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ee08c54c-7f3c-4bca-a03f-4689594e7bd7 | towards-a-fair-comparison-and-realistic | 2205.12569 | null | https://arxiv.org/abs/2205.12569v2 | https://arxiv.org/pdf/2205.12569v2.pdf | Towards a Fair Comparison and Realistic Evaluation Framework of Android Malware Detectors based on Static Analysis and Machine Learning | As in other cybersecurity areas, machine learning (ML) techniques have emerged as a promising solution to detect Android malware. In this sense, many proposals employing a variety of algorithms and feature sets have been presented to date, often reporting impresive detection performances. However, the lack of reproducibility and the absence of a standard evaluation framework make these proposals difficult to compare. In this paper, we perform an analysis of 10 influential research works on Android malware detection using a common evaluation framework. We have identified five factors that, if not taken into account when creating datasets and designing detectors, significantly affect the trained ML models and their performances. In particular, we analyze the effect of (1) the presence of duplicated samples, (2) label (goodware/greyware/malware) attribution, (3) class imbalance, (4) the presence of apps that use evasion techniques and, (5) the evolution of apps. Based on this extensive experimentation, we conclude that the studied ML-based detectors have been evaluated optimistically, which justifies the good published results. Our findings also highlight that it is imperative to generate realistic experimental scenarios, taking into account the aforementioned factors, to foster the rise of better ML-based Android malware detection solutions. | ['Jose Miguel-Alonso', 'Alexander Mendiburu', 'Usue Mori', 'Borja Molina-Coronado'] | 2022-05-25 | null | null | null | null | ['android-malware-detection'] | ['miscellaneous'] | [ 2.41376236e-01 -2.67057657e-01 -5.43922782e-01 -3.75055261e-02
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ac823ae1-8db4-43e8-8c32-4e2ebc4df46d | chinese-temporal-tagging-with-heideltime | null | null | https://aclanthology.org/E14-4026 | https://aclanthology.org/E14-4026.pdf | Chinese Temporal Tagging with HeidelTime | null | ['Jannik Str{\\"o}tgen', 'Michael Gertz', 'Hui Li', 'Julian Zell'] | 2014-04-01 | null | null | null | eacl-2014-4 | ['temporal-information-extraction', 'temporal-tagging'] | ['natural-language-processing', 'natural-language-processing'] | [-8.63703638e-02 1.71006292e-01 -6.22772932e-01 -4.08054382e-01
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dec797ba-bc8b-479a-96f3-7f05386d0350 | solo-t-dirl-socially-aware-dynamic-local | 2209.07996 | null | https://arxiv.org/abs/2209.07996v1 | https://arxiv.org/pdf/2209.07996v1.pdf | SoLo T-DIRL: Socially-Aware Dynamic Local Planner based on Trajectory-Ranked Deep Inverse Reinforcement Learning | This work proposes a new framework for a socially-aware dynamic local planner in crowded environments by building on the recently proposed Trajectory-ranked Maximum Entropy Deep Inverse Reinforcement Learning (T-MEDIRL). To address the social navigation problem, our multi-modal learning planner explicitly considers social interaction factors, as well as social-awareness factors into T-MEDIRL pipeline to learn a reward function from human demonstrations. Moreover, we propose a novel trajectory ranking score using the sudden velocity change of pedestrians around the robot to address the sub-optimality in human demonstrations. Our evaluation shows that this method can successfully make a robot navigate in a crowded social environment and outperforms the state-of-art social navigation methods in terms of the success rate, navigation time, and invasion rate. | ['Maani Ghaffari', 'Tribhi Kathuria', 'Theodor Chakhachiro', 'Yifan Xu'] | 2022-09-16 | null | null | null | null | ['social-navigation'] | ['robots'] | [-3.94642413e-01 4.47326183e-01 1.59950256e-01 -1.46167606e-01
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c5c8d89b-f77a-4e69-bfdd-0b349feb6ca8 | think-positive-towards-twitter-sentiment | null | null | https://aclanthology.org/S14-2115 | https://aclanthology.org/S14-2115.pdf | Think Positive: Towards Twitter Sentiment Analysis from Scratch | null | ["C{\\'\\i}cero dos Santos"] | 2014-08-01 | null | null | null | semeval-2014-8 | ['twitter-sentiment-analysis'] | ['natural-language-processing'] | [-8.63703638e-02 1.71006292e-01 -6.22772932e-01 -4.08054382e-01
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d983b636-0e9e-4ae4-bf66-290f583660b5 | compact-transformer-tracker-with-correlative | 2301.10938 | null | https://arxiv.org/abs/2301.10938v1 | https://arxiv.org/pdf/2301.10938v1.pdf | Compact Transformer Tracker with Correlative Masked Modeling | Transformer framework has been showing superior performances in visual object tracking for its great strength in information aggregation across the template and search image with the well-known attention mechanism. Most recent advances focus on exploring attention mechanism variants for better information aggregation. We find these schemes are equivalent to or even just a subset of the basic self-attention mechanism. In this paper, we prove that the vanilla self-attention structure is sufficient for information aggregation, and structural adaption is unnecessary. The key is not the attention structure, but how to extract the discriminative feature for tracking and enhance the communication between the target and search image. Based on this finding, we adopt the basic vision transformer (ViT) architecture as our main tracker and concatenate the template and search image for feature embedding. To guide the encoder to capture the invariant feature for tracking, we attach a lightweight correlative masked decoder which reconstructs the original template and search image from the corresponding masked tokens. The correlative masked decoder serves as a plugin for the compact transform tracker and is skipped in inference. Our compact tracker uses the most simple structure which only consists of a ViT backbone and a box head, and can run at 40 fps. Extensive experiments show the proposed compact transform tracker outperforms existing approaches, including advanced attention variants, and demonstrates the sufficiency of self-attention in tracking tasks. Our method achieves state-of-the-art performance on five challenging datasets, along with the VOT2020, UAV123, LaSOT, TrackingNet, and GOT-10k benchmarks. Our project is available at https://github.com/HUSTDML/CTTrack. | ['Wei Yang', 'Yi-Ping Phoebe Chen', 'Junqing Yu', 'Run Luo', 'Zikai Song'] | 2023-01-26 | null | null | null | null | ['visual-object-tracking'] | ['computer-vision'] | [-1.81020141e-01 -1.78241864e-01 -3.14739883e-01 9.31216218e-03
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a1801c19-ba2a-4653-a948-4f93b21e1a17 | analyzing-the-impact-of-sars-cov-2-variants | 2206.12309 | null | https://arxiv.org/abs/2206.12309v1 | https://arxiv.org/pdf/2206.12309v1.pdf | Analyzing the impact of SARS-CoV-2 variants on respiratory sound signals | The COVID-19 outbreak resulted in multiple waves of infections that have been associated with different SARS-CoV-2 variants. Studies have reported differential impact of the variants on respiratory health of patients. We explore whether acoustic signals, collected from COVID-19 subjects, show computationally distinguishable acoustic patterns suggesting a possibility to predict the underlying virus variant. We analyze the Coswara dataset which is collected from three subject pools, namely, i) healthy, ii) COVID-19 subjects recorded during the delta variant dominant period, and iii) data from COVID-19 subjects recorded during the omicron surge. Our findings suggest that multiple sound categories, such as cough, breathing, and speech, indicate significant acoustic feature differences when comparing COVID-19 subjects with omicron and delta variants. The classification areas-under-the-curve are significantly above chance for differentiating subjects infected by omicron from those infected by delta. Using a score fusion from multiple sound categories, we obtained an area-under-the-curve of 89% and 52.4% sensitivity at 95% specificity. Additionally, a hierarchical three class approach was used to classify the acoustic data into healthy and COVID-19 positive, and further COVID-19 subjects into delta and omicron variants providing high level of 3-class classification accuracy. These results suggest new ways for designing sound based COVID-19 diagnosis approaches. | ['Murali Alagesan', 'Sadhana Gonuguntla', 'Suhail K K', 'Sahiti Nori', 'Chandrakiran C', 'Sriram Ganapathy', 'Pravin Mote', 'Srikanth Raj Chetupalli', 'Neeraj Kumar Sharma', 'Debottam Dutta', 'Debarpan Bhattacharya'] | 2022-06-24 | null | null | null | null | ['covid-19-detection'] | ['medical'] | [ 2.16130927e-01 -6.65273547e-01 4.70822513e-01 9.65962037e-02
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907d21cb-dec1-4452-9a75-f09b3fa84db9 | first-steps-towards-the-semi-automatic | null | null | https://aclanthology.org/L12-1037 | https://aclanthology.org/L12-1037.pdf | First Steps towards the Semi-automatic Development of a Wordformation-based Lexicon of Latin | Although lexicography of Latin has a long tradition dating back to ancient grammarians, and almost all Latin grammars devote to wordformation at least one part of the section(s) concerning morphology, none of the today available lexical resources and NLP tools of Latin feature a wordformation-based organization of the Latin lexicon. In this paper, we describe the first steps towards the semi-automatic development of a wordformation-based lexicon of Latin, by detailing several problems occurring while building the lexicon and presenting our solutions. Developing a wordformation-based lexicon of Latin is nowadays of outmost importance, as the last years have seen a large growth of annotated corpora of Latin texts of different eras. While these corpora include lemmatization, morphological tagging and syntactic analysis, none of them features segmentation of the word forms and wordformation relations between the lexemes. This restricts the browsing and the exploitation of the annotated data for linguistic research and NLP tasks, such as information retrieval and heuristics in PoS tagging of unknown words. | ['Marco Passarotti', 'Francesco Mambrini'] | 2012-05-01 | null | null | null | lrec-2012-5 | ['morphological-tagging'] | ['natural-language-processing'] | [ 1.25790192e-02 2.40773678e-01 -3.35922569e-01 -2.85028249e-01
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8c29ff3c-5b8f-4c5c-91a4-cd40fa4e211c | buffet-benchmarking-large-language-models-for | 2305.14857 | null | https://arxiv.org/abs/2305.14857v1 | https://arxiv.org/pdf/2305.14857v1.pdf | BUFFET: Benchmarking Large Language Models for Few-shot Cross-lingual Transfer | Despite remarkable advancements in few-shot generalization in natural language processing, most models are developed and evaluated primarily in English. To facilitate research on few-shot cross-lingual transfer, we introduce a new benchmark, called BUFFET, which unifies 15 diverse tasks across 54 languages in a sequence-to-sequence format and provides a fixed set of few-shot examples and instructions. BUFFET is designed to establish a rigorous and equitable evaluation framework for few-shot cross-lingual transfer across a broad range of tasks and languages. Using BUFFET, we perform thorough evaluations of state-of-the-art multilingual large language models with different transfer methods, namely in-context learning and fine-tuning. Our findings reveal significant room for improvement in few-shot in-context cross-lingual transfer. In particular, ChatGPT with in-context learning often performs worse than much smaller mT5-base models fine-tuned on English task data and few-shot in-language examples. Our analysis suggests various avenues for future research in few-shot cross-lingual transfer, such as improved pretraining, understanding, and future evaluations. | ['Hannaneh Hajishirzi', 'Sebastian Ruder', 'Yulia Tsvetkov', 'Machel Reid', 'Hila Gonen', 'Terra Blevins', 'Xinyan Velocity Yu', 'Sneha Kudugunta', 'Akari Asai'] | 2023-05-24 | null | null | null | null | ['cross-lingual-transfer'] | ['natural-language-processing'] | [ 3.64736468e-02 -3.97214651e-01 -5.50868928e-01 -7.09003985e-01
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03c4c9cc-4c9c-46e2-a55b-08c8aa42c1a0 | computationally-efficient-approach-for | 2211.1125 | null | https://arxiv.org/abs/2211.11250v1 | https://arxiv.org/pdf/2211.11250v1.pdf | Computationally Efficient Approach for Preheating of Battery Electric Vehicles before Fast Charging in Cold Climates | This paper investigates battery preheating before fast charging, for a battery electric vehicle (BEV) driving in a cold climate. To prevent the battery from performance degradation at low temperatures, a thermal management (TM) system has been considered, including a high-voltage coolant heater (HVCH) for the battery and cabin compartment heating. Accordingly, an optimal control problem (OCP) has been formulated in the form of a nonlinear program (NLP), aiming at minimising the total energy consumption of the battery. The main focus here is to develop a computationally efficient approach, mimicking the optimal preheating behavior without a noticeable increase in the total energy consumption. The proposed algorithm is simple enough to be implemented in a low-level electronic control unit of the vehicle, by eliminating the need for solving the full NLP in the cost of only 1Wh increase in the total energy consumption. | ['Jonas Fredriksson', 'Viktor Larsson', 'Nikolce Murgovski', 'Jimmy Forsman', 'Ahad Hamednia'] | 2022-11-21 | null | null | null | null | ['total-energy'] | ['miscellaneous'] | [-1.01594828e-01 3.65538329e-01 -1.09290406e-01 -6.72933161e-02
