documents and words. In GCTM, we first model the input document as the document word bipartite graph (DWBG), and construct positive and negative word co-occurrence graphs (WCGs), encoded by graph neural networks, to express in-depth semantic correlation and irrelevance among words. Based on the DWBG and WCGs, we design the document-word information propagation (DWIP) process to perform the edge perturbation of DWBG, based on multi-hop correlations/irrelevance among documents and words. This yields the desired negative and positive samples, which will be utilized for GCL together with the prototypes to improve learning document topic representations and latent topics. We further show that GCL can be interpreted as the structured variational graph auto-encoder which maximizes the mutual information of latent topic representations of different perspectives on DWBG. Experiments on several benchmark datasets demonstrate the effectiveness of our method for topic coherence and document representation learning compared with existing SOTA methods. | ['Sophia Ananiadou', 'Qianqian Xie', 'Lei Liu', 'Zheheng Luo'] | 2023-07-05 | null | null | null | null | ['contrastive-learning', 'contrastive-learning', 'representation-learning'] | ['computer-vision', 'methodology', 'methodology'] | [ 7.98905492e-02 4.40514266e-01 -6.20050609e-01 -1.26010746e-01
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e2a9fc06-3c60-4185-9b28-e931be55ec36 | deep-polarization-cues-for-transparent-object | null | null | http://openaccess.thecvf.com/content_CVPR_2020/html/Kalra_Deep_Polarization_Cues_for_Transparent_Object_Segmentation_CVPR_2020_paper.html | http://openaccess.thecvf.com/content_CVPR_2020/papers/Kalra_Deep_Polarization_Cues_for_Transparent_Object_Segmentation_CVPR_2020_paper.pdf | Deep Polarization Cues for Transparent Object Segmentation | Segmentation of transparent objects is a hard, open problem in computer vision. Transparent objects lack texture of their own, adopting instead the texture of scene background. This paper reframes the problem of transparent object segmentation into the realm of light polarization, i.e., the rotation of light waves. We use a polarization camera to capture multi-modal imagery and couple this with a unique deep learning backbone for processing polarization input data. Our method achieves instance segmentation on cluttered, transparent objects in various scene and background conditions, demonstrating an improvement over traditional image-based approaches. As an application we use this for robotic bin picking of transparent objects.
| [' Achuta Kadambi', ' Ramesh Raskar', ' Kartik Venkataraman', ' Supreeth Krishna Rao', ' Vage Taamazyan', 'Agastya Kalra'] | 2020-06-01 | null | null | null | cvpr-2020-6 | ['transparent-objects'] | ['computer-vision'] | [ 6.81461871e-01 4.78965491e-02 2.05710277e-01 -2.61648923e-01
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e3c32f9d-272a-4ca5-8e0b-634f3b4d9567 | signal-quality-assessment-of | 2202.00606 | null | https://arxiv.org/abs/2202.00606v1 | https://arxiv.org/pdf/2202.00606v1.pdf | Signal Quality Assessment of Photoplethysmogram Signals using Quantum Pattern Recognition and lightweight CNN Architecture | Photoplethysmography (PPG) signal comprises physiological information related to cardiorespiratory health. However, while recording, these PPG signals are easily corrupted by motion artifacts and body movements, leading to noise enriched, poor quality signals. Therefore ensuring high-quality signals is necessary to extract cardiorespiratory information accurately. Although there exists several rule-based and Machine-Learning (ML) - based approaches for PPG signal quality estimation, those algorithms' efficacy is questionable. Thus, this work proposes a lightweight CNN architecture for signal quality assessment employing a novel Quantum pattern recognition (QPR) technique. The proposed algorithm is validated on manually annotated data obtained from the University of Queensland database. A total of 28366, 5s signal segments are preprocessed and transformed into image files of 20 x 500 pixels. The image files are treated as an input to the 2D CNN architecture. The developed model classifies the PPG signal as `good' or `bad' with an accuracy of 98.3% with 99.3% sensitivity, 94.5% specificity and 98.9% F1-score. Finally, the performance of the proposed framework is validated against the noisy `Welltory app' collected PPG database. Even in a noisy environment, the proposed architecture proved its competence. Experimental analysis concludes that a slim architecture along with a novel Spatio-temporal pattern recognition technique improve the system's performance. Hence, the proposed approach can be useful to classify good and bad PPG signals for a resource-constrained wearable implementation. | ['Sayan Sarkar', 'Aayushman Ghosh', 'Tamaghno Chatterjee'] | 2022-02-01 | null | null | null | null | ['photoplethysmography-ppg'] | ['medical'] | [ 3.88306260e-01 -2.33242065e-01 2.73139756e-02 -2.56024092e-01
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b181c733-1b5e-4003-b030-28b55d876e2a | openqa-hybrid-qa-system-relying-on-structured | 2112.15356 | null | https://arxiv.org/abs/2112.15356v1 | https://arxiv.org/pdf/2112.15356v1.pdf | OpenQA: Hybrid QA System Relying on Structured Knowledge Base as well as Non-structured Data | Search 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. | ['Ziwei Wang', 'Lingyu Liu', 'Yang Liu', 'Yuxin Qin', 'Bin Xu', 'Gaochen Wu'] | 2021-12-31 | null | null | null | null | ['answer-selection'] | ['natural-language-processing'] | [-3.26770395e-01 1.28573358e-01 1.40912920e-01 -4.55989093e-01
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b157e902-f2ae-4323-a0fb-598e2ceeac72 | an-accurate-texture-complexity-estimation-for | null | null | https://ieeexplore.ieee.org/document/8963890 | https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=8963890 | An Accurate Texture Complexity Estimation for Quality-Enhanced and Secure Image Steganography | Content-adaptive steganography intends to hide data in the complex texture content of the image. Recently, some secure steganography methods have been proposed to identify the textural complexity of an image. However, most of the techniques do not take into account the information of pixel variation around the central pixel in all possible directions and therefore they are unable to accurately analyse the texture complexity. This work offers a quality-enhanced and secure method of content-adaptive image steganography. The proposed method is divided into three sequential steps: image segmentation, pixel complexity identi cation, and data embedding. An input cover image is initially divided into small local regions and the pixel-complexity is identi ed based on the proposed Complex Block Prior (CBP) criterion. In a local block, a high pass lter (HPF) bank is applied and eight residual responses are obtained. Following the CBP criterion, a complexity level out of nine levels is assigned to an individualized pixel block. The pixels are then arranged in the priority of complexity from highest to lowest. Data embedding for the corresponding complexity level then takes place using the proposed adaptive embedding algorithm. Experimental results verify the preservation of visual quality of stego images produced by the proposed method. Three image datasets: Standard test images, BOWS2 and BOSS-base are used for the experimentation and comparison with prior state-of-art methods. Highest values of the IQ (image quality) parameters e.g., SSIM and WPSNR show the effectiveness of the proposed method. | ['YASAR AMIN', 'MANSOOR SHAUKAT KHAN', 'MUHAMMAD ALI RIAZ', 'SYEDA IFFAT NAQVI', 'HUMAYUN SHAHID', 'MUHAMMAD JAMIL KHAN', 'FAWAD', 'AYESHA SAEED'] | 2020-01-20 | null | null | null | journal-ieee-2020-1 | ['image-steganography'] | ['computer-vision'] | [ 9.33772981e-01 -6.98374733e-02 1.77426543e-02 1.35467932e-01