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7e3bf49f-0a44-4b91-914f-15e09fb79d8e | a-multi-task-model-for-sentiment-aided-stance | 2211.03533 | null | https://arxiv.org/abs/2211.03533v1 | https://arxiv.org/pdf/2211.03533v1.pdf | A Multi-task Model for Sentiment Aided Stance Detection of Climate Change Tweets | Climate change has become one of the biggest challenges of our time. Social media platforms such as Twitter play an important role in raising public awareness and spreading knowledge about the dangers of the current climate crisis. With the increasing number of campaigns and communication about climate change through social media, the information could create more awareness and reach the general public and policy makers. However, these Twitter communications lead to polarization of beliefs, opinion-dominated ideologies, and often a split into two communities of climate change deniers and believers. In this paper, we propose a framework that helps identify denier statements on Twitter and thus classifies the stance of the tweet into one of the two attitudes towards climate change (denier/believer). The sentimental aspects of Twitter data on climate change are deeply rooted in general public attitudes toward climate change. Therefore, our work focuses on learning two closely related tasks: Stance Detection and Sentiment Analysis of climate change tweets. We propose a multi-task framework that performs stance detection (primary task) and sentiment analysis (auxiliary task) simultaneously. The proposed model incorporates the feature-specific and shared-specific attention frameworks to fuse multiple features and learn the generalized features for both tasks. The experimental results show that the proposed framework increases the performance of the primary task, i.e., stance detection by benefiting from the auxiliary task, i.e., sentiment analysis compared to its uni-modal and single-task variants. | ['Wolfgang Nejdl', 'Marco Fisichella', 'Apoorva Upadhyaya'] | 2022-11-07 | null | null | null | null | ['stance-detection'] | ['natural-language-processing'] | [ 1.19965300e-01 -4.21392843e-02 -2.33186558e-01 -5.73806524e-01
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6793900d-03ae-4775-b735-12a51f39e59b | a-preliminary-analysis-on-the-code-generation | 2305.09402 | null | https://arxiv.org/abs/2305.09402v1 | https://arxiv.org/pdf/2305.09402v1.pdf | A Preliminary Analysis on the Code Generation Capabilities of GPT-3.5 and Bard AI Models for Java Functions | This paper evaluates the capability of two state-of-the-art artificial intelligence (AI) models, GPT-3.5 and Bard, in generating Java code given a function description. We sourced the descriptions from CodingBat.com, a popular online platform that provides practice problems to learn programming. We compared the Java code generated by both models based on correctness, verified through the platform's own test cases. The results indicate clear differences in the capabilities of the two models. GPT-3.5 demonstrated superior performance, generating correct code for approximately 90.6% of the function descriptions, whereas Bard produced correct code for 53.1% of the functions. While both models exhibited strengths and weaknesses, these findings suggest potential avenues for the development and refinement of more advanced AI-assisted code generation tools. The study underlines the potential of AI in automating and supporting aspects of software development, although further research is required to fully realize this potential. | ['Marco Ortu', 'Silvia Bartolucci', 'Giuseppe Destefanis'] | 2023-05-16 | null | null | null | null | ['code-generation'] | ['computer-code'] | [-1.36430591e-01 5.66016436e-01 -1.04887895e-01 -3.60417038e-01
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65c239da-0edc-4602-9853-2416811d658e | discovering-hierarchical-achievements-in | 2307.03486 | null | https://arxiv.org/abs/2307.03486v1 | https://arxiv.org/pdf/2307.03486v1.pdf | Discovering Hierarchical Achievements in Reinforcement Learning via Contrastive Learning | Discovering achievements with a hierarchical structure on procedurally generated environments poses a significant challenge. This requires agents to possess a broad range of abilities, including generalization and long-term reasoning. Many prior methods are built upon model-based or hierarchical approaches, with the belief that an explicit module for long-term planning would be beneficial for learning hierarchical achievements. However, these methods require an excessive amount of environment interactions or large model sizes, limiting their practicality. In this work, we identify that proximal policy optimization (PPO), a simple and versatile model-free algorithm, outperforms the prior methods with recent implementation practices. Moreover, we find that the PPO agent can predict the next achievement to be unlocked to some extent, though with low confidence. Based on this observation, we propose a novel contrastive learning method, called achievement distillation, that strengthens the agent's capability to predict the next achievement. Our method exhibits a strong capacity for discovering hierarchical achievements and shows state-of-the-art performance on the challenging Crafter environment using fewer model parameters in a sample-efficient regime. | ['Hyun Oh Song', 'Bumsoo Park', 'Junyoung Yeom', 'Seungyong Moon'] | 2023-07-07 | null | null | null | null | ['contrastive-learning', 'contrastive-learning'] | ['computer-vision', 'methodology'] | [-5.65977395e-02 3.00100833e-01 -3.35149109e-01 5.55469096e-03
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e0b5be9a-2bb2-4026-a73a-a8023461fc8d | a-unified-weight-learning-and-low-rank | 2005.04619 | null | https://arxiv.org/abs/2005.04619v4 | https://arxiv.org/pdf/2005.04619v4.pdf | A Unified Weight Learning and Low-Rank Regression Model for Robust Complex Error Modeling | One of the most important problems in regression-based error model is modeling the complex representation error caused by various corruptions and environment changes in images. For example, in robust face recognition, images are often affected by varying types and levels of corruptions, such as random pixel corruptions, block occlusions, or disguises. However, existing works are not robust enough to solve this problem due to they cannot model the complex corrupted errors very well. In this paper, we address this problem by a unified sparse weight learning and low-rank approximation regression model, which enables the random noises and contiguous occlusions in images to be treated simultaneously. For the random noise, we define a generalized correntropy (GC) function to match the error distribution. For the structured error caused by occlusions or disguises, we propose a GC function based rank approximation to measure the rank of error matrices. Since the proposed objective function is non-convex, an effective iterative optimization algorithm is developed to achieve the optimal weight learning and low-rank approximation. Extensive experimental results on three public face databases show that the proposed model can fit the error distribution and structure very well, thus obtain better recognition accuracies in comparison with the existing methods. | ['Jun Zhou', 'Yongsheng Gao', 'Miaohua Zhang'] | 2020-05-10 | null | null | null | null | ['robust-face-recognition'] | ['computer-vision'] | [ 7.96366781e-02 -7.71582544e-01 1.72319397e-01 -4.12709951e-01
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ca23a848-f2c4-4334-96ab-bbd3981e8713 | joint-alignment-of-multi-task-feature-and | 2209.04112 | null | https://arxiv.org/abs/2209.04112v1 | https://arxiv.org/pdf/2209.04112v1.pdf | Joint Alignment of Multi-Task Feature and Label Spaces for Emotion Cause Pair Extraction | Emotion cause pair extraction (ECPE), as one of the derived subtasks of emotion cause analysis (ECA), shares rich inter-related features with emotion extraction (EE) and cause extraction (CE). Therefore EE and CE are frequently utilized as auxiliary tasks for better feature learning, modeled via multi-task learning (MTL) framework by prior works to achieve state-of-the-art (SoTA) ECPE results. However, existing MTL-based methods either fail to simultaneously model the specific features and the interactive feature in between, or suffer from the inconsistency of label prediction. In this work, we consider addressing the above challenges for improving ECPE by performing two alignment mechanisms with a novel A^2Net model. We first propose a feature-task alignment to explicitly model the specific emotion-&cause-specific features and the shared interactive feature. Besides, an inter-task alignment is implemented, in which the label distance between the ECPE and the combinations of EE&CE are learned to be narrowed for better label consistency. Evaluations of benchmarks show that our methods outperform current best-performing systems on all ECA subtasks. Further analysis proves the importance of our proposed alignment mechanisms for the task. | ['Donghong Ji', 'Fei Li', 'Hao Fei', 'Shengqiong Wu', 'Jingye Li', 'Xiaochuan Shi', 'Shunjie Chen'] | 2022-09-09 | null | https://aclanthology.org/2022.coling-1.606 | https://aclanthology.org/2022.coling-1.606.pdf | coling-2022-10 | ['emotion-cause-pair-extraction'] | ['natural-language-processing'] | [ 1.58673882e-01 -1.47327349e-01 -1.92796186e-01 -6.58228755e-01
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fa3325e6-7e00-495d-803b-4d65946dce48 | semi-supervised-formality-style-transfer-with | 2203.1362 | null | https://arxiv.org/abs/2203.13620v1 | https://arxiv.org/pdf/2203.13620v1.pdf | Semi-Supervised Formality Style Transfer with Consistency Training | Formality style transfer (FST) is a task that involves paraphrasing an informal sentence into a formal one without altering its meaning. To address the data-scarcity problem of existing parallel datasets, previous studies tend to adopt a cycle-reconstruction scheme to utilize additional unlabeled data, where the FST model mainly benefits from target-side unlabeled sentences. In this work, we propose a simple yet effective semi-supervised framework to better utilize source-side unlabeled sentences based on consistency training. Specifically, our approach augments pseudo-parallel data obtained from a source-side informal sentence by enforcing the model to generate similar outputs for its perturbed version. Moreover, we empirically examined the effects of various data perturbation methods and propose effective data filtering strategies to improve our framework. Experimental results on the GYAFC benchmark demonstrate that our approach can achieve state-of-the-art results, even with less than 40% of the parallel data. | ['Naoaki Okazaki', 'An Wang', 'Ao Liu'] | 2022-03-25 | null | https://aclanthology.org/2022.acl-long.321 | https://aclanthology.org/2022.acl-long.321.pdf | acl-2022-5 | ['formality-style-transfer'] | ['natural-language-processing'] | [ 5.66969812e-01 9.59143713e-02 -2.00626910e-01 -5.19202530e-01
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63487603-a11e-452d-baa5-8971ff870bb9 | representing-and-extracting-knowledge-from | 2304.13084 | null | https://arxiv.org/abs/2304.13084v1 | https://arxiv.org/pdf/2304.13084v1.pdf | Representing and extracting knowledge from single cell data | Single-cell analysis is currently one of the most high-resolution techniques to study biology. The large complex datasets that have been generated have spurred numerous developments in computational biology, in particular the use of advanced statistics and machine learning. This review attempts to explain the deeper theoretical concepts that underpin current state-of-the-art analysis methods. Single-cell analysis is covered from cell, through instruments, to current and upcoming models. A minimum of mathematics and statistics has been used, but the reader is assumed to either have basic knowledge of single-cell analysis workflows, or have a solid knowledge of statistics. The aim of this review is to spread concepts which are not yet in common use, especially from topology and generative processes, and how new statistical models can be developed to capture more of biology. This opens epistemological questions regarding our ontology and models, and some pointers will be given to how natural language processing (NLP) may help overcome our cognitive limitations for understanding single-cell data. | ['Johan Henriksson', 'Sarang Chafle', 'Ionut Sebastian Mihai'] | 2023-04-25 | null | null | null | null | ['art-analysis'] | ['computer-vision'] | [ 4.20140177e-01 -1.71884477e-01 -3.94429527e-02 -2.75004357e-02
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aefb8ca5-0625-4a1a-a9f2-abfd76432188 | privacy-preserving-collaborative-learning-1 | 2212.06322 | null | https://arxiv.org/abs/2212.06322v1 | https://arxiv.org/pdf/2212.06322v1.pdf | Privacy-Preserving Collaborative Learning through Feature Extraction | We propose a framework in which multiple entities collaborate to build a machine learning model while preserving privacy of their data. The approach utilizes feature embeddings from shared/per-entity feature extractors transforming data into a feature space for cooperation between entities. We propose two specific methods and compare them with a baseline method. In Shared Feature Extractor (SFE) Learning, the entities use a shared feature extractor to compute feature embeddings of samples. In Locally Trained Feature Extractor (LTFE) Learning, each entity uses a separate feature extractor and models are trained using concatenated features from all entities. As a baseline, in Cooperatively Trained Feature Extractor (CTFE) Learning, the entities train models by sharing raw data. Secure multi-party algorithms are utilized to train models without revealing data or features in plain text. We investigate the trade-offs among SFE, LTFE, and CTFE in regard to performance, privacy leakage (using an off-the-shelf membership inference attack), and computational cost. LTFE provides the most privacy, followed by SFE, and then CTFE. Computational cost is lowest for SFE and the relative speed of CTFE and LTFE depends on network architecture. CTFE and LTFE provide the best accuracy. We use MNIST, a synthetic dataset, and a credit card fraud detection dataset for evaluations. | ['Farshad Khorrami', 'Siddharth Garg', 'Prashanth Krishnamurthy', 'Hao Fu', 'Alireza Sarmadi'] | 2022-12-13 | null | null | null | null | ['inference-attack', 'membership-inference-attack'] | ['adversarial', 'computer-vision'] | [-2.23646253e-01 1.16957434e-01 -6.36511594e-02 -5.59280992e-01
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d71e0acf-77a3-413a-a422-67d25ed7d199 | off-policy-action-anticipation-in-multi-agent | 2304.01447 | null | https://arxiv.org/abs/2304.01447v1 | https://arxiv.org/pdf/2304.01447v1.pdf | Off-Policy Action Anticipation in Multi-Agent Reinforcement Learning | Learning anticipation in Multi-Agent Reinforcement Learning (MARL) is a reasoning paradigm where agents anticipate the learning steps of other agents to improve cooperation among themselves. As MARL uses gradient-based optimization, learning anticipation requires using Higher-Order Gradients (HOG), with so-called HOG methods. Existing HOG methods are based on policy parameter anticipation, i.e., agents anticipate the changes in policy parameters of other agents. Currently, however, these existing HOG methods have only been applied to differentiable games or games with small state spaces. In this work, we demonstrate that in the case of non-differentiable games with large state spaces, existing HOG methods do not perform well and are inefficient due to their inherent limitations related to policy parameter anticipation and multiple sampling stages. To overcome these problems, we propose Off-Policy Action Anticipation (OffPA2), a novel framework that approaches learning anticipation through action anticipation, i.e., agents anticipate the changes in actions of other agents, via off-policy sampling. We theoretically analyze our proposed OffPA2 and employ it to develop multiple HOG methods that are applicable to non-differentiable games with large state spaces. We conduct a large set of experiments and illustrate that our proposed HOG methods outperform the existing ones regarding efficiency and performance. | ['Gijs Dubbelman', 'Pavol Jancura', 'Daan de Geus', 'Ariyan Bighashdel'] | 2023-04-04 | null | null | null | null | ['action-anticipation'] | ['computer-vision'] | [-2.11984843e-01 8.81644487e-02 -1.99289441e-01 2.89551228e-01
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10281804-e59d-4e64-b342-395019e630e6 | multilingual-epidemic-event-extraction-from | null | null | https://aclanthology.org/2021.ranlp-main.138 | https://aclanthology.org/2021.ranlp-main.138.pdf | Multilingual Epidemic Event Extraction : From Simple Classification Methods to Open Information Extraction (OIE) and Ontology | There is an incredible amount of information available in the form of textual documents due to the growth of information sources. In order to get the information into an actionable way, it is common to use information extraction and more specifically the event extraction, it became crucial in various domains even in public health. In this paper, we address the problem of the epidemic event extraction in potentially any language, so that we tested different corpuses on an existed multilingual system for tele-epidemiology: the Data Analysis for Information Extraction in any Language(DANIEL) system. We focused on the influence of the number of documents on the performance of the system, on average results show that it is able to achieve a precision and recall around 82%, but when we resorted to the evaluation by event by checking whether it has been really detected or not, the results are not satisfactory according to this paper’s evaluation. Our idea is to propose a system that uses an ontology which includes information in different languages and covers specific epidemiological concepts, it is also based on the multilingual open information extraction for the relation extraction step to reduce the expert intervention and to restrict the content for each text. We describe a methodology of five main stages: Pre-processing, relation extraction, named entity recognition (NER), event recognition and the matching between the information extracted and the ontology. | ['Gaël Lejeune', 'Sihem Sahnoun'] | null | null | https://aclanthology.org/2021.ranlp-1.138 | https://aclanthology.org/2021.ranlp-1.138.pdf | ranlp-2021-9 | ['epidemiology', 'open-information-extraction'] | ['medical', 'natural-language-processing'] | [ 1.48518430e-02 4.50977325e-01 1.96340576e-01 5.09107262e-02
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7632c079-f6f0-495b-8d9a-6f6dec48a140 | anomalybert-self-supervised-transformer-for | 2305.04468 | null | https://arxiv.org/abs/2305.04468v1 | https://arxiv.org/pdf/2305.04468v1.pdf | AnomalyBERT: Self-Supervised Transformer for Time Series Anomaly Detection using Data Degradation Scheme | Mechanical defects in real situations affect observation values and cause abnormalities in multivariate time series, such as sensor values or network data. To perceive abnormalities in such data, it is crucial to understand the temporal context and interrelation between variables simultaneously. The anomaly detection task for time series, especially for unlabeled data, has been a challenging problem, and we address it by applying a suitable data degradation scheme to self-supervised model training. We define four types of synthetic outliers and propose the degradation scheme in which a portion of input data is replaced with one of the synthetic outliers. Inspired by the self-attention mechanism, we design a Transformer-based architecture to recognize the temporal context and detect unnatural sequences with high efficiency. Our model converts multivariate data points into temporal representations with relative position bias and yields anomaly scores from these representations. Our method, AnomalyBERT, shows a great capability of detecting anomalies contained in complex time series and surpasses previous state-of-the-art methods on five real-world benchmarks. Our code is available at https://github.com/Jhryu30/AnomalyBERT. | ['Myungjoo Kang', 'Imseong Park', 'Jung Hyun Ryu', 'Eunseok Yang', 'Yungi Jeong'] | 2023-05-08 | null | null | null | null | ['time-series-anomaly-detection'] | ['time-series'] | [ 2.67013371e-01 -3.49620074e-01 2.89508492e-01 -3.85782182e-01
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63003f97-a233-4c61-a996-117e73d93bf7 | depth-sims-semi-parametric-image-and-depth | 2203.03405 | null | https://arxiv.org/abs/2203.03405v2 | https://arxiv.org/pdf/2203.03405v2.pdf | Depth-SIMS: Semi-Parametric Image and Depth Synthesis | In this paper we present a compositing image synthesis method that generates RGB canvases with well aligned segmentation maps and sparse depth maps, coupled with an in-painting network that transforms the RGB canvases into high quality RGB images and the sparse depth maps into pixel-wise dense depth maps. We benchmark our method in terms of structural alignment and image quality, showing an increase in mIoU over SOTA by 3.7 percentage points and a highly competitive FID. Furthermore, we analyse the quality of the generated data as training data for semantic segmentation and depth completion, and show that our approach is more suited for this purpose than other methods. | ['Paul Newman', 'Matthew Gadd', 'Daniele De Martini', 'Valentina Musat'] | 2022-03-07 | null | null | null | null | ['depth-completion'] | ['computer-vision'] | [ 7.56124854e-01 4.20581311e-01 2.47605070e-01 -2.45000392e-01
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ac689baf-d199-4c58-a028-4872e3694778 | fast-rotation-search-with-stereographic | null | null | http://openaccess.thecvf.com/content_cvpr_2014/html/Bustos_Fast_Rotation_Search_2014_CVPR_paper.html | http://openaccess.thecvf.com/content_cvpr_2014/papers/Bustos_Fast_Rotation_Search_2014_CVPR_paper.pdf | Fast Rotation Search with Stereographic Projections for 3D Registration | Recently there has been a surge of interest to use branch-and-bound (bnb) optimisation for 3D point cloud registration. While bnb guarantees globally optimal solutions, it is usually too slow to be practical. A fundamental source of difficulty is the search for the rotation parameters in the 3D rigid transform. In this work, assuming that the translation parameters are known, we focus on constructing a fast rotation search algorithm. With respect to an inherently robust geometric matching criterion, we propose a novel bounding function for bnb that allows rapid evaluation. Underpinning our bounding function is the usage of stereographic projections to precompute and spatially index all possible point matches. This yields a robust and global algorithm that is significantly faster than previous methods. To conduct full 3D registration, the translation can be supplied by 3D feature matching, or by another optimisation framework that provides the translation. On various challenging point clouds, including those taken out of lab settings, our approach demonstrates superior efficiency. | ['Tat-Jun Chin', 'David Suter', 'Alvaro Parra Bustos'] | 2014-06-01 | null | null | null | cvpr-2014-6 | ['3d-feature-matching', 'geometric-matching'] | ['computer-vision', 'computer-vision'] | [ 1.39525130e-01 -4.33825284e-01 9.20242742e-02 -1.13267362e-01
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8621f14e-6fae-4ce1-8200-128f957e3bd4 | image-augmentation-for-satellite-images | 2207.1458 | null | https://arxiv.org/abs/2207.14580v1 | https://arxiv.org/pdf/2207.14580v1.pdf | Image Augmentation for Satellite Images | This study proposes the use of generative models (GANs) for augmenting the EuroSAT dataset for the Land Use and Land Cover (LULC) Classification task. We used DCGAN and WGAN-GP to generate images for each class in the dataset. We then explored the effect of augmenting the original dataset by about 10% in each case on model performance. The choice of GAN architecture seems to have no apparent effect on the model performance. However, a combination of geometric augmentation and GAN-generated images improved baseline results. Our study shows that GANs augmentation can improve the generalizability of deep classification models on satellite images. | ['Olayiwola Arowolo', 'Opeyemi Ajayi', 'Peter Owoade', 'Oluwadara Adedeji'] | 2022-07-29 | null | null | null | null | ['image-augmentation'] | ['computer-vision'] | [ 3.23449433e-01 5.79787433e-01 3.71304899e-02 -4.02989447e-01
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c21fe5e8-6d90-44c1-8477-21a94a32a4bc | ered-enhanced-text-representations-with | 2208.08954 | null | https://arxiv.org/abs/2208.08954v1 | https://arxiv.org/pdf/2208.08954v1.pdf | Ered: Enhanced Text Representations with Entities and Descriptions | External knowledge,e.g., entities and entity descriptions, can help humans understand texts. Many works have been explored to include external knowledge in the pre-trained models. These methods, generally, design pre-training tasks and implicitly introduce knowledge by updating model weights, alternatively, use it straightforwardly together with the original text. Though effective, there are some limitations. On the one hand, it is implicit and only model weights are paid attention to, the pre-trained entity embeddings are ignored. On the other hand, entity descriptions may be lengthy, and inputting into the model together with the original text may distract the model's attention. This paper aims to explicitly include both entities and entity descriptions in the fine-tuning stage. First, the pre-trained entity embeddings are fused with the original text representation and updated by the backbone model layer by layer. Second, descriptions are represented by the knowledge module outside the backbone model, and each knowledge layer is selectively connected to one backbone layer for fusing. Third, two knowledge-related auxiliary tasks, i.e., entity/description enhancement and entity enhancement/pollution task, are designed to smooth the semantic gaps among evolved representations. We conducted experiments on four knowledge-oriented tasks and two common tasks, and the results achieved new state-of-the-art on several datasets. Besides, we conduct an ablation study to show that each module in our method is necessary. The code is available at https://github.com/lshowway/Ered. | ['Yuxuan Lei', 'Shuai Ma', 'Qinghua Zhao'] | 2022-08-18 | null | null | null | null | ['entity-embeddings'] | ['methodology'] | [-4.06073369e-02 3.49490106e-01 -2.36627415e-01 -3.82467359e-01
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d6deb77e-c920-4f0b-9beb-7dedc68ec23d | attention-neural-model-for-temporal-relation | null | null | https://aclanthology.org/W19-1917 | https://aclanthology.org/W19-1917.pdf | Attention Neural Model for Temporal Relation Extraction | Neural network models have shown promise in the temporal relation extraction task. In this paper, we present the attention based neural network model to extract the containment relations within sentences from clinical narratives. The attention mechanism used on top of GRU model outperforms the existing state-of-the-art neural network models on THYME corpus in intra-sentence temporal relation extraction. | ['Li-Wei Wang', 'Vipin Chaudhary', 'Sijia Liu', 'Hongfang Liu'] | 2019-06-01 | null | null | null | ws-2019-6 | ['temporal-relation-extraction'] | ['natural-language-processing'] | [ 1.79401264e-01 9.11918104e-01 -5.05118310e-01 -3.24309111e-01