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8143943b-72f0-4da3-aa68-c242ad6925f8 | topodiff-a-performance-and-constraint-guided | 2208.09591 | null | https://arxiv.org/abs/2208.09591v2 | https://arxiv.org/pdf/2208.09591v2.pdf | Diffusion Models Beat GANs on Topology Optimization | Structural topology optimization, which aims to find the optimal physical structure that maximizes mechanical performance, is vital in engineering design applications in aerospace, mechanical, and civil engineering. Generative adversarial networks (GANs) have recently emerged as a popular alternative to traditional iterative topology optimization methods. However, these models are often difficult to train, have limited generalizability, and due to their goal of mimicking optimal structures, neglect manufacturability and performance objectives like mechanical compliance. We propose TopoDiff - a conditional diffusion-model-based architecture to perform performance-aware and manufacturability-aware topology optimization that overcomes these issues. Our model introduces a surrogate model-based guidance strategy that actively favors structures with low compliance and good manufacturability. Our method significantly outperforms a state-of-art conditional GAN by reducing the average error on physical performance by a factor of eight and by producing eleven times fewer infeasible samples. By introducing diffusion models to topology optimization, we show that conditional diffusion models have the ability to outperform GANs in engineering design synthesis applications too. Our work also suggests a general framework for engineering optimization problems using diffusion models and external performance with constraint-aware guidance. We publicly share the data, code, and trained models here: https://decode.mit.edu/projects/topodiff/. | ['Faez Ahmed', 'François Mazé'] | 2022-08-20 | null | null | null | null | ['design-synthesis'] | ['adversarial'] | [-6.10430352e-02 2.70109892e-01 -1.79986537e-01 7.43400082e-02
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1711ab78-f2fa-4081-b5d4-e30caf26710e | incorporating-external-pos-tagger-for | 2106.06731 | null | https://arxiv.org/abs/2106.06731v1 | https://arxiv.org/pdf/2106.06731v1.pdf | Incorporating External POS Tagger for Punctuation Restoration | Punctuation restoration is an important post-processing step in automatic speech recognition. Among other kinds of external information, part-of-speech (POS) taggers provide informative tags, suggesting each input token's syntactic role, which has been shown to be beneficial for the punctuation restoration task. In this work, we incorporate an external POS tagger and fuse its predicted labels into the existing language model to provide syntactic information. Besides, we propose sequence boundary sampling (SBS) to learn punctuation positions more efficiently as a sequence tagging task. Experimental results show that our methods can consistently obtain performance gains and achieve a new state-of-the-art on the common IWSLT benchmark. Further ablation studies illustrate that both large pre-trained language models and the external POS tagger take essential parts to improve the model's performance. | ['Zhouhan Lin', 'Xiangyu Liu', 'Jinfeng Li', 'Boxin Wang', 'Wei Wang', 'Ning Shi'] | 2021-06-12 | null | null | null | null | ['punctuation-restoration'] | ['natural-language-processing'] | [ 3.79727453e-01 1.27926454e-01 -5.18705368e-01 -4.55947280e-01
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bb21f4b2-ae64-4115-9601-3bbea6116f13 | scalable-method-for-bayesian-experimental | 2306.17615 | null | https://arxiv.org/abs/2306.17615v1 | https://arxiv.org/pdf/2306.17615v1.pdf | Scalable method for Bayesian experimental design without integrating over posterior distribution | We address the computational efficiency in solving the A-optimal Bayesian design of experiments problems for which the observational model is based on partial differential equations and, consequently, is computationally expensive to evaluate. A-optimality is a widely used and easy-to-interpret criterion for the Bayesian design of experiments. The criterion seeks the optimal experiment design by minimizing the expected conditional variance, also known as the expected posterior variance. This work presents a novel likelihood-free method for seeking the A-optimal design of experiments without sampling or integrating the Bayesian posterior distribution. In our approach, the expected conditional variance is obtained via the variance of the conditional expectation using the law of total variance, while we take advantage of the orthogonal projection property to approximate the conditional expectation. Through an asymptotic error estimation, we show that the intractability of the posterior does not affect the performance of our approach. We use an artificial neural network (ANN) to approximate the nonlinear conditional expectation to implement our method. For dealing with continuous experimental design parameters, we integrate the training process of the ANN into minimizing the expected conditional variance. Specifically, we propose a non-local approximation of the conditional expectation and apply transfer learning to reduce the number of evaluations of the observation model. Through numerical experiments, we demonstrate that our method significantly reduces the number of observational model evaluations compared with common importance sampling-based approaches. This reduction is crucial, considering the computationally expensive nature of these models. | ['Raúl Tempone', 'Sebastian Krumscheid', 'Luis Espath', 'Vinh Hoang'] | 2023-06-30 | null | null | null | null | ['experimental-design', 'transfer-learning'] | ['methodology', 'miscellaneous'] | [ 1.76957786e-01 -1.12417117e-01 1.17882095e-01 -9.24743712e-02
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069245f6-167a-4d04-9c9e-0a10f3f7fcde | adversarial-attacks-on-deep-algorithmic | 2010.11388 | null | https://arxiv.org/abs/2010.11388v1 | https://arxiv.org/pdf/2010.11388v1.pdf | Adversarial Attacks on Deep Algorithmic Trading Policies | Deep Reinforcement Learning (DRL) has become an appealing solution to algorithmic trading such as high frequency trading of stocks and cyptocurrencies. However, DRL have been shown to be susceptible to adversarial attacks. It follows that algorithmic trading DRL agents may also be compromised by such adversarial techniques, leading to policy manipulation. In this paper, we develop a threat model for deep trading policies, and propose two attack techniques for manipulating the performance of such policies at test-time. Furthermore, we demonstrate the effectiveness of the proposed attacks against benchmark and real-world DQN trading agents. | ['Ali Fathi', 'Vahid Behzadan', 'Nancirose Piazza', 'Yaser Faghan'] | 2020-10-22 | null | null | null | null | ['algorithmic-trading'] | ['time-series'] | [-5.52305102e-01 -1.02131203e-01 -9.59945023e-02 -4.68876623e-02
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be649c1b-66cd-403c-9497-c5084e8d5099 | estimation-of-standard-auction-models | 2205.02060 | null | https://arxiv.org/abs/2205.02060v1 | https://arxiv.org/pdf/2205.02060v1.pdf | Estimation of Standard Auction Models | We provide efficient estimation methods for first- and second-price auctions under independent (asymmetric) private values and partial observability. Given a finite set of observations, each comprising the identity of the winner and the price they paid in a sequence of identical auctions, we provide algorithms for non-parametrically estimating the bid distribution of each bidder, as well as their value distributions under equilibrium assumptions. We provide finite-sample estimation bounds which are uniform in that their error rates do not depend on the bid/value distributions being estimated. Our estimation guarantees advance a body of work in Econometrics wherein only identification results have been obtained, unless the setting is symmetric, parametric, or all bids are observable. Our guarantees also provide computationally and statistically effective alternatives to classical techniques from reliability theory. Finally, our results are immediately applicable to Dutch and English auctions. | ['Manolis Zampetakis', 'Andrew Ilyas', 'Constantinos Daskalakis', 'Yeshwanth Cherapanamjeri'] | 2022-05-04 | null | null | null | null | ['econometrics'] | ['miscellaneous'] | [-3.77141237e-01 -9.09520760e-02 -7.24568903e-01 -2.51584709e-01