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20d24555-d9c8-4198-897a-5d06d04bdb69 | assessment-framework-for-deepfake-detection | 2304.06125 | null | https://arxiv.org/abs/2304.06125v1 | https://arxiv.org/pdf/2304.06125v1.pdf | Assessment Framework for Deepfake Detection in Real-world Situations | Detecting digital face manipulation in images and video has attracted extensive attention due to the potential risk to public trust. To counteract the malicious usage of such techniques, deep learning-based deepfake detection methods have been employed and have exhibited remarkable performance. However, the performance of such detectors is often assessed on related benchmarks that hardly reflect real-world situations. For example, the impact of various image and video processing operations and typical workflow distortions on detection accuracy has not been systematically measured. In this paper, a more reliable assessment framework is proposed to evaluate the performance of learning-based deepfake detectors in more realistic settings. To the best of our acknowledgment, it is the first systematic assessment approach for deepfake detectors that not only reports the general performance under real-world conditions but also quantitatively measures their robustness toward different processing operations. To demonstrate the effectiveness and usage of the framework, extensive experiments and detailed analysis of three popular deepfake detection methods are further presented in this paper. In addition, a stochastic degradation-based data augmentation method driven by realistic processing operations is designed, which significantly improves the robustness of deepfake detectors. | ['Touradj Ebrahimi', 'Yuhang Lu'] | 2023-04-12 | null | null | null | null | ['face-swapping'] | ['computer-vision'] | [-2.99717616e-02 -2.56566882e-01 -2.47167535e-02 -2.03544319e-01
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3b511172-4ed2-42b5-b162-fec2ee3d3ef5 | a-comparative-study-of-graph-matching | 2207.00291 | null | https://arxiv.org/abs/2207.00291v2 | https://arxiv.org/pdf/2207.00291v2.pdf | A Comparative Study of Graph Matching Algorithms in Computer Vision | The graph matching optimization problem is an essential component for many tasks in computer vision, such as bringing two deformable objects in correspondence. Naturally, a wide range of applicable algorithms have been proposed in the last decades. Since a common standard benchmark has not been developed, their performance claims are often hard to verify as evaluation on differing problem instances and criteria make the results incomparable. To address these shortcomings, we present a comparative study of graph matching algorithms. We create a uniform benchmark where we collect and categorize a large set of existing and publicly available computer vision graph matching problems in a common format. At the same time we collect and categorize the most popular open-source implementations of graph matching algorithms. Their performance is evaluated in a way that is in line with the best practices for comparing optimization algorithms. The study is designed to be reproducible and extensible to serve as a valuable resource in the future. Our study provides three notable insights: 1.) popular problem instances are exactly solvable in substantially less than 1 second and, therefore, are insufficient for future empirical evaluations; 2.) the most popular baseline methods are highly inferior to the best available methods; 3.) despite the NP-hardness of the problem, instances coming from vision applications are often solvable in a few seconds even for graphs with more than 500 vertices. | ['Bogdan Savchynskyy', 'Paul Swoboda', 'Dagmar Kainmüller', 'Carsten Rother', 'Florian Bernard', 'Lisa Hutschenreiter', 'Lorenz Feineis', 'Stefan Haller'] | 2022-07-01 | null | null | null | null | ['graph-matching'] | ['graphs'] | [ 3.62915426e-01 1.22974426e-01 -2.06432909e-01 -2.20166773e-01
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e4b177f8-2f96-4ed4-9d4a-a0b1479b2ecd | level-line-guided-edge-drawing-for-robust | 2305.05883 | null | https://arxiv.org/abs/2305.05883v1 | https://arxiv.org/pdf/2305.05883v1.pdf | Level-line Guided Edge Drawing for Robust Line Segment Detection | Line segment detection plays a cornerstone role in computer vision tasks. Among numerous detection methods that have been recently proposed, the ones based on edge drawing attract increasing attention owing to their excellent detection efficiency. However, the existing methods are not robust enough due to the inadequate usage of image gradients for edge drawing and line segment fitting. Based on the observation that the line segments should locate on the edge points with both consistent coordinates and level-line information, i.e., the unit vector perpendicular to the gradient orientation, this paper proposes a level-line guided edge drawing for robust line segment detection (GEDRLSD). The level-line information provides potential directions for edge tracking, which could be served as a guideline for accurate edge drawing. Additionally, the level-line information is fused in line segment fitting to improve the robustness. Numerical experiments show the superiority of the proposed GEDRLSD algorithm compared with state-of-the-art methods. | ['Ce Zhu', 'Yipeng Liu', 'Yingjie Zhou', 'Xinyu Lin'] | 2023-05-10 | null | null | null | null | ['line-segment-detection'] | ['computer-vision'] | [-1.09879106e-01 -5.45320213e-01 -1.41100436e-01 2.59679668e-02
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a250fd39-361b-4a5b-9dea-79dabd2c89bd | grammatical-error-detection-in-transcriptions | null | null | https://aclanthology.org/2020.coling-main.195 | https://aclanthology.org/2020.coling-main.195.pdf | Grammatical error detection in transcriptions of spoken English | We describe the collection of transcription corrections and grammatical error annotations for the CrowdED Corpus of spoken English monologues on business topics. The corpus recordings were crowdsourced from native speakers of English and learners of English with German as their first language. The new transcriptions and annotations are obtained from different crowdworkers: we analyse the 1108 new crowdworker submissions and propose that they can be used for automatic transcription post-editing and grammatical error correction for speech. To further explore the data we train grammatical error detection models with various configurations including pre-trained and contextual word representations as input, additional features and auxiliary objectives, and extra training data from written error-annotated corpora. We find that a model concatenating pre-trained and contextual word representations as input performs best, and that additional information does not lead to further performance gains. | ['Paula Buttery', 'Marek Rei', 'Kate Knill', 'Christian Bentz', 'Andrew Caines'] | 2020-12-01 | null | null | null | coling-2020-8 | ['grammatical-error-detection'] | ['natural-language-processing'] | [ 5.19240350e-02 5.44434428e-01 1.42080680e-01 -7.60942757e-01
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5cf344cb-4107-47c0-8e0d-ddc613802165 | joint-cluster-head-selection-and-trajectory | 2112.00333 | null | https://arxiv.org/abs/2112.00333v1 | https://arxiv.org/pdf/2112.00333v1.pdf | Joint Cluster Head Selection and Trajectory Planning in UAV-Aided IoT Networks by Reinforcement Learning with Sequential Model | Employing unmanned aerial vehicles (UAVs) has attracted growing interests and emerged as the state-of-the-art technology for data collection in Internet-of-Things (IoT) networks. In this paper, with the objective of minimizing the total energy consumption of the UAV-IoT system, we formulate the problem of jointly designing the UAV's trajectory and selecting cluster heads in the IoT network as a constrained combinatorial optimization problem which is classified as NP-hard and challenging to solve. We propose a novel deep reinforcement learning (DRL) with a sequential model strategy that can effectively learn the policy represented by a sequence-to-sequence neural network for the UAV's trajectory design in an unsupervised manner. Through extensive simulations, the obtained results show that the proposed DRL method can find the UAV's trajectory that requires much less energy consumption when compared to other baseline algorithms and achieves close-to-optimal performance. In addition, simulation results show that the trained model by our proposed DRL algorithm has an excellent generalization ability to larger problem sizes without the need to retrain the model. | ['Jerome Henry', 'Robert Barton', 'Ha H. Nguyen', 'Ebrahim Bedeer', 'Botao Zhu'] | 2021-12-01 | null | null | null | null | ['trajectory-planning'] | ['robots'] | [-1.41803712e-01 -1.65860161e-01 -2.07688734e-01 -2.09736526e-01
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7cdb2743-0a40-4abf-9411-9a00fb9a5d9f | manet-improving-video-denoising-with-a-multi | 2202.09704 | null | https://arxiv.org/abs/2202.09704v2 | https://arxiv.org/pdf/2202.09704v2.pdf | MANet: Improving Video Denoising with a Multi-Alignment Network | In video denoising, the adjacent frames often provide very useful information, but accurate alignment is needed before such information can be harnassed. In this work, we present a multi-alignment network, which generates multiple flow proposals followed by attention-based averaging. It serves to mimic the non-local mechanism, suppressing noise by averaging multiple observations. Our approach can be applied to various state-of-the-art models that are based on flow estimation. Experiments on a large-scale video dataset demonstrate that our method improves the denoising baseline model by 0.2dB, and further reduces the parameters by 47% with model distillation. Code is available at https://github.com/IndigoPurple/MANet. | ['Edmund Y. Lam', 'Jiebo Luo', 'Zhongrui Wang', 'Haitian Zheng', 'Yaping Zhao'] | 2022-02-20 | null | null | null | null | ['video-denoising'] | ['computer-vision'] | [ 8.38687047e-02 -2.40343004e-01 -1.83027625e-01 -2.54972756e-01
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750a0cb9-e1ca-4a85-a364-b838a34c1faf | blazepose-on-device-real-time-body-pose | 2006.10204 | null | https://arxiv.org/abs/2006.10204v1 | https://arxiv.org/pdf/2006.10204v1.pdf | BlazePose: On-device Real-time Body Pose tracking | We present BlazePose, a lightweight convolutional neural network architecture for human pose estimation that is tailored for real-time inference on mobile devices. During inference, the network produces 33 body keypoints for a single person and runs at over 30 frames per second on a Pixel 2 phone. This makes it particularly suited to real-time use cases like fitness tracking and sign language recognition. Our main contributions include a novel body pose tracking solution and a lightweight body pose estimation neural network that uses both heatmaps and regression to keypoint coordinates. | ['Valentin Bazarevsky', 'Matthias Grundmann', 'Tyler Zhu', 'Karthik Raveendran', 'Ivan Grishchenko', 'Fan Zhang'] | 2020-06-17 | null | null | null | null | ['2d-human-pose-estimation'] | ['computer-vision'] | [ 2.02119008e-01 2.60831147e-01 -3.95372838e-01 -1.97887912e-01
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50eb9b31-4e92-4dd5-a6e3-abf488af3231 | boun-tabi-smm4h22-text-to-text-adverse-drug | null | null | https://aclanthology.org/2022.smm4h-1.9 | https://aclanthology.org/2022.smm4h-1.9.pdf | BOUN-TABI@SMM4H’22: Text-to-Text Adverse Drug Event Extraction with Data Balancing and Prompting | This paper describes models developed for the Social Media Mining for Health 2022 Shared Task. We participated in two subtasks: classification of English tweets reporting adverse drug events (ADE) (Task 1a) and extraction of ADE spans in such tweets (Task 1b). We developed two separate systems based on the T5 model, viewing these tasks as sequence-to-sequence problems. To address the class imbalance, we made use of data balancing via over- and undersampling on both tasks. For the ADE extraction task, we explored prompting to further benefit from the T5 model and its formulation. Additionally, we built an ensemble model, utilizing both balanced and prompted models. The proposed models outperformed the current state-of-the-art, with an F1 score of 0.655 on ADE classification and a Partial F1 score of 0.527 on ADE extraction. | ['Zeynep Yirmibeşoğlu', 'Gökçe Uludoğan'] | null | null | null | null | smm4h-coling-2022-10 | ['event-extraction'] | ['natural-language-processing'] | [ 4.61098880e-01 3.16424787e-01 -4.02881712e-01 -5.30894756e-01
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0c43271a-77f7-4a4d-8568-4138d626d066 | attention-based-second-order-pooling-network | null | null | https://ieeexplore.ieee.org/document/9325094 | https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=9325094 | Attention-Based Second-Order Pooling Network for Hyperspectral Image Classification | Deep learning (DL) has exhibited huge potentials for hyperspectral image (HSI) classification due to its powerful nonlinear modeling and end-to-end optimization characteristics. Although the superior performance of DL-based methods has
been witnessed, some limitations can still be found. On the one hand, existing DL frameworks usually resorted to first-order
statistical features, whereas they rarely considered second-order or higher-order statistical features. On the other hand, the
optimization of complex hyperparameters (e.g., the layer number and convolutional kernel size) is time-consuming and a very tough task, making the designed DL framework unexplainable. To overcome these challenges, we propose a novel attention-based second-order pooling network (A-SPN). First, a first-order feature operator is designed to model the spectral–spatial information of HSI. Second, an attention-based second-order pooling (A-SOP) operator is designed to model discriminative and representative features. Finally, a fully connected layer with softmax loss is used for classification. The proposed framework can obtain second-order statistical features in an end-to-end manner. In addition, A-SPN is free of complex hyperparameters tuning, making it more explainable and easily equipped for classification tasks. Experimental results based on three common hyperspectral data sets demonstrate that A-SPN outperforms other traditional and state-of-the-art DL-based HSI classification methods in terms of generalization performance with limited training samples, classification accuracy, convergence rate, and computational complexity. | ['Peijun Du', 'Yifeng Liu', 'Mengxue Zhang', 'Zhaohui Xue'] | 2021-01-14 | null | null | null | ieee-transactions-on-geoscience-and-remote-5 | ['remote-sensing-image-classification'] | ['miscellaneous'] | [ 0.23371309 -0.4821272 -0.11973314 -0.45752448 -0.61448485 -0.04842366