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4711bb06-6125-4216-9105-847b2f005526 | vulberta-simplified-source-code-pre-training | 2205.12424 | null | https://arxiv.org/abs/2205.12424v1 | https://arxiv.org/pdf/2205.12424v1.pdf | VulBERTa: Simplified Source Code Pre-Training for Vulnerability Detection | This paper presents VulBERTa, a deep learning approach to detect security vulnerabilities in source code. Our approach pre-trains a RoBERTa model with a custom tokenisation pipeline on real-world code from open-source C/C++ projects. The model learns a deep knowledge representation of the code syntax and semantics, which we leverage to train vulnerability detection classifiers. We evaluate our approach on binary and multi-class vulnerability detection tasks across several datasets (Vuldeepecker, Draper, REVEAL and muVuldeepecker) and benchmarks (CodeXGLUE and D2A). The evaluation results show that VulBERTa achieves state-of-the-art performance and outperforms existing approaches across different datasets, despite its conceptual simplicity, and limited cost in terms of size of training data and number of model parameters. | ['Sergio Maffeis', 'Hazim Hanif'] | 2022-05-25 | null | null | null | null | ['vulnerability-detection'] | ['miscellaneous'] | [-7.06227183e-01 -1.41617954e-01 -4.57618713e-01 -2.27860630e-01
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86b14ae0-1297-49d8-b432-738d0131965c | multilevel-sentiment-analysis-in-arabic | 2205.12328 | null | https://arxiv.org/abs/2205.12328v1 | https://arxiv.org/pdf/2205.12328v1.pdf | Multilevel sentiment analysis in arabic | In this study, we aimed to improve the performance results of Arabic sentiment analysis. This can be achieved by investigating the most successful machine learning method and the most useful feature vector to classify sentiments in both term and document levels into two (positive or negative) categories. Moreover, specification of one polarity degree for the term that has more than one is investigated. Also to handle the negations and intensifications, some rules are developed. According to the obtained results, Artificial Neural Network classifier is nominated as the best classifier in both term and document level sentiment analysis (SA) for Arabic Language. Furthermore, the average F-score achieved in the term level SA for both positive and negative testing classes is 0.92. In the document level SA, the average F-score for positive testing classes is 0.94, while for negative classes is 0.93. | ['Ebru Sezer', 'Ahmed Nassar'] | 2022-05-24 | null | null | null | null | ['arabic-sentiment-analysis'] | ['natural-language-processing'] | [-4.22730409e-02 3.27115059e-02 -2.38860279e-01 -4.66671914e-01
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f14422d9-e290-4beb-9f77-79445d40ef1b | how-state-of-the-art-models-can-deal-with | null | null | https://aclanthology.org/2020.paclic-1.43 | https://aclanthology.org/2020.paclic-1.43.pdf | How State-Of-The-Art Models Can Deal With Long-Form Question Answering | null | ['Le-Minh Nguyen', 'Ha-Thanh Nguyen', 'Vu Tran', 'Minh-Quan Bui'] | null | null | null | null | paclic-2020-10 | ['long-form-question-answering'] | ['natural-language-processing'] | [-8.63703638e-02 1.71006292e-01 -6.22772932e-01 -4.08054382e-01
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37a5b9ac-e947-4294-8443-8056abd76a73 | an-efficient-general-purpose-modular-vision | 2306.17165 | null | https://arxiv.org/abs/2306.17165v1 | https://arxiv.org/pdf/2306.17165v1.pdf | An Efficient General-Purpose Modular Vision Model via Multi-Task Heterogeneous Training | We present a model that can perform multiple vision tasks and can be adapted to other downstream tasks efficiently. Despite considerable progress in multi-task learning, most efforts focus on learning from multi-label data: a single image set with multiple task labels. Such multi-label data sets are rare, small, and expensive. We say heterogeneous to refer to image sets with different task labels, or to combinations of single-task datasets. Few have explored training on such heterogeneous datasets. General-purpose vision models are still dominated by single-task pretraining, and it remains unclear how to scale up multi-task models by leveraging mainstream vision datasets designed for different purposes. The challenges lie in managing large intrinsic differences among vision tasks, including data distribution, architectures, task-specific modules, dataset scales, and sampling strategies. To address these challenges, we propose to modify and scale up mixture-of-experts (MoE) vision transformers, so that they can simultaneously learn classification, detection, and segmentation on diverse mainstream vision datasets including ImageNet, COCO, and ADE20K. Our approach achieves comparable results to single-task state-of-the-art models and demonstrates strong generalization on downstream tasks. Due to its emergent modularity, this general-purpose model decomposes into high-performing components, efficiently adapting to downstream tasks. We can fine-tune it with fewer training parameters, fewer model parameters, and less computation. Additionally, its modularity allows for easy expansion in continual-learning-without-forgetting scenarios. Finally, these functions can be controlled and combined to meet various demands of downstream tasks. | ['Chuang Gan', 'Erik Learned-Miller', 'Masayoshi Tomizuka', 'Wei Zhan', 'Yikang Shen', 'Mingyu Ding', 'Zitian Chen'] | 2023-06-29 | null | null | null | null | ['multi-task-learning'] | ['methodology'] | [ 2.24740461e-01 -3.01872671e-01 -3.55432779e-02 -2.74439692e-01
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e7750914-a756-433a-b477-bb16da6178f4 | real-time-high-quality-stereo-matching-system | 2212.00488 | null | https://arxiv.org/abs/2212.00488v1 | https://arxiv.org/pdf/2212.00488v1.pdf | Real-Time High-Quality Stereo Matching System on a GPU | In this paper, we propose a low error rate and real-time stereo vision system on GPU. Many stereo vision systems on GPU have been proposed to date. In those systems, the error rates and the processing speed are in trade-off relationship. We propose a real-time stereo vision system on GPU for the high resolution images. This system also maintains a low error rate compared to other fast systems. In our approach, we have implemented the cost aggregation (CA), cross-checking and median filter on GPU in order to realize the real-time processing. Its processing speed is 40 fps for 1436x992 pixels images when the maximum disparity is 145, and its error rate is the lowest among the GPU systems which are faster than 30 fps. | ['Tsutomu Maruyama', 'Qiong Chang'] | 2022-12-01 | null | null | null | null | ['stereo-matching-1'] | ['computer-vision'] | [-2.72444449e-02 -5.18065453e-01 5.20108700e-01 -3.06186616e-01
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297040e4-5c66-49b3-acdf-b61fabc17eb1 | ask-me-anything-in-your-native-language-1 | null | null | https://aclanthology.org/2022.naacl-main.30 | https://aclanthology.org/2022.naacl-main.30.pdf | Ask Me Anything in Your Native Language | Cross-lingual question answering is a thriving field in the modern world, helping people to search information on the web more efficiently. One of the important scenarios is to give an answer even there is no answer in the language a person asks a question with. We present a novel approach based on single encoder for query and passage for retrieval from multi-lingual collection, together with cross-lingual generative reader. It achieves a new state of the art in both retrieval and end-to-end tasks on the XOR TyDi dataset outperforming the previous results up to 10% on several languages. We find that our approach can be generalized to more than 20 languages in zero-shot approach and outperform all previous models by 12%. | ['Valentin Malykh', 'Irina Piontkovskaya', 'Dmitry Abulkhanov', 'Nikita Sorokin'] | null | null | null | null | naacl-2022-7 | ['cross-lingual-question-answering'] | ['natural-language-processing'] | [-3.46493781e-01 -2.96133220e-01 -3.14406566e-02 -1.92900464e-01
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a3ab882f-f419-4588-9b7c-971194af50ed | bertic-the-transformer-language-model-for | 2104.09243 | null | https://arxiv.org/abs/2104.09243v1 | https://arxiv.org/pdf/2104.09243v1.pdf | BERTić -- The Transformer Language Model for Bosnian, Croatian, Montenegrin and Serbian | In this paper we describe a transformer model pre-trained on 8 billion tokens of crawled text from the Croatian, Bosnian, Serbian and Montenegrin web domains. We evaluate the transformer model on the tasks of part-of-speech tagging, named-entity-recognition, geo-location prediction and commonsense causal reasoning, showing improvements on all tasks over state-of-the-art models. For commonsense reasoning evaluation, we introduce COPA-HR -- a translation of the Choice of Plausible Alternatives (COPA) dataset into Croatian. The BERTi\'c model is made available for free usage and further task-specific fine-tuning through HuggingFace. | ['Davor Lauc', 'Nikola Ljubešić'] | 2021-04-19 | null | null | null | null | ['commonsense-causal-reasoning'] | ['natural-language-processing'] | [-2.07453266e-01 4.96600240e-01 -3.56328815e-01 -4.85482991e-01
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742a47a8-2ee0-4d32-a6eb-27786627548d | multi-attribute-relation-extraction-mare | 2111.09035 | null | https://arxiv.org/abs/2111.09035v1 | https://arxiv.org/pdf/2111.09035v1.pdf | Multi-Attribute Relation Extraction (MARE) -- Simplifying the Application of Relation Extraction | Natural language understanding's relation extraction makes innovative and encouraging novel business concepts possible and facilitates new digitilized decision-making processes. Current approaches allow the extraction of relations with a fixed number of entities as attributes. Extracting relations with an arbitrary amount of attributes requires complex systems and costly relation-trigger annotations to assist these systems. We introduce multi-attribute relation extraction (MARE) as an assumption-less problem formulation with two approaches, facilitating an explicit mapping from business use cases to the data annotations. Avoiding elaborated annotation constraints simplifies the application of relation extraction approaches. The evaluation compares our models to current state-of-the-art event extraction and binary relation extraction methods. Our approaches show improvement compared to these on the extraction of general multi-attribute relations. | ['Albert Zündorf', 'Bodo Kraft', 'Philipp Kohl', 'Lars Klöser'] | 2021-11-17 | null | null | null | null | ['binary-relation-extraction'] | ['natural-language-processing'] | [ 6.65130839e-02 9.42416966e-01 -7.56913662e-01 -4.84337240e-01
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9384796d-9198-40f2-b93b-779bb56bfafb | towards-compact-cnns-via-collaborative | 2105.11228 | null | https://arxiv.org/abs/2105.11228v1 | https://arxiv.org/pdf/2105.11228v1.pdf | Towards Compact CNNs via Collaborative Compression | Channel pruning and tensor decomposition have received extensive attention in convolutional neural network compression. However, these two techniques are traditionally deployed in an isolated manner, leading to significant accuracy drop when pursuing high compression rates. In this paper, we propose a Collaborative Compression (CC) scheme, which joints channel pruning and tensor decomposition to compress CNN models by simultaneously learning the model sparsity and low-rankness. Specifically, we first investigate the compression sensitivity of each layer in the network, and then propose a Global Compression Rate Optimization that transforms the decision problem of compression rate into an optimization problem. After that, we propose multi-step heuristic compression to remove redundant compression units step-by-step, which fully considers the effect of the remaining compression space (i.e., unremoved compression units). Our method demonstrates superior performance gains over previous ones on various datasets and backbone architectures. For example, we achieve 52.9% FLOPs reduction by removing 48.4% parameters on ResNet-50 with only a Top-1 accuracy drop of 0.56% on ImageNet 2012. | ['Rongrong Ji', 'Qi Tian', 'Jincheng Ma', 'Fan Yang', 'Fei Chao', 'Mengdi Wang', 'Qixiang Ye', 'Jianzhuang Liu', 'Shaohui Lin', 'Yuchao Li'] | 2021-05-24 | null | http://openaccess.thecvf.com//content/CVPR2021/html/Li_Towards_Compact_CNNs_via_Collaborative_Compression_CVPR_2021_paper.html | http://openaccess.thecvf.com//content/CVPR2021/papers/Li_Towards_Compact_CNNs_via_Collaborative_Compression_CVPR_2021_paper.pdf | cvpr-2021-1 | ['neural-network-compression', 'neural-network-compression'] | ['methodology', 'miscellaneous'] | [ 3.11068207e-01 -2.37496216e-02 -2.91334778e-01 -3.74619275e-01
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53375e70-6554-4b77-a560-b5318c5cc635 | explicit-knowledge-transfer-for-weakly | 2211.16740 | null | https://arxiv.org/abs/2211.16740v3 | https://arxiv.org/pdf/2211.16740v3.pdf | Explicit Knowledge Transfer for Weakly-Supervised Code Generation | Large language models (LLMs) can acquire strong code-generation capabilities through few-shot learning. In contrast, supervised fine-tuning is still needed for smaller models to achieve good performance. Such fine-tuning demands a large number of task-specific NL-code pairs, which are expensive to obtain. In this paper, we attempt to transfer the code generation ability of an LLM to a smaller model with the aid of weakly-supervised data. More specifically, we propose explicit knowledge transfer (EKT), which uses the few-shot capabilities of a teacher LLM to create NL-code pairs that we then filter for correctness and fine-tune the student on. We evaluate EKT on the task of generating code solutions to math word problems from the GSM8k dataset. We find that EKT not only yields better performance than training with expert iteration, but also outperforms knowledge distillation, another form of knowledge transfer. A GPT-Neo 1.3B model trained using EKT with a GPT-J teacher achieves a 12.4% pass@100 on GSM8k, while the same student and teacher trained with knowledge distillation yield only a 3.7% pass@100. We also show that it is possible for a student model to outperform the teacher using EKT. | ['Dragomir Radev', 'Hailey Schoelkopf', 'Ansong Ni', 'Zhangir Azerbayev'] | 2022-11-30 | null | null | null | null | ['gsm8k'] | ['natural-language-processing'] | [ 2.12588325e-01 3.65476757e-01 -3.71737666e-02 -1.69416308e-01
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406ed225-57c4-4fa3-a944-ac864b407a6a | unsupervised-statistical-machine-translation | 1809.01272 | null | http://arxiv.org/abs/1809.01272v1 | http://arxiv.org/pdf/1809.01272v1.pdf | Unsupervised Statistical Machine Translation | While modern machine translation has relied on large parallel corpora, a
recent line of work has managed to train Neural Machine Translation (NMT)
systems from monolingual corpora only (Artetxe et al., 2018c; Lample et al.,
2018). Despite the potential of this approach for low-resource settings,
existing systems are far behind their supervised counterparts, limiting their
practical interest. In this paper, we propose an alternative approach based on
phrase-based Statistical Machine Translation (SMT) that significantly closes
the gap with supervised systems. Our method profits from the modular
architecture of SMT: we first induce a phrase table from monolingual corpora
through cross-lingual embedding mappings, combine it with an n-gram language
model, and fine-tune hyperparameters through an unsupervised MERT variant. In
addition, iterative backtranslation improves results further, yielding, for
instance, 14.08 and 26.22 BLEU points in WMT 2014 English-German and
English-French, respectively, an improvement of more than 7-10 BLEU points over
previous unsupervised systems, and closing the gap with supervised SMT (Moses
trained on Europarl) down to 2-5 BLEU points. Our implementation is available
at https://github.com/artetxem/monoses | ['Mikel Artetxe', 'Gorka Labaka', 'Eneko Agirre'] | 2018-09-04 | unsupervised-statistical-machine-translation-1 | https://aclanthology.org/D18-1399 | https://aclanthology.org/D18-1399.pdf | emnlp-2018-10 | ['unsupervised-machine-translation'] | ['natural-language-processing'] | [ 1.83381543e-01 1.07913740e-01 -4.23557639e-01 -2.35208645e-01
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ecef1396-145d-44d3-bb45-fc30326577e9 | class-incremental-learning-using-diffusion | 2306.17560 | null | https://arxiv.org/abs/2306.17560v1 | https://arxiv.org/pdf/2306.17560v1.pdf | Class-Incremental Learning using Diffusion Model for Distillation and Replay | Class-incremental learning aims to learn new classes in an incremental fashion without forgetting the previously learned ones. Several research works have shown how additional data can be used by incremental models to help mitigate catastrophic forgetting. In this work, following the recent breakthrough in text-to-image generative models and their wide distribution, we propose the use of a pretrained Stable Diffusion model as a source of additional data for class-incremental learning. Compared to competitive methods that rely on external, often unlabeled, datasets of real images, our approach can generate synthetic samples belonging to the same classes as the previously encountered images. This allows us to use those additional data samples not only in the distillation loss but also for replay in the classification loss. Experiments on the competitive benchmarks CIFAR100, ImageNet-Subset, and ImageNet demonstrate how this new approach can be used to further improve the performance of state-of-the-art methods for class-incremental learning on large scale datasets. | ['Tsuyoshi Murata', 'Yin Jun Phua', 'Xin Liu', 'Quentin Jodelet'] | 2023-06-30 | null | null | null | null | ['class-incremental-learning', 'incremental-learning'] | ['computer-vision', 'methodology'] | [ 3.40849817e-01 3.46223265e-01 5.50035946e-03 -2.95770288e-01