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cdd74422-bff8-470a-9cb4-559653a0d197 | fuzzy-finite-element-model-updating-using | 1701.00833 | null | http://arxiv.org/abs/1701.00833v1 | http://arxiv.org/pdf/1701.00833v1.pdf | Fuzzy finite element model updating using metaheuristic optimization algorithms | In this paper, a non-probabilistic method based on fuzzy logic is used to
update finite element models (FEMs). Model updating techniques use the measured
data to improve the accuracy of numerical models of structures. However, the
measured data are contaminated with experimental noise and the models are
inaccurate due to randomness in the parameters. This kind of aleatory
uncertainty is irreducible, and may decrease the accuracy of the finite element
model updating process. However, uncertainty quantification methods can be used
to identify the uncertainty in the updating parameters. In this paper, the
uncertainties associated with the modal parameters are defined as fuzzy
membership functions, while the model updating procedure is defined as an
optimization problem at each {\alpha}-cut level. To determine the membership
functions of the updated parameters, an objective function is defined and
minimized using two metaheuristic optimization algorithms: ant colony
optimization (ACO) and particle swarm optimization (PSO). A structural example
is used to investigate the accuracy of the fuzzy model updating strategy using
the PSO and ACO algorithms. Furthermore, the results obtained by the fuzzy
finite element model updating are compared with the Bayesian model updating
results. | ['H. Haddad Khodaparast', 'I. Boulkaibet', 'T. Marwala', 'S. Adhikari', 'M. I. Friswell'] | 2017-01-03 | null | null | null | null | ['metaheuristic-optimization'] | ['methodology'] | [-6.17641546e-02 -1.89148217e-01 2.65427709e-01 -1.40285060e-01
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a7af0e25-1266-4fd2-b365-e6c51602b431 | enhancing-neural-mathematical-reasoning-by | 2203.14487 | null | https://arxiv.org/abs/2203.14487v1 | https://arxiv.org/pdf/2203.14487v1.pdf | Enhancing Neural Mathematical Reasoning by Abductive Combination with Symbolic Library | Mathematical reasoning recently has been shown as a hard challenge for neural systems. Abilities including expression translation, logical reasoning, and mathematics knowledge acquiring appear to be essential to overcome the challenge. This paper demonstrates that some abilities can be achieved through abductive combination with discrete systems that have been programmed with human knowledge. On a mathematical reasoning dataset, we adopt the recently proposed abductive learning framework, and propose the ABL-Sym algorithm that combines the Transformer neural models with a symbolic mathematics library. ABL-Sym shows 9.73% accuracy improvement on the interpolation tasks and 47.22% accuracy improvement on the extrapolation tasks, over the state-of-the-art approaches. Online demonstration: http://math.polixir.ai | ['Yang Yu', 'Yangyang Hu'] | 2022-03-28 | null | null | null | null | ['mathematical-reasoning'] | ['natural-language-processing'] | [-8.32125098e-02 5.67509413e-01 -9.14101079e-02 -3.25478733e-01
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43d97432-b46c-4d1f-aca8-938a02e1d0b6 | a-novel-explainable-artificial-intelligence | 2307.04137 | null | https://arxiv.org/abs/2307.04137v1 | https://arxiv.org/pdf/2307.04137v1.pdf | A Novel Explainable Artificial Intelligence Model in Image Classification problem | In recent years, artificial intelligence is increasingly being applied widely in many different fields and has a profound and direct impact on human life. Following this is the need to understand the principles of the model making predictions. Since most of the current high-precision models are black boxes, neither the AI scientist nor the end-user deeply understands what's going on inside these models. Therefore, many algorithms are studied for the purpose of explaining AI models, especially those in the problem of image classification in the field of computer vision such as LIME, CAM, GradCAM. However, these algorithms still have limitations such as LIME's long execution time and CAM's confusing interpretation of concreteness and clarity. Therefore, in this paper, we propose a new method called Segmentation - Class Activation Mapping (SeCAM) that combines the advantages of these algorithms above, while at the same time overcoming their disadvantages. We tested this algorithm with various models, including ResNet50, Inception-v3, VGG16 from ImageNet Large Scale Visual Recognition Challenge (ILSVRC) data set. Outstanding results when the algorithm has met all the requirements for a specific explanation in a remarkably concise time. | ['Xuan Phong Nguyen', 'Vo Thanh Khang Nguyen', 'Truong Thanh Hung Nguyen', 'Quoc Hung Cao'] | 2023-07-09 | null | null | null | null | ['explainable-artificial-intelligence', 'object-recognition'] | ['computer-vision', 'computer-vision'] | [ 2.62737334e-01 3.54732387e-02 -1.44716218e-01 -4.20246840e-01
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925e0abc-f31d-433f-a030-d2281eac1d2e | thematic-recommendations-on-knowledge-graphs | 2105.05733 | null | https://arxiv.org/abs/2105.05733v1 | https://arxiv.org/pdf/2105.05733v1.pdf | Thematic recommendations on knowledge graphs using multilayer networks | We present a framework to generate and evaluate thematic recommendations based on multilayer network representations of knowledge graphs (KGs). In this representation, each layer encodes a different type of relationship in the KG, and directed interlayer couplings connect the same entity in different roles. The relative importance of different types of connections is captured by an intuitive salience matrix that can be estimated from data, tuned to incorporate domain knowledge, address different use cases, or respect business logic. We apply an adaptation of the personalised PageRank algorithm to multilayer models of KGs to generate item-item recommendations. These recommendations reflect the knowledge we hold about the content and are suitable for thematic and/or cold-start recommendation settings. Evaluating thematic recommendations from user data presents unique challenges that we address by developing a method to evaluate recommendations relying on user-item ratings, yet respecting their thematic nature. We also show that the salience matrix can be estimated from user data. We demonstrate the utility of our methods by significantly improving consumption metrics in an AB test where collaborative filtering delivered subpar performance. We also apply our approach to movie recommendation using publicly-available data to ensure the reproducibility of our results. We demonstrate that our approach outperforms existing thematic recommendation methods and is even competitive with collaborative filtering approaches. | ['Till Hoffmann', 'Dimitrios Korkinof', 'Mariano Beguerisse-Díaz'] | 2021-05-12 | null | null | null | null | ['movie-recommendation'] | ['miscellaneous'] | [ 2.22546048e-02 3.82698804e-01 -5.44802010e-01 -4.53331113e-01
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598eb0b3-4a14-46d6-95ce-a8751029b5ab | deep-neural-network-based-i-vector-mapping | 1810.07309 | null | http://arxiv.org/abs/1810.07309v1 | http://arxiv.org/pdf/1810.07309v1.pdf | Deep neural network based i-vector mapping for speaker verification using short utterances | Text-independent speaker recognition using short utterances is a highly
challenging task due to the large variation and content mismatch between short
utterances. I-vector based systems have become the standard in speaker
verification applications, but they are less effective with short utterances.
In this paper, we first compare two state-of-the-art universal background model
training methods for i-vector modeling using full-length and short utterance
evaluation tasks. The two methods are Gaussian mixture model (GMM) based and
deep neural network (DNN) based methods. The results indicate that the
I-vector_DNN system outperforms the I-vector_GMM system under various
durations. However, the performances of both systems degrade significantly as
the duration of the utterances decreases. To address this issue, we propose two
novel nonlinear mapping methods which train DNN models to map the i-vectors
extracted from short utterances to their corresponding long-utterance
i-vectors. The mapped i-vector can restore missing information and reduce the
variance of the original short-utterance i-vectors. The proposed methods both
model the joint representation of short and long utterance i-vectors by using
autoencoder. Experimental results using the NIST SRE 2010 dataset show that
both methods provide significant improvement and result in a max of 28.43%
relative improvement in Equal Error Rates from a baseline system, when using
deep encoder with residual blocks and adding an additional phoneme vector. When
further testing the best-validated models of SRE10 on the Speaker In The Wild
dataset, the methods result in a 23.12% improvement on arbitrary-duration (1-5
s) short-utterance conditions. | ['Ying-Nian Wu', 'Yang Shi', 'Jinxi Guo', 'Abeer Alwan', 'Ning Xu', 'Kaiyuan Xu', 'Kailun Qian'] | 2018-10-16 | null | null | null | null | ['text-independent-speaker-recognition'] | ['speech'] | [ 9.77993533e-02 -3.18804681e-01 -2.62275822e-02 -6.12208545e-01
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a8ad19fb-b573-4999-9414-c287e8b57f34 | entity-enhancement-for-implicit-discourse | null | null | https://aclanthology.org/2021.acl-short.116 | https://aclanthology.org/2021.acl-short.116.pdf | Entity Enhancement for Implicit Discourse Relation Classification in the Biomedical Domain | Implicit discourse relation classification is a challenging task, in particular when the text domain is different from the standard Penn Discourse Treebank (PDTB; Prasad et al., 2008) training corpus domain (Wall Street Journal in 1990s). We here tackle the task of implicit discourse relation classification on the biomedical domain, for which the Biomedical Discourse Relation Bank (BioDRB; Prasad et al., 2011) is available. We show that entity information can be used to improve discourse relational argument representation. In a first step, we show that explicitly marked instances that are content-wise similar to the target relations can be used to achieve good performance in the cross-domain setting using a simple unsupervised voting pipeline. As a further step, we show that with the linked entity information from the first step, a transformer which is augmented with entity-related information (KBERT; Liu et al., 2020) sets the new state of the art performance on the dataset, outperforming the large pre-trained BioBERT (Lee et al., 2020) model by 2{\%} points. | ['Vera Demberg', 'Wei Shi'] | 2021-08-01 | null | null | null | acl-2021-5 | ['implicit-discourse-relation-classification'] | ['natural-language-processing'] | [ 6.37415588e-01 1.28108096e+00 -4.83191282e-01 -2.02463001e-01
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2fa1a317-a467-4df4-9a21-fddcb49c97ec | incorporating-semantic-attention-in-video | null | null | https://aclanthology.org/L18-1477 | https://aclanthology.org/L18-1477.pdf | Incorporating Semantic Attention in Video Description Generation | null | ['Hideki Nakayama', 'Naoaki Okazaki', 'Natsuda Laokulrat'] | 2018-05-01 | incorporating-semantic-attention-in-video-1 | https://aclanthology.org/L18-1477 | https://aclanthology.org/L18-1477.pdf | lrec-2018-5 | ['video-description'] | ['computer-vision'] | [-8.63703638e-02 1.71006292e-01 -6.22772932e-01 -4.08054382e-01
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ec51139f-3d93-4a45-942a-f8797f447b34 | conversational-search-for-learning | 2001.02912 | null | https://arxiv.org/abs/2001.02912v1 | https://arxiv.org/pdf/2001.02912v1.pdf | Conversational Search for Learning Technologies | Conversational search is based on a user-system cooperation with the objective to solve an information-seeking task. In this report, we discuss the implication of such cooperation with the learning perspective from both user and system side. We also focus on the stimulation of learning through a key component of conversational search, namely the multimodality of communication way, and discuss the implication in terms of information retrieval. We end with a research road map describing promising research directions and perspectives. | ['Laure Soulier', 'Sharon Oviatt'] | 2020-01-09 | null | null | null | null | ['conversational-search'] | ['natural-language-processing'] | [-1.77049190e-01 7.72282660e-01 -5.12295067e-01 -2.62035541e-02
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3a53543a-252f-418b-b0a4-b5d6fb392ff3 | improving-adversarial-text-generation-with-n | null | null | https://aclanthology.org/2021.paclic-1.69 | https://aclanthology.org/2021.paclic-1.69.pdf | Improving Adversarial Text Generation with n-Gram Matching | null | ['Massimo Piccardi', 'Shijie Li'] | null | null | null | null | paclic-2021-11 | ['adversarial-text'] | ['adversarial'] | [-8.63703638e-02 1.71006292e-01 -6.22772932e-01 -4.08054382e-01
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8a64470f-7e73-43af-97cd-7c4a4e6124ef | temporal-pattern-attention-for-multivariate | 1809.04206 | null | https://arxiv.org/abs/1809.04206v3 | https://arxiv.org/pdf/1809.04206v3.pdf | Temporal Pattern Attention for Multivariate Time Series Forecasting | Forecasting multivariate time series data, such as prediction of electricity consumption, solar power production, and polyphonic piano pieces, has numerous valuable applications. However, complex and non-linear interdependencies between time steps and series complicate the task. To obtain accurate prediction, it is crucial to model long-term dependency in time series data, which can be achieved to some good extent by recurrent neural network (RNN) with attention mechanism. Typical attention mechanism reviews the information at each previous time step and selects the relevant information to help generate the outputs, but it fails to capture the temporal patterns across multiple time steps. In this paper, we propose to use a set of filters to extract time-invariant temporal patterns, which is similar to transforming time series data into its "frequency domain". Then we proposed a novel attention mechanism to select relevant time series, and use its "frequency domain" information for forecasting. We applied the proposed model on several real-world tasks and achieved state-of-the-art performance in all of them with only one exception. | ['Hung-Yi Lee', 'Fan-Keng Sun', 'Shun-Yao Shih'] | 2018-09-12 | null | null | null | null | ['univariate-time-series-forecasting'] | ['time-series'] | [ 2.45774269e-01 -6.06459975e-01 -7.80494213e-02 -2.90682793e-01
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7b5f28eb-4a0a-443e-b93d-00f954fc872e | authorship-verification-an-approach-based-on | 1607.08885 | null | http://arxiv.org/abs/1607.08885v1 | http://arxiv.org/pdf/1607.08885v1.pdf | Authorship Verification - An Approach based on Random Forest | Authorship attribution, being an important problem in many areas in-cluding
information retrieval, computational linguistics, law and journalism etc., has
been identified as a subject of increasingly research interest in the re-cent
years. In case of Author Identification task in PAN at CLEF 2015, the main
focus was given on cross-genre and cross-topic author verification tasks. We
have used several word-based and style-based features to identify the
dif-ferences between the known and unknown problems of one given set and label
the unknown ones accordingly using a Random Forest based classifier. | ['Promita Maitra', 'Souvick Ghosh', 'Dipankar Das'] | 2016-07-29 | null | null | null | null | ['authorship-verification'] | ['natural-language-processing'] | [-2.58110706e-02 -2.30939120e-01 -2.82021761e-01 -1.25379115e-01
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f9e567af-3040-4bbf-b365-91615bf803ab | one-shot-neural-band-selection-for-spectral | 2305.09236 | null | https://arxiv.org/abs/2305.09236v1 | https://arxiv.org/pdf/2305.09236v1.pdf | One-shot neural band selection for spectral recovery | Band selection has a great impact on the spectral recovery quality. To solve this ill-posed inverse problem, most band selection methods adopt hand-crafted priors or exploit clustering or sparse regularization constraints to find most prominent bands. These methods are either very slow due to the computational cost of repeatedly training with respect to different selection frequencies or different band combinations. Many traditional methods rely on the scene prior and thus are not applicable to other scenarios. In this paper, we present a novel one-shot Neural Band Selection (NBS) framework for spectral recovery. Unlike conventional searching approaches with a discrete search space and a non-differentiable search strategy, our NBS is based on the continuous relaxation of the band selection process, thus allowing efficient band search using gradient descent. To enable the compatibility for se- lecting any number of bands in one-shot, we further exploit the band-wise correlation matrices to progressively suppress similar adjacent bands. Extensive evaluations on the NTIRE 2022 Spectral Reconstruction Challenge demonstrate that our NBS achieves consistent performance gains over competitive baselines when examined with four different spectral recov- ery methods. Our code will be publicly available. | ['Ajin Meng', 'Zhilin Han', 'Liu Liu', 'YiTao Zhang', 'You Song', 'Wenshuai Xu', 'Zhenbo Xu', 'Hai-Miao Hu'] | 2023-05-16 | null | null | null | null | ['spectral-reconstruction'] | ['computer-vision'] | [ 6.44645810e-01 -5.99760532e-01 -4.48802322e-01 -1.05811015e-01
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80800465-5cd8-420c-9512-cd17da8ae0a9 | speaker-independent-neural-formant-synthesis | 2306.01957 | null | https://arxiv.org/abs/2306.01957v1 | https://arxiv.org/pdf/2306.01957v1.pdf | Speaker-independent neural formant synthesis | We describe speaker-independent speech synthesis driven by a small set of phonetically meaningful speech parameters such as formant frequencies. The intention is to leverage deep-learning advances to provide a highly realistic signal generator that includes control affordances required for stimulus creation in the speech sciences. Our approach turns input speech parameters into predicted mel-spectrograms, which are rendered into waveforms by a pre-trained neural vocoder. Experiments with WaveNet and HiFi-GAN confirm that the method achieves our goals of accurate control over speech parameters combined with high perceptual audio quality. We also find that the small set of phonetically relevant speech parameters we use is sufficient to allow for speaker-independent synthesis (a.k.a. universal vocoding). | ['Lauri Juvela', 'Gustav Eje Henter', 'Zofia Malisz', 'Pablo Pérez Zarazaga'] | 2023-06-02 | null | null | null | null | ['speech-synthesis'] | ['speech'] | [ 3.91908437e-01 3.39118361e-01 -8.62864405e-02 -6.76536262e-02
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86f87ac3-8da5-4dc7-bf11-242080ae0885 | bert-for-joint-multichannel-speech | 2010.10892 | null | https://arxiv.org/abs/2010.10892v2 | https://arxiv.org/pdf/2010.10892v2.pdf | BERT for Joint Multichannel Speech Dereverberation with Spatial-aware Tasks | We propose a method for joint multichannel speech dereverberation with two spatial-aware tasks: direction-of-arrival (DOA) estimation and speech separation. The proposed method addresses involved tasks as a sequence to sequence mapping problem, which is general enough for a variety of front-end speech enhancement tasks. The proposed method is inspired by the excellent sequence modeling capability of bidirectional encoder representation from transformers (BERT). Instead of utilizing explicit representations from pretraining in a self-supervised manner, we utilizes transformer encoded hidden representations in a supervised manner. Both multichannel spectral magnitude and spectral phase information of varying length utterances are encoded. Experimental result demonstrates the effectiveness of the proposed method. | ['Yang Jiao'] | 2020-10-21 | null | null | null | null | ['speech-dereverberation'] | ['speech'] | [ 3.84162217e-01 -3.88165236e-01 -2.24149730e-02 -2.38116160e-01
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e047312d-4db5-48de-8c5b-2bd7c2ad55b2 | remix-regret-minimization-for-monotonic-value | 2302.05593 | null | https://arxiv.org/abs/2302.05593v1 | https://arxiv.org/pdf/2302.05593v1.pdf | ReMIX: Regret Minimization for Monotonic Value Function Factorization in Multiagent Reinforcement Learning | Value function factorization methods have become a dominant approach for cooperative multiagent reinforcement learning under a centralized training and decentralized execution paradigm. By factorizing the optimal joint action-value function using a monotonic mixing function of agents' utilities, these algorithms ensure the consistency between joint and local action selections for decentralized decision-making. Nevertheless, the use of monotonic mixing functions also induces representational limitations. Finding the optimal projection of an unrestricted mixing function onto monotonic function classes is still an open problem. To this end, we propose ReMIX, formulating this optimal projection problem for value function factorization as a regret minimization over the projection weights of different state-action values. Such an optimization problem can be relaxed and solved using the Lagrangian multiplier method to obtain the close-form optimal projection weights. By minimizing the resulting policy regret, we can narrow the gap between the optimal and the restricted monotonic mixing functions, thus obtaining an improved monotonic value function factorization. Our experimental results on Predator-Prey and StarCraft Multiagent Challenge environments demonstrate the effectiveness of our method, indicating the better capabilities of handling environments with non-monotonic value functions. | ['Tian Lan', 'Hanhan Zhou', 'Yongsheng Mei'] | 2023-02-11 | null | null | null | null | ['starcraft'] | ['playing-games'] | [-2.73500025e-01 1.29597843e-01 -5.90354502e-01 -8.90354663e-02
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2f4d1c7b-43eb-4ed3-bb30-48d345d63912 | high-speed-human-action-recognition-using-a | 2305.15283 | null | https://arxiv.org/abs/2305.15283v2 | https://arxiv.org/pdf/2305.15283v2.pdf | High Speed Human Action Recognition using a Photonic Reservoir Computer | The recognition of human actions in videos is one of the most active research fields in computer vision. The canonical approach consists in a more or less complex preprocessing stages of the raw video data, followed by a relatively simple classification algorithm. Here we address recognition of human actions using the reservoir computing algorithm, which allows us to focus on the classifier stage. We introduce a new training method for the reservoir computer, based on "Timesteps Of Interest", which combines in a simple way short and long time scales. We study the performance of this algorithm using both numerical simulations and a photonic implementation based on a single non-linear node and a delay line on the well known KTH dataset. We solve the task with high accuracy and speed, to the point of allowing for processing multiple video streams in real time. The present work is thus an important step towards developing efficient dedicated hardware for video processing. | ['Serge Massar', 'Piotr Antonik', 'Enrico Picco'] | 2023-05-24 | null | null | null | null | ['action-recognition-in-videos', 'action-recognition'] | ['computer-vision', 'computer-vision'] | [ 5.91223776e-01 -2.60863185e-01 2.46656626e-01 2.89835203e-02
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b66584b2-cb67-4864-8f9e-0c46f875ed73 | allowing-for-weak-identification-when-testing | 2210.11398 | null | https://arxiv.org/abs/2210.11398v1 | https://arxiv.org/pdf/2210.11398v1.pdf | Allowing for weak identification when testing GARCH-X type models | In this paper, we use the results in Andrews and Cheng (2012), extended to allow for parameters to be near or at the boundary of the parameter space, to derive the asymptotic distributions of the two test statistics that are used in the two-step (testing) procedure proposed by Pedersen and Rahbek (2019). The latter aims at testing the null hypothesis that a GARCH-X type model, with exogenous covariates (X), reduces to a standard GARCH type model, while allowing the "GARCH parameter" to be unidentified. We then provide a characterization result for the asymptotic size of any test for testing this null hypothesis before numerically establishing a lower bound on the asymptotic size of the two-step procedure at the 5% nominal level. This lower bound exceeds the nominal level, revealing that the two-step procedure does not control asymptotic size. In a simulation study, we show that this finding is relevant for finite samples, in that the two-step procedure can suffer from overrejection in finite samples. We also propose a new test that, by construction, controls asymptotic size and is found to be more powerful than the two-step procedure when the "ARCH parameter" is "very small" (in which case the two-step procedure underrejects). | ['Philipp Ketz'] | 2022-10-20 | null | null | null | null | ['type'] | ['speech'] | [-1.36602506e-01 2.84494728e-01 3.75324488e-02 -6.69830218e-02
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6d9a6a2d-9e6c-4dff-9c01-d7ce432f984e | knowledge-distillation-meets-few-shot | null | null | https://aclanthology.org/2022.nlp4convai-1.10 | https://aclanthology.org/2022.nlp4convai-1.10.pdf | Knowledge Distillation Meets Few-Shot Learning: An Approach for Few-Shot Intent Classification Within and Across Domains | Large Transformer-based natural language understanding models have achieved state-of-the-art performance in dialogue systems. However, scarce labeled data for training, the large model size, and low inference speed hinder their deployment in low-resource scenarios. Few-shot learning and knowledge distillation techniques have been introduced to reduce the need for labeled data and computational resources, respectively. However, these techniques are incompatible because few-shot learning trains models using few data, whereas, knowledge distillation requires sufficient data to train smaller, yet competitive models that run on limited computational resources. In this paper, we address the problem of distilling generalizable small models under the few-shot setting for the intent classification task. Considering in-domain and cross-domain few-shot learning scenarios, we introduce an approach for distilling small models that generalize to new intent classes and domains using only a handful of labeled examples. We conduct experiments on public intent classification benchmarks, and observe a slight performance gap between small models and large Transformer-based models. Overall, our results in both few-shot scenarios confirm the generalization ability of the small distilled models while having lower computational costs. | ['Fabian Küch', 'Shima Asaadi', 'Anna Sauer'] | null | null | null | null | nlp4convai-acl-2022-5 | ['cross-domain-few-shot', 'cross-domain-few-shot-learning'] | ['computer-vision', 'computer-vision'] | [ 1.36619415e-02 5.56988120e-01 -5.05745530e-01 -5.75256586e-01