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4dbea16b-58e5-470f-beff-8a0b119a9d2f | principles-and-examples-of-plausible | 1703.01697 | null | http://arxiv.org/abs/1703.01697v2 | http://arxiv.org/pdf/1703.01697v2.pdf | Principles and Examples of Plausible Reasoning and Propositional Plausible Logic | Plausible reasoning concerns situations whose inherent lack of precision is
not quantified; that is, there are no degrees or levels of precision, and hence
no use of numbers like probabilities. A hopefully comprehensive set of
principles that clarifies what it means for a formal logic to do plausible
reasoning is presented. A new propositional logic, called Propositional
Plausible Logic (PPL), is defined and applied to some important examples. PPL
is the only non-numeric non-monotonic logic we know of that satisfies all the
principles and correctly reasons with all the examples. Some important results
about PPL are proved. | ['David Billington'] | 2017-03-06 | null | null | null | null | ['formal-logic'] | ['reasoning'] | [ 5.17773479e-02 9.95053053e-01 -5.26158869e-01 -5.96351027e-01
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7b2a0357-7f36-47a1-bb8e-3b1ddc737149 | violence-detection-in-videos | 2109.08941 | null | https://arxiv.org/abs/2109.08941v1 | https://arxiv.org/pdf/2109.08941v1.pdf | Violence Detection in Videos | In the recent years, there has been a tremendous increase in the amount of video content uploaded to social networking and video sharing websites like Facebook and Youtube. As of result of this, the risk of children getting exposed to adult and violent content on the web also increased. To address this issue, an approach to automatically detect violent content in videos is proposed in this work. Here, a novel attempt is made also to detect the category of violence present in a video. A system which can automatically detect violence from both Hollywood movies and videos from the web is extremely useful not only in parental control but also for applications related to movie ratings, video surveillance, genre classification and so on. Here, both audio and visual features are used to detect violence. MFCC features are used as audio cues. Blood, Motion, and SentiBank features are used as visual cues. Binary SVM classifiers are trained on each of these features to detect violence. Late fusion using a weighted sum of classification scores is performed to get final classification scores for each of the violence class target by the system. To determine optimal weights for each of the violence classes an approach based on grid search is employed. Publicly available datasets, mainly Violent Scene Detection (VSD), are used for classifier training, weight calculation, and testing. The performance of the system is evaluated on two classification tasks, Multi-Class classification, and Binary Classification. The results obtained for Binary Classification are better than the baseline results from MediaEval-2014. | ['Andreas Dengel', 'Christian Schulze', 'Praveen Tirupattur'] | 2021-09-18 | null | null | null | null | ['genre-classification'] | ['computer-vision'] | [ 2.00353235e-01 -1.48988590e-01 -2.21542865e-01 -2.94726014e-01
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d3af907f-93c8-408c-aeae-f0c9cd49cae1 | on-the-robustness-of-generative-retrieval | 2306.12756 | null | https://arxiv.org/abs/2306.12756v1 | https://arxiv.org/pdf/2306.12756v1.pdf | On the Robustness of Generative Retrieval Models: An Out-of-Distribution Perspective | Recently, we have witnessed generative retrieval increasingly gaining attention in the information retrieval (IR) field, which retrieves documents by directly generating their identifiers. So far, much effort has been devoted to developing effective generative retrieval models. There has been less attention paid to the robustness perspective. When a new retrieval paradigm enters into the real-world application, it is also critical to measure the out-of-distribution (OOD) generalization, i.e., how would generative retrieval models generalize to new distributions. To answer this question, firstly, we define OOD robustness from three perspectives in retrieval problems: 1) The query variations; 2) The unforeseen query types; and 3) The unforeseen tasks. Based on this taxonomy, we conduct empirical studies to analyze the OOD robustness of several representative generative retrieval models against dense retrieval models. The empirical results indicate that the OOD robustness of generative retrieval models requires enhancement. We hope studying the OOD robustness of generative retrieval models would be advantageous to the IR community. | ['Xueqi Cheng', 'Wei Chen', 'Jiafeng Guo', 'Ruqing Zhang', 'Yu-An Liu'] | 2023-06-22 | null | null | null | null | ['retrieval', 'information-retrieval'] | ['methodology', 'natural-language-processing'] | [ 5.45977913e-02 -2.26177588e-01 2.72554369e-03 -7.58184567e-02
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b0aa0429-ac3a-424e-9462-af23d6794f5f | multistage-model-for-robust-face-alignment | 2002.01075 | null | https://arxiv.org/abs/2002.01075v1 | https://arxiv.org/pdf/2002.01075v1.pdf | Multistage Model for Robust Face Alignment Using Deep Neural Networks | An ability to generalize unconstrained conditions such as severe occlusions and large pose variations remains a challenging goal to achieve in face alignment. In this paper, a multistage model based on deep neural networks is proposed which takes advantage of spatial transformer networks, hourglass networks and exemplar-based shape constraints. First, a spatial transformer - generative adversarial network which consists of convolutional layers and residual units is utilized to solve the initialization issues caused by face detectors, such as rotation and scale variations, to obtain improved face bounding boxes for face alignment. Then, stacked hourglass network is employed to obtain preliminary locations of landmarks as well as their corresponding scores. In addition, an exemplar-based shape dictionary is designed to determine landmarks with low scores based on those with high scores. By incorporating face shape constraints, misaligned landmarks caused by occlusions or cluttered backgrounds can be considerably improved. Extensive experiments based on challenging benchmark datasets are performed to demonstrate the superior performance of the proposed method over other state-of-the-art methods. | ['Jian Zhou', 'Rui Cheng', 'Huabin Wang', 'Hon Keung Kwan', 'Liang Tao'] | 2020-02-04 | null | null | null | null | ['robust-face-alignment'] | ['computer-vision'] | [-1.11454226e-01 -1.12023495e-01 1.30549803e-01 -6.99408174e-01
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081a56ee-c1ae-47bb-98f9-49f81fe58851 | innovative-drug-like-molecule-generation-from | 2211.06566 | null | https://arxiv.org/abs/2211.06566v1 | https://arxiv.org/pdf/2211.06566v1.pdf | Innovative Drug-like Molecule Generation from Flow-based Generative Model | To design a drug given a biological molecule by using deep learning methods, there are many successful models published recently. People commonly used generative models to design new molecules given certain protein. LiGAN was regarded as the baseline of deep learning model which was developed on convolutional neural networks. Recently, GraphBP showed its ability to predict innovative "real" chemicals that the binding affinity outperformed with traditional molecular docking methods by using a flow-based generative model with a graph neural network and multilayer perception. However, all those methods regarded proteins as rigid bodies and only include a very small part of proteins related to binding. However, the dynamics of proteins are essential for drug binding. Based on GraphBP, we proposed to generate more solid work derived from protein data bank. The results will be evaluated by validity and binding affinity by using a computational chemistry algorithm. | ['Linxiaoyi Wan', 'Haotian Zhang'] | 2022-11-12 | null | null | null | null | ['molecular-docking'] | ['medical'] | [-3.07098269e-01 7.82846957e-02 -9.75805894e-02 -2.23123595e-01
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d3524eb7-5594-4d42-93c2-a534f7016b61 | 1st-place-solution-to-the-epic-kitchens | 2207.05730 | null | https://arxiv.org/abs/2207.05730v1 | https://arxiv.org/pdf/2207.05730v1.pdf | 1st Place Solution to the EPIC-Kitchens Action Anticipation Challenge 2022 | In this report, we describe the technical details of our submission to the EPIC-Kitchens Action Anticipation Challenge 2022. In this competition, we develop the following two approaches. 1) Anticipation Time Knowledge Distillation using the soft labels learned by the teacher model as knowledge to guide the student network to learn the information of anticipation time; 2) Verb-Noun Relation Module for building the relationship between verbs and nouns. Our method achieves state-of-the-art results on the testing set of EPIC-Kitchens Action Anticipation Challenge 2022. | ['Changxing Ding', 'Zeyu Jiang'] | 2022-07-10 | null | null | null | null | ['action-anticipation'] | ['computer-vision'] | [ 3.10195573e-02 5.48707128e-01 -3.79101127e-01 -7.63943434e-01
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b2c16508-385c-47a4-8275-3977d19cb48c | end-to-end-learning-for-stochastic | 2306.04174 | null | https://arxiv.org/abs/2306.04174v2 | https://arxiv.org/pdf/2306.04174v2.pdf | End-to-End Learning for Stochastic Optimization: A Bayesian Perspective | We develop a principled approach to end-to-end learning in stochastic optimization. First, we show that the standard end-to-end learning algorithm admits a Bayesian interpretation and trains a posterior Bayes action map. Building on the insights of this analysis, we then propose new end-to-end learning algorithms for training decision maps that output solutions of empirical risk minimization and distributionally robust optimization problems, two dominant modeling paradigms in optimization under uncertainty. Numerical results for a synthetic newsvendor problem illustrate the key differences between alternative training schemes. We also investigate an economic dispatch problem based on real data to showcase the impact of the neural network architecture of the decision maps on their test performance. | ['Tobias Sutter', 'Daniel Kuhn', 'Yves Rychener'] | 2023-06-07 | null | null | null | null | ['stochastic-optimization'] | ['methodology'] | [-2.90956229e-01 5.07762790e-01 -2.39757627e-01 -7.69414485e-01
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5375edfa-b5a2-428d-a9c3-5ad147ac499e | implicit-riemannian-concave-potential-maps | 2110.01288 | null | https://arxiv.org/abs/2110.01288v1 | https://arxiv.org/pdf/2110.01288v1.pdf | Implicit Riemannian Concave Potential Maps | We are interested in the challenging problem of modelling densities on Riemannian manifolds with a known symmetry group using normalising flows. This has many potential applications in physical sciences such as molecular dynamics and quantum simulations. In this work we combine ideas from implicit neural layers and optimal transport theory to propose a generalisation of existing work on exponential map flows, Implicit Riemannian Concave Potential Maps, IRCPMs. IRCPMs have some nice properties such as simplicity of incorporating symmetries and are less expensive than ODE-flows. We provide an initial theoretical analysis of its properties and layout sufficient conditions for stable optimisation. Finally, we illustrate the properties of IRCPMs with density estimation experiments on tori and spheres. | ['Sébastien Racanière', 'Danilo J. Rezende'] | 2021-10-04 | null | null | null | null | ['normalising-flows'] | ['methodology'] | [-3.10980678e-01 4.16515201e-01 4.75952178e-02 -5.29653169e-02
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78056ce2-83b8-44a3-b4bb-6be923e3e192 | recent-trends-in-deep-learning-based | 1908.03628 | null | https://arxiv.org/abs/1908.03628v2 | https://arxiv.org/pdf/1908.03628v2.pdf | Recent Trends in Deep Learning Based Personality Detection | Recently, the automatic prediction of personality traits has received a lot of attention. Specifically, personality trait prediction from multimodal data has emerged as a hot topic within the field of affective computing. In this paper, we review significant machine learning models which have been employed for personality detection, with an emphasis on deep learning-based methods. This review paper provides an overview of the most popular approaches to automated personality detection, various computational datasets, its industrial applications, and state-of-the-art machine learning models for personality detection with specific focus on multimodal approaches. Personality detection is a very broad and diverse topic: this survey only focuses on computational approaches and leaves out psychological studies on personality detection. | ['Yash Mehta', 'Navonil Majumder', 'Erik Cambria', 'Alexander Gelbukh'] | 2019-08-07 | null | null | null | null | ['personality-trait-recognition'] | ['computer-vision'] | [-2.28987038e-01 4.67463881e-02 -8.06611497e-03 -5.70194781e-01
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860e5a79-f419-4368-933f-c712e89a45ec | non-negative-networks-against-adversarial | 1806.06108 | null | http://arxiv.org/abs/1806.06108v2 | http://arxiv.org/pdf/1806.06108v2.pdf | Non-Negative Networks Against Adversarial Attacks | Adversarial attacks against neural networks are a problem of considerable
importance, for which effective defenses are not yet readily available. We make
progress toward this problem by showing that non-negative weight constraints
can be used to improve resistance in specific scenarios. In particular, we show
that they can provide an effective defense for binary classification problems
with asymmetric cost, such as malware or spam detection. We also show the
potential for non-negativity to be helpful to non-binary problems by applying
it to image classification. | ['Jared Sylvester', 'William Fleshman', 'Mark McLean', 'Edward Raff', 'Steven Forsyth'] | 2018-06-15 | null | null | null | null | ['spam-detection'] | ['natural-language-processing'] | [ 6.69808507e-01 1.04402214e-01 -2.94839472e-01 -4.88541067e-01
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cbf689c1-382f-44a8-bd05-b11fa8bfeb11 | a-higher-order-semantic-dependency-parser | 2201.11312 | null | https://arxiv.org/abs/2201.11312v1 | https://arxiv.org/pdf/2201.11312v1.pdf | A Higher-Order Semantic Dependency Parser | Higher-order features bring significant accuracy gains in semantic dependency parsing. However, modeling higher-order features with exact inference is NP-hard. Graph neural networks (GNNs) have been demonstrated to be an effective tool for solving NP-hard problems with approximate inference in many graph learning tasks. Inspired by the success of GNNs, we investigate building a higher-order semantic dependency parser by applying GNNs. Instead of explicitly extracting higher-order features from intermediate parsing graphs, GNNs aggregate higher-order information concisely by stacking multiple GNN layers. Experimental results show that our model outperforms the previous state-of-the-art parser on the SemEval 2015 Task 18 English datasets. | ['Zhiqiang Gao', 'Yikemaiti Sataer', 'Yunlong Fan', 'Bin Li'] | 2022-01-27 | null | null | null | null | ['semantic-dependency-parsing'] | ['natural-language-processing'] | [-1.72619432e-01 8.24889600e-01 -3.07222694e-01 -6.99184716e-01