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4fa5a5f7-b4a6-443b-b610-68fd3cbff72f | model-free-context-aware-word-composition | null | null | https://aclanthology.org/C18-1240 | https://aclanthology.org/C18-1240.pdf | Model-Free Context-Aware Word Composition | Word composition is a promising technique for representation learning of large linguistic units (e.g., phrases, sentences and documents). However, most of the current composition models do not take the ambiguity of words and the context outside of a linguistic unit into consideration for learning representations, and consequently suffer from the inaccurate representation of semantics. To address this issue, we propose a model-free context-aware word composition model, which employs the latent semantic information as global context for learning representations. The proposed model attempts to resolve the word sense disambiguation and word composition in a unified framework. Extensive evaluation shows consistent improvements over various strong word representation/composition models at different granularities (including word, phrase and sentence), demonstrating the effectiveness of our proposed method. | ['Xianpei Han', 'Bo An', 'Le Sun'] | 2018-08-01 | model-free-context-aware-word-composition-1 | https://aclanthology.org/C18-1240 | https://aclanthology.org/C18-1240.pdf | coling-2018-8 | ['learning-word-embeddings'] | ['methodology'] | [ 3.90810162e-01 -1.75505713e-01 -6.37515366e-01 -3.10060173e-01
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59d0b139-61f6-4a96-b36b-a12909f700b0 | exploring-metaphoric-paraphrase-generation | null | null | https://aclanthology.org/2021.conll-1.26 | https://aclanthology.org/2021.conll-1.26.pdf | Exploring Metaphoric Paraphrase Generation | Metaphor generation is a difficult task, and has seen tremendous improvement with the advent of deep pretrained models. We focus here on the specific task of metaphoric paraphrase generation, in which we provide a literal sentence and generate a metaphoric sentence which paraphrases that input. We compare naive, “free” generation models with those that exploit forms of control over the generation process, adding additional information based on conceptual metaphor theory. We evaluate two methods for generating paired training data, which is then used to train T5 models for free and controlled generation. We use crowdsourcing to evaluate the results, showing that free models tend to generate more fluent paraphrases, while controlled models are better at generating novel metaphors. We then analyze evaluation metrics, showing that different metrics are necessary to capture different aspects of metaphoric paraphrasing. We release our data and models, as well as our annotated results in order to facilitate development of better evaluation metrics. | ['Iryna Gurevych', 'Nils Beck', 'Kevin Stowe'] | null | null | null | null | conll-emnlp-2021-11 | ['paraphrase-generation', 'paraphrase-generation'] | ['computer-code', 'natural-language-processing'] | [ 1.44307837e-01 1.49951637e-01 1.02027856e-01 -2.95941919e-01
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c04b20d7-d9ad-452f-875d-4dfd0f772450 | resource-efficient-mountainous-skyline | 2107.10997 | null | https://arxiv.org/abs/2107.10997v1 | https://arxiv.org/pdf/2107.10997v1.pdf | Resource Efficient Mountainous Skyline Extraction using Shallow Learning | Skyline plays a pivotal role in mountainous visual geo-localization and localization/navigation of planetary rovers/UAVs and virtual/augmented reality applications. We present a novel mountainous skyline detection approach where we adapt a shallow learning approach to learn a set of filters to discriminate between edges belonging to sky-mountain boundary and others coming from different regions. Unlike earlier approaches, which either rely on extraction of explicit feature descriptors and their classification, or fine-tuning general scene parsing deep networks for sky segmentation, our approach learns linear filters based on local structure analysis. At test time, for every candidate edge pixel, a single filter is chosen from the set of learned filters based on pixel's structure tensor, and then applied to the patch around it. We then employ dynamic programming to solve the shortest path problem for the resultant multistage graph to get the sky-mountain boundary. The proposed approach is computationally faster than earlier methods while providing comparable performance and is more suitable for resource constrained platforms e.g., mobile devices, planetary rovers and UAVs. We compare our proposed approach against earlier skyline detection methods using four different data sets. Our code is available at \url{https://github.com/TouqeerAhmad/skyline_detection}. | ['George Bebis', 'Martin Čadík', 'Ebrahim Emami', 'Touqeer Ahmad'] | 2021-07-23 | null | null | null | null | ['scene-parsing'] | ['computer-vision'] | [-3.13104615e-02 -2.73109853e-01 -1.80252641e-01 -3.39632064e-01
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ad7b80db-82d0-4a4d-a095-2445373bd3d1 | auxiliary-learning-by-implicit | 2007.02693 | null | https://arxiv.org/abs/2007.02693v3 | https://arxiv.org/pdf/2007.02693v3.pdf | Auxiliary Learning by Implicit Differentiation | Training neural networks with auxiliary tasks is a common practice for improving the performance on a main task of interest. Two main challenges arise in this multi-task learning setting: (i) designing useful auxiliary tasks; and (ii) combining auxiliary tasks into a single coherent loss. Here, we propose a novel framework, AuxiLearn, that targets both challenges based on implicit differentiation. First, when useful auxiliaries are known, we propose learning a network that combines all losses into a single coherent objective function. This network can learn non-linear interactions between tasks. Second, when no useful auxiliary task is known, we describe how to learn a network that generates a meaningful, novel auxiliary task. We evaluate AuxiLearn in a series of tasks and domains, including image segmentation and learning with attributes in the low data regime, and find that it consistently outperforms competing methods. | ['Idan Achituve', 'Haggai Maron', 'Gal Chechik', 'Ethan Fetaya', 'Aviv Navon'] | 2020-06-22 | null | https://openreview.net/forum?id=n7wIfYPdVet | https://openreview.net/pdf?id=n7wIfYPdVet | iclr-2021-1 | ['small-data', 'auxiliary-learning'] | ['computer-vision', 'methodology'] | [ 7.32690156e-01 1.98311321e-02 -1.57744616e-01 -3.61069173e-01
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b8b78c15-0dcc-432c-a28c-201dd6cb392c | user-loss-a-forced-choice-inspired-approach | 1807.09303 | null | http://arxiv.org/abs/1807.09303v2 | http://arxiv.org/pdf/1807.09303v2.pdf | User Loss -- A Forced-Choice-Inspired Approach to Train Neural Networks directly by User Interaction | In this paper, we investigate whether is it possible to train a neural
network directly from user inputs. We consider this approach to be highly
relevant for applications in which the point of optimality is not well-defined
and user-dependent. Our application is medical image denoising which is
essential in fluoroscopy imaging. In this field every user, i.e. physician, has
a different flavor and image quality needs to be tailored towards each
individual.
To address this important problem, we propose to construct a loss function
derived from a forced-choice experiment. In order to make the learning problem
feasible, we operate in the domain of precision learning, i.e., we inspire the
network architecture by traditional signal processing methods in order to
reduce the number of trainable parameters. The algorithm that was used for this
is a Laplacian pyramid with only six trainable parameters.
In the experimental results, we demonstrate that two image experts who prefer
different filter characteristics between sharpness and de-noising can be
created using our approach. Also models trained for a specific user perform
best on this users test data. This approach opens the way towards
implementation of direct user feedback in deep learning and is applicable for a
wide range of application. | ['Andreas Maier', 'Bernhard Stimpel', 'Shahab Zarei', 'Christopher Syben'] | 2018-07-24 | null | null | null | null | ['medical-image-denoising'] | ['computer-vision'] | [ 5.33922911e-01 2.01868489e-01 3.50962162e-01 -5.02771854e-01
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77fb8242-9b75-4462-8d93-36b8e04c072e | rete-retrieval-enhanced-temporal-event | 2202.06129 | null | https://arxiv.org/abs/2202.06129v1 | https://arxiv.org/pdf/2202.06129v1.pdf | RETE: Retrieval-Enhanced Temporal Event Forecasting on Unified Query Product Evolutionary Graph | With the increasing demands on e-commerce platforms, numerous user action history is emerging. Those enriched action records are vital to understand users' interests and intents. Recently, prior works for user behavior prediction mainly focus on the interactions with product-side information. However, the interactions with search queries, which usually act as a bridge between users and products, are still under investigated. In this paper, we explore a new problem named temporal event forecasting, a generalized user behavior prediction task in a unified query product evolutionary graph, to embrace both query and product recommendation in a temporal manner. To fulfill this setting, there involves two challenges: (1) the action data for most users is scarce; (2) user preferences are dynamically evolving and shifting over time. To tackle those issues, we propose a novel Retrieval-Enhanced Temporal Event (RETE) forecasting framework. Unlike existing methods that enhance user representations via roughly absorbing information from connected entities in the whole graph, RETE efficiently and dynamically retrieves relevant entities centrally on each user as high-quality subgraphs, preventing the noise propagation from the densely evolutionary graph structures that incorporate abundant search queries. And meanwhile, RETE autoregressively accumulates retrieval-enhanced user representations from each time step, to capture evolutionary patterns for joint query and product prediction. Empirically, extensive experiments on both the public benchmark and four real-world industrial datasets demonstrate the effectiveness of the proposed RETE method. | ['Tarek Abdelzaher', 'Bing Yin', 'Tong Zhao', 'Qingyu Yin', 'Danqing Zhang', 'Zheng Li', 'Ruijie Wang'] | 2022-02-12 | null | null | null | null | ['product-recommendation'] | ['miscellaneous'] | [-2.99244672e-02 -3.59968752e-01 -5.34860969e-01 -2.78344363e-01
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1c91a0f0-1baf-47b2-a043-9f7c05eceb8e | unidu-towards-a-unified-generative-dialogue | 2204.04637 | null | https://arxiv.org/abs/2204.04637v2 | https://arxiv.org/pdf/2204.04637v2.pdf | UniDU: Towards A Unified Generative Dialogue Understanding Framework | With the development of pre-trained language models, remarkable success has been witnessed in dialogue understanding (DU). However, current DU approaches usually employ independent models for each distinct DU task without considering shared knowledge across different DU tasks. In this paper, we propose a unified generative dialogue understanding framework, named {\em UniDU}, to achieve effective information exchange across diverse DU tasks. Here, we reformulate all DU tasks into a unified prompt-based generative model paradigm. More importantly, a novel model-agnostic multi-task training strategy (MATS) is introduced to dynamically adapt the weights of diverse tasks for best knowledge sharing during training, based on the nature and available data of each task. Experiments on ten DU datasets covering five fundamental DU tasks show that the proposed UniDU framework largely outperforms task-specific well-designed methods on all tasks. MATS also reveals the knowledge-sharing structure of these tasks. Finally, UniDU obtains promising performance in the unseen dialogue domain, showing the great potential for generalization. | ['Kai Yu', 'Jian-Guang Lou', 'Su Zhu', 'Yuncong Liu', 'Libo Qin', 'Bei Chen', 'Lu Chen', 'Zhi Chen'] | 2022-04-10 | null | https://aclanthology.org/2022.sigdial-1.43 | https://aclanthology.org/2022.sigdial-1.43.pdf | sigdial-acl-2022-9 | ['dialogue-understanding', 'slot-filling'] | ['natural-language-processing', 'natural-language-processing'] | [ 2.57870313e-02 1.96671918e-01 -6.09510168e-02 -3.53349239e-01
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5b4108fe-ef99-41c1-9513-b803fc2f0130 | applications-of-machine-learning-for-the | 2212.03114 | null | https://arxiv.org/abs/2212.03114v3 | https://arxiv.org/pdf/2212.03114v3.pdf | Applications of Machine Learning for the Ratemaking in Agricultural Insurances | This paper evaluates Machine Learning (ML) in establishing ratemaking for new insurance schemes. To make the evaluation feasible, we established expected indemnities as premiums. Then, we use ML to forecast indemnities using a minimum set of variables. The analysis simulates the introduction of an income insurance scheme, the so-called Income Stabilization Tool (IST), in Italy as a case study using farm-level data from the FADN from 2008-2018. We predicted the expected IST indemnities using three ML tools, LASSO, Elastic Net, and Boosting, that perform variable selection, comparing with the Generalized Linear Model (baseline) usually adopted in insurance investigations. Furthermore, Tweedie distribution is implemented to consider the peculiarity shape of the indemnities function, characterized by zero-inflated, no-negative value, and asymmetric fat-tail. The robustness of the results was evaluated by comparing the econometric and economic performance of the models. Specifically, ML has obtained the best goodness-of-fit than baseline, using a small and stable selection of regressors and significantly reducing the gathering cost of information. However, Boosting enabled it to obtain the best economic performance, balancing the most and most minor risky subjects optimally and achieving good economic sustainability. These findings suggest how machine learning can be successfully applied in agricultural insurance.This study represents one of the first to use ML and Tweedie distribution in agricultural insurance, demonstrating its potential to overcome multiple issues. | ['Luigi Biagini'] | 2022-12-06 | null | null | null | null | ['variable-selection'] | ['methodology'] | [ 4.43386398e-02 3.61880630e-01 -8.27583492e-01 -2.39757106e-01