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cd0761cd-9a06-488f-b0af-c414fc67a1eb | mum-mix-image-tiles-and-unmix-feature-tiles | 2111.10958 | null | https://arxiv.org/abs/2111.10958v2 | https://arxiv.org/pdf/2111.10958v2.pdf | MUM : Mix Image Tiles and UnMix Feature Tiles for Semi-Supervised Object Detection | Many recent semi-supervised learning (SSL) studies build teacher-student architecture and train the student network by the generated supervisory signal from the teacher. Data augmentation strategy plays a significant role in the SSL framework since it is hard to create a weak-strong augmented input pair without losing label information. Especially when extending SSL to semi-supervised object detection (SSOD), many strong augmentation methodologies related to image geometry and interpolation-regularization are hard to utilize since they possibly hurt the location information of the bounding box in the object detection task. To address this, we introduce a simple yet effective data augmentation method, Mix/UnMix (MUM), which unmixes feature tiles for the mixed image tiles for the SSOD framework. Our proposed method makes mixed input image tiles and reconstructs them in the feature space. Thus, MUM can enjoy the interpolation-regularization effect from non-interpolated pseudo-labels and successfully generate a meaningful weak-strong pair. Furthermore, MUM can be easily equipped on top of various SSOD methods. Extensive experiments on MS-COCO and PASCAL VOC datasets demonstrate the superiority of MUM by consistently improving the mAP performance over the baseline in all the tested SSOD benchmark protocols. | ['Nojun Kwak', 'Jongkeun Na', 'Jisoo Jeong', 'Seunghyeon Seo', 'Jooyoung Jang', 'Jongmok Kim'] | 2021-11-22 | null | null | null | null | ['semi-supervised-object-detection'] | ['computer-vision'] | [ 4.13603574e-01 2.29597047e-01 -2.17216522e-01 -4.78047490e-01
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ab4a241d-31a1-446c-87ab-b78db15ce6a5 | rescuespeech-a-german-corpus-for-speech | 2306.04054 | null | https://arxiv.org/abs/2306.04054v1 | https://arxiv.org/pdf/2306.04054v1.pdf | RescueSpeech: A German Corpus for Speech Recognition in Search and Rescue Domain | Despite recent advancements in speech recognition, there are still difficulties in accurately transcribing conversational and emotional speech in noisy and reverberant acoustic environments. This poses a particular challenge in the search and rescue (SAR) domain, where transcribing conversations among rescue team members is crucial to support real-time decision-making. The scarcity of speech data and associated background noise in SAR scenarios make it difficult to deploy robust speech recognition systems. To address this issue, we have created and made publicly available a German speech dataset called RescueSpeech. This dataset includes real speech recordings from simulated rescue exercises. Additionally, we have released competitive training recipes and pre-trained models. Our study indicates that the current level of performance achieved by state-of-the-art methods is still far from being acceptable. | ['Josef van Genabith', 'Ivana Kruijff Korbayova', 'Bernd Kiefer', 'Mirco Ravanelli', 'Sangeet Sagar'] | 2023-06-06 | null | null | null | null | ['robust-speech-recognition'] | ['speech'] | [ 1.79551795e-01 -5.01073487e-02 6.33593798e-01 -4.45066035e-01
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4b41f4b9-7090-421c-ac31-26bfd2dc358e | a-general-approach-for-traffic-classification | null | null | https://ieeexplore.ieee.org/document/9626148/authors#authors | https://ieeexplore.ieee.org/document/9626148/authors#authors | A General Approach for Traffic Classification in Wireless Networks Using Deep Learning | Traffic Classification (TC) systems allow inferring the application that is generating the traffic being analyzed. State-of-the-art TC algorithms are based on Deep Learning (DL) and have outperformed traditional methods in complex and modern scenarios, even if traffic is encrypted. Most of the works on TC assume the traffic flows on a wired network under the same network management domain. This assumption limits the capabilities of TC systems in wireless networks since users’ traffic on one network domain can be negatively impacted by undetected users’ traffic from other network domains or detected ones but with no traffic context in a shared spectrum. To solve this problem, we introduce a novel framework to achieve TC at any layer on the radio network stack. We propose a spectrum-based procedure that uses a DL-based classifier to realize this framework. We design two DL-based classifiers, a novel Convolutional Neural Network (CNN) spectrum-based TC and a Recurrent Neural Networks (RNN) as baseline architecture, and benchmark their performance on three TC tasks at different radio stack layers. The datasets were generated by combining packet traces from real transmissions with an 802.11 standard-compliant waveform generator. Performance evaluations show that the best model can achieve an accuracy above 92% in the most demanding TC task, a drop of only 4.37% in accuracy compared to a byte-based DL approach, with micro-second per-packet prediction time, which is very promising for delivering real-time spectrum-based traffic analyzers. | ['Miguel Camelo'] | 2022-03-01 | null | null | null | 2022-03-2022-3 | ['traffic-classification'] | ['miscellaneous'] | [ 5.67767560e-01 -1.62750632e-01 -4.97308016e-01 -2.07057223e-01
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eb6ee306-b5a2-4d65-b1db-a9a7c99e8fcb | on-the-validity-of-conformal-prediction-for | 2306.07252 | null | https://arxiv.org/abs/2306.07252v3 | https://arxiv.org/pdf/2306.07252v3.pdf | On the Validity of Conformal Prediction for Network Data Under Non-Uniform Sampling | We study the properties of conformal prediction for network data under various sampling mechanisms that commonly arise in practice but often result in a non-representative sample of nodes. We interpret these sampling mechanisms as selection rules applied to a superpopulation and study the validity of conformal prediction conditional on an appropriate selection event. We show that the sampled subarray is exchangeable conditional on the selection event if the selection rule satisfies a permutation invariance property and a joint exchangeability condition holds for the superpopulation. Our result implies the finite-sample validity of conformal prediction for certain selection events related to ego networks and snowball sampling. We also show that when data are sampled via a random walk on a graph, a variant of weighted conformal prediction yields asymptotically valid prediction sets for an independently selected node from the population. | ['Robert Lunde'] | 2023-06-12 | null | null | null | null | ['conformal-prediction', 'conformal-prediction'] | ['computer-vision', 'reasoning'] | [ 6.65483832e-01 6.37444735e-01 -3.89013559e-01 -3.07779789e-01
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deef7177-1812-45da-b783-9efccc014580 | frame-fusion-with-vehicle-motion-prediction | 2306.10699 | null | https://arxiv.org/abs/2306.10699v1 | https://arxiv.org/pdf/2306.10699v1.pdf | Frame Fusion with Vehicle Motion Prediction for 3D Object Detection | In LiDAR-based 3D detection, history point clouds contain rich temporal information helpful for future prediction. In the same way, history detections should contribute to future detections. In this paper, we propose a detection enhancement method, namely FrameFusion, which improves 3D object detection results by fusing history frames. In FrameFusion, we ''forward'' history frames to the current frame and apply weighted Non-Maximum-Suppression on dense bounding boxes to obtain a fused frame with merged boxes. To ''forward'' frames, we use vehicle motion models to estimate the future pose of the bounding boxes. However, the commonly used constant velocity model fails naturally on turning vehicles, so we explore two vehicle motion models to address this issue. On Waymo Open Dataset, our FrameFusion method consistently improves the performance of various 3D detectors by about $2$ vehicle level 2 APH with negligible latency and slightly enhances the performance of the temporal fusion method MPPNet. We also conduct extensive experiments on motion model selection. | ['Chao Ma', 'Naiyan Wang', 'Feng Wang', 'Xirui Li'] | 2023-06-19 | null | null | null | null | ['motion-prediction', '3d-object-detection', 'future-prediction'] | ['computer-vision', 'computer-vision', 'computer-vision'] | [-4.31267709e-01 -3.75434160e-01 -3.97918403e-01 -4.84058917e-01
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3bdb31e2-8008-4c27-98e7-7f89fb966c1b | enhancing-covid-19-severity-analysis-through | 2303.07130 | null | https://arxiv.org/abs/2303.07130v3 | https://arxiv.org/pdf/2303.07130v3.pdf | Enhancing COVID-19 Severity Analysis through Ensemble Methods | Computed Tomography (CT) scans provide a detailed image of the lungs, allowing clinicians to observe the extent of damage caused by COVID-19. The CT severity score (CTSS) based scoring method is used to identify the extent of lung involvement observed on a CT scan. This paper presents a domain knowledge-based pipeline for extracting regions of infection in COVID-19 patients using a combination of image-processing algorithms and a pre-trained UNET model. The severity of the infection is then classified into different categories using an ensemble of three machine-learning models: Extreme Gradient Boosting, Extremely Randomized Trees, and Support Vector Machine. The proposed system was evaluated on a validation dataset in the AI-Enabled Medical Image Analysis Workshop and COVID-19 Diagnosis Competition (AI-MIA-COV19D) and achieved a macro F1 score of 64%. These results demonstrate the potential of combining domain knowledge with machine learning techniques for accurate COVID-19 diagnosis using CT scans. The implementation of the proposed system for severity analysis is available at \textit{https://github.com/aanandt/Enhancing-COVID-19-Severity-Analysis-through-Ensemble-Methods.git } | ['Hema A Murthy', 'Anand Thyagachandran'] | 2023-03-13 | null | null | null | null | ['covid-19-detection'] | ['medical'] | [ 7.97127113e-02 -2.73397207e-01 -1.05330378e-01 -2.71757782e-01
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619c12e6-2ab1-4526-84d7-84ff1db3f591 | estimation-and-hac-based-inference-for | 1912.06307 | null | https://arxiv.org/abs/1912.06307v4 | https://arxiv.org/pdf/1912.06307v4.pdf | High-Dimensional Granger Causality Tests with an Application to VIX and News | We study Granger causality testing for high-dimensional time series using regularized regressions. To perform proper inference, we rely on heteroskedasticity and autocorrelation consistent (HAC) estimation of the asymptotic variance and develop the inferential theory in the high-dimensional setting. To recognize the time series data structures we focus on the sparse-group LASSO estimator, which includes the LASSO and the group LASSO as special cases. We establish the debiased central limit theorem for low dimensional groups of regression coefficients and study the HAC estimator of the long-run variance based on the sparse-group LASSO residuals. This leads to valid time series inference for individual regression coefficients as well as groups, including Granger causality tests. The treatment relies on a new Fuk-Nagaev inequality for a class of $\tau$-mixing processes with heavier than Gaussian tails, which is of independent interest. In an empirical application, we study the Granger causal relationship between the VIX and financial news. | ['Eric Ghysels', 'Jonas Striaukas', 'Andrii Babii'] | 2019-12-13 | null | null | null | null | ['time-series-regression'] | ['time-series'] | [-2.67035097e-01 -2.47284159e-01 -3.40127289e-01 -3.80633235e-01
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e79a9966-aa9d-404a-9e59-342a89275e7b | a-character-level-ngram-based-mt-approach-for | null | null | https://openreview.net/forum?id=U_cqFnIXla | https://openreview.net/pdf?id=U_cqFnIXla | A Character-level Ngram-based MT Approach for Lexical Normalization in Social Media | This paper presents an ngram-based MT approach that operates at character-level to generate possible canonical forms for lexical variants in social media text. It utilizes a joint n-gram model to learn edit sequences of word pairs, thus overcomes the shortage of phrase-based approach that is unable to capture dependencies across phrases. We evaluate our approach on two English tweet datasets and observe that the ngram-based approach significantly outperforms phrase-based approach in normalization task. Our simple model achieves a broad coverage on diverse variants which is comparable to complex hybrid systems. | ['Anonymous'] | 2021-12-17 | null | null | null | acl-arr-december-2022-12 | ['lexical-normalization'] | ['natural-language-processing'] | [ 3.31545293e-01 -2.96364762e-02 -4.44092959e-01 -2.49787256e-01
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d4dca77f-a385-4ab7-8248-9ab2e60d5133 | darer-dual-task-temporal-relational-recurrent | 2203.03856 | null | https://arxiv.org/abs/2203.03856v1 | https://arxiv.org/pdf/2203.03856v1.pdf | DARER: Dual-task Temporal Relational Recurrent Reasoning Network for Joint Dialog Sentiment Classification and Act Recognition | The task of joint dialog sentiment classification (DSC) and act recognition (DAR) aims to simultaneously predict the sentiment label and act label for each utterance in a dialog. In this paper, we put forward a new framework which models the explicit dependencies via integrating \textit{prediction-level interactions} other than semantics-level interactions, more consistent with human intuition. Besides, we propose a speaker-aware temporal graph (SATG) and a dual-task relational temporal graph (DRTG) to introduce \textit{temporal relations} into dialog understanding and dual-task reasoning. To implement our framework, we propose a novel model dubbed DARER, which first generates the context-, speaker- and temporal-sensitive utterance representations via modeling SATG, then conducts recurrent dual-task relational reasoning on DRTG, in which process the estimated label distributions act as key clues in prediction-level interactions. Experiment results show that DARER outperforms existing models by large margins while requiring much less computation resource and costing less training time. Remarkably, on DSC task in Mastodon, DARER gains a relative improvement of about 25% over previous best model in terms of F1, with less than 50% parameters and about only 60% required GPU memory. | ['Ivor W. Tsang', 'Bowen Xing'] | 2022-03-08 | null | https://aclanthology.org/2022.findings-acl.286 | https://aclanthology.org/2022.findings-acl.286.pdf | findings-acl-2022-5 | ['relational-reasoning', 'dialog-act-classification'] | ['natural-language-processing', 'natural-language-processing'] | [ 4.07792367e-02 4.67911482e-01 -3.17707956e-01 -1.03590977e+00
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d820315d-c275-497c-8092-2c9bce05bb26 | unique-geometry-and-texture-from | 2003.08885 | null | https://arxiv.org/abs/2003.08885v3 | https://arxiv.org/pdf/2003.08885v3.pdf | Unique Geometry and Texture from Corresponding Image Patches | We present a sufficient condition for recovering unique texture and viewpoints from unknown orthographic projections of a flat texture process. We show that four observations are sufficient in general, and we characterize the ambiguous cases. The results are applicable to shape from texture and texture-based structure from motion. | ['Todd Zickler', 'Dor Verbin', 'Steven J. Gortler'] | 2020-03-19 | null | null | null | null | ['shape-from-texture'] | ['computer-vision'] | [ 4.82936472e-01 4.51661125e-02 3.81503962e-02 -3.45627487e-01
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e380342c-3d07-4bd2-8b73-d538a05ded07 | object-centric-multi-task-learning-for-human | 2303.06800 | null | https://arxiv.org/abs/2303.06800v1 | https://arxiv.org/pdf/2303.06800v1.pdf | Object-Centric Multi-Task Learning for Human Instances | Human is one of the most essential classes in visual recognition tasks such as detection, segmentation, and pose estimation. Although much effort has been put into individual tasks, multi-task learning for these three tasks has been rarely studied. In this paper, we explore a compact multi-task network architecture that maximally shares the parameters of the multiple tasks via object-centric learning. To this end, we propose a novel query design to encode the human instance information effectively, called human-centric query (HCQ). HCQ enables for the query to learn explicit and structural information of human as well such as keypoints. Besides, we utilize HCQ in prediction heads of the target tasks directly and also interweave HCQ with the deformable attention in Transformer decoders to exploit a well-learned object-centric representation. Experimental results show that the proposed multi-task network achieves comparable accuracy to state-of-the-art task-specific models in human detection, segmentation, and pose estimation task, while it consumes less computational costs. | ['ByungIn Yoo', 'Seung-In Park', 'Seongeun Kim', 'Solae Lee', 'Sangil Jung', 'Hyeongseok Son'] | 2023-03-13 | null | null | null | null | ['human-detection'] | ['computer-vision'] | [-5.24643734e-02 -6.05850853e-02 -1.66885227e-01 -4.35619444e-01
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4.23814088e-01 -1.12389982e+00 3.27821404e-01 -5.52400984e-02] | [7.528437614440918, -0.4846934676170349] |
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