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d4ac1669-dc9b-42cf-97b5-9da85a551230 | syntax-guided-neural-module-distillation-to | 2301.08998 | null | https://arxiv.org/abs/2301.08998v2 | https://arxiv.org/pdf/2301.08998v2.pdf | Syntax-guided Neural Module Distillation to Probe Compositionality in Sentence Embeddings | Past work probing compositionality in sentence embedding models faces issues determining the causal impact of implicit syntax representations. Given a sentence, we construct a neural module net based on its syntax parse and train it end-to-end to approximate the sentence's embedding generated by a transformer model. The distillability of a transformer to a Syntactic NeurAl Module Net (SynNaMoN) then captures whether syntax is a strong causal model of its compositional ability. Furthermore, we address questions about the geometry of semantic composition by specifying individual SynNaMoN modules' internal architecture & linearity. We find differences in the distillability of various sentence embedding models that broadly correlate with their performance, but observe that distillability doesn't considerably vary by model size. We also present preliminary evidence that much syntax-guided composition in sentence embedding models is linear, and that non-linearities may serve primarily to handle non-compositional phrases. | ['Rohan Pandey'] | 2023-01-21 | null | null | null | null | ['sentence-embeddings', 'sentence-embeddings', 'semantic-composition'] | ['methodology', 'natural-language-processing', 'natural-language-processing'] | [ 2.04844072e-01 7.79723287e-01 -2.62277782e-01 -5.74480057e-01
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e5afa870-da03-46bb-bf88-c5d44a7c4a3e | eecbs-a-bounded-suboptimal-search-for-multi | 2010.01367 | null | https://arxiv.org/abs/2010.01367v2 | https://arxiv.org/pdf/2010.01367v2.pdf | EECBS: A Bounded-Suboptimal Search for Multi-Agent Path Finding | Multi-Agent Path Finding (MAPF), i.e., finding collision-free paths for multiple robots, is important for many applications where small runtimes are necessary, including the kind of automated warehouses operated by Amazon. CBS is a leading two-level search algorithm for solving MAPF optimally. ECBS is a bounded-suboptimal variant of CBS that uses focal search to speed up CBS by sacrificing optimality and instead guaranteeing that the costs of its solutions are within a given factor of optimal. In this paper, we study how to decrease its runtime even further using inadmissible heuristics. Motivated by Explicit Estimation Search (EES), we propose Explicit Estimation CBS (EECBS), a new bounded-suboptimal variant of CBS, that uses online learning to obtain inadmissible estimates of the cost of the solution of each high-level node and uses EES to choose which high-level node to expand next. We also investigate recent improvements of CBS and adapt them to EECBS. We find that EECBS with the improvements runs significantly faster than the state-of-the-art bounded-suboptimal MAPF algorithms ECBS, BCP-7, and eMDD-SAT on a variety of MAPF instances. We hope that the scalability of EECBS enables additional applications for bounded-suboptimal MAPF algorithms. | ['Sven Koenig', 'Wheeler Ruml', 'Jiaoyang Li'] | 2020-10-03 | null | null | null | null | ['multi-agent-path-finding'] | ['playing-games'] | [-3.97954315e-01 3.05593371e-01 -4.83818531e-01 -7.10526556e-02
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dbb08fde-735a-4dde-b6ec-4b4121c0a471 | learning-landmarks-motion-from-speech-for | 2306.01415 | null | https://arxiv.org/abs/2306.01415v1 | https://arxiv.org/pdf/2306.01415v1.pdf | Learning Landmarks Motion from Speech for Speaker-Agnostic 3D Talking Heads Generation | This paper presents a novel approach for generating 3D talking heads from raw audio inputs. Our method grounds on the idea that speech related movements can be comprehensively and efficiently described by the motion of a few control points located on the movable parts of the face, i.e., landmarks. The underlying musculoskeletal structure then allows us to learn how their motion influences the geometrical deformations of the whole face. The proposed method employs two distinct models to this aim: the first one learns to generate the motion of a sparse set of landmarks from the given audio. The second model expands such landmarks motion to a dense motion field, which is utilized to animate a given 3D mesh in neutral state. Additionally, we introduce a novel loss function, named Cosine Loss, which minimizes the angle between the generated motion vectors and the ground truth ones. Using landmarks in 3D talking head generation offers various advantages such as consistency, reliability, and obviating the need for manual-annotation. Our approach is designed to be identity-agnostic, enabling high-quality facial animations for any users without additional data or training. | ['Stefano Berretti', 'Claudio Ferrari', 'Federico Nocentini'] | 2023-06-02 | null | null | null | null | ['talking-head-generation'] | ['computer-vision'] | [-3.58266197e-02 6.32508636e-01 -2.05787085e-02 -3.44134629e-01
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bf355f65-07b8-44bc-b3e3-6fa0f1ad54e6 | systematic-inequalities-in-language-1 | null | null | https://aclanthology.org/2022.acl-long.376 | https://aclanthology.org/2022.acl-long.376.pdf | Systematic Inequalities in Language Technology Performance across the World’s Languages | Natural language processing (NLP) systems have become a central technology in communication, education, medicine, artificial intelligence, and many other domains of research and development. While the performance of NLP methods has grown enormously over the last decade, this progress has been restricted to a minuscule subset of the world’s \approx6,500 languages. We introduce a framework for estimating the global utility of language technologies as revealed in a comprehensive snapshot of recent publications in NLP. Our analyses involve the field at large, but also more in-depth studies on both user-facing technologies (machine translation, language understanding, question answering, text-to-speech synthesis) as well as foundational NLP tasks (dependency parsing, morphological inflection). In the process, we (1) quantify disparities in the current state of NLP research, (2) explore some of its associated societal and academic factors, and (3) produce tailored recommendations for evidence-based policy making aimed at promoting more global and equitable language technologies. Data and code to reproduce the findings discussed in this paper areavailable on GitHub (https://github.com/neubig/globalutility). | ['Graham Neubig', 'Antonios Anastasopoulos', 'Damian Blasi'] | null | null | null | null | acl-2022-5 | ['morphological-inflection'] | ['natural-language-processing'] | [ 2.49406844e-02 3.96515995e-01 -5.55214107e-01 -2.43760914e-01
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0a146ab4-fa28-4d64-be94-7142c37cc3f9 | improving-language-model-negotiation-with | 2305.10142 | null | https://arxiv.org/abs/2305.10142v1 | https://arxiv.org/pdf/2305.10142v1.pdf | Improving Language Model Negotiation with Self-Play and In-Context Learning from AI Feedback | We study whether multiple large language models (LLMs) can autonomously improve each other in a negotiation game by playing, reflecting, and criticizing. We are interested in this question because if LLMs were able to improve each other, it would imply the possibility of creating strong AI agents with minimal human intervention. We ask two LLMs to negotiate with each other, playing the roles of a buyer and a seller, respectively. They aim to reach a deal with the buyer targeting a lower price and the seller a higher one. A third language model, playing the critic, provides feedback to a player to improve the player's negotiation strategies. We let the two agents play multiple rounds, using previous negotiation history and AI feedback as in-context demonstrations to improve the model's negotiation strategy iteratively. We use different LLMs (GPT and Claude) for different roles and use the deal price as the evaluation metric. Our experiments reveal multiple intriguing findings: (1) Only a subset of the language models we consider can self-play and improve the deal price from AI feedback, weaker models either do not understand the game's rules or cannot incorporate AI feedback for further improvement. (2) Models' abilities to learn from the feedback differ when playing different roles. For example, it is harder for Claude-instant to improve as the buyer than as the seller. (3) When unrolling the game to multiple rounds, stronger agents can consistently improve their performance by meaningfully using previous experiences and iterative AI feedback, yet have a higher risk of breaking the deal. We hope our work provides insightful initial explorations of having models autonomously improve each other with game playing and AI feedback. | ['Mirella Lapata', 'Tushar Khot', 'Hao Peng', 'Yao Fu'] | 2023-05-17 | null | null | null | null | ['unrolling'] | ['computer-vision'] | [-4.35259342e-02 7.38755107e-01 -9.58102942e-02 -8.41804072e-02
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977753c2-94e7-4cf0-99a5-574c89bc4dbd | cooperative-multi-objective-reinforcement | 2306.09662 | null | https://arxiv.org/abs/2306.09662v1 | https://arxiv.org/pdf/2306.09662v1.pdf | Cooperative Multi-Objective Reinforcement Learning for Traffic Signal Control and Carbon Emission Reduction | Existing traffic signal control systems rely on oversimplified rule-based methods, and even RL-based methods are often suboptimal and unstable. To address this, we propose a cooperative multi-objective architecture called Multi-Objective Multi-Agent Deep Deterministic Policy Gradient (MOMA-DDPG), which estimates multiple reward terms for traffic signal control optimization using age-decaying weights. Our approach involves two types of agents: one focuses on optimizing local traffic at each intersection, while the other aims to optimize global traffic throughput. We evaluate our method using real-world traffic data collected from an Asian country's traffic cameras. Despite the inclusion of a global agent, our solution remains decentralized as this agent is no longer necessary during the inference stage. Our results demonstrate the effectiveness of MOMA-DDPG, outperforming state-of-the-art methods across all performance metrics. Additionally, our proposed system minimizes both waiting time and carbon emissions. Notably, this paper is the first to link carbon emissions and global agents in traffic signal control. | ['Shin You Teng', 'Jun Wei Hsieh', 'Cheng Ruei Tang'] | 2023-06-16 | null | null | null | null | ['multi-objective-reinforcement-learning'] | ['methodology'] | [-2.49931186e-01 -8.13163817e-02 -3.99273723e-01 -6.73557669e-02
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3791b560-402e-4f05-aeef-4d6550c8352c | a-constrained-weighted-l1-minimization | 1709.0409 | null | http://arxiv.org/abs/1709.04090v2 | http://arxiv.org/pdf/1709.04090v2.pdf | A Constrained, Weighted-L1 Minimization Approach for Joint Discovery of Heterogeneous Neural Connectivity Graphs | Determining functional brain connectivity is crucial to understanding the
brain and neural differences underlying disorders such as autism. Recent
studies have used Gaussian graphical models to learn brain connectivity via
statistical dependencies across brain regions from neuroimaging. However,
previous studies often fail to properly incorporate priors tailored to
neuroscience, such as preferring shorter connections. To remedy this problem,
the paper here introduces a novel, weighted-$\ell_1$, multi-task graphical
model (W-SIMULE). This model elegantly incorporates a flexible prior, along
with a parallelizable formulation. Additionally, W-SIMULE extends the
often-used Gaussian assumption, leading to considerable performance increases.
Here, applications to fMRI data show that W-SIMULE succeeds in determining
functional connectivity in terms of (1) log-likelihood, (2) finding edges that
differentiate groups, and (3) classifying different groups based on their
connectivity, achieving 58.6\% accuracy on the ABIDE dataset. Having
established W-SIMULE's effectiveness, it links four key areas to autism, all of
which are consistent with the literature. Due to its elegant domain adaptivity,
W-SIMULE can be readily applied to various data types to effectively estimate
connectivity. | ['Yanjun Qi', 'Chandan Singh', 'Beilun Wang'] | 2017-09-13 | a-constrained-weighted-l1-minimization-1 | null | null | arxiv-2017-9 | ['connectivity-estimation'] | ['graphs'] | [-1.63436625e-02 6.32742792e-02 -8.02248158e-03 -4.57191765e-01
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b96e9fc6-52ba-4510-bd20-7f1dcdd9bad8 | warpinggan-warping-multiple-uniform-priors | 2203.12917 | null | https://arxiv.org/abs/2203.12917v1 | https://arxiv.org/pdf/2203.12917v1.pdf | WarpingGAN: Warping Multiple Uniform Priors for Adversarial 3D Point Cloud Generation | We propose WarpingGAN, an effective and efficient 3D point cloud generation network. Unlike existing methods that generate point clouds by directly learning the mapping functions between latent codes and 3D shapes, Warping-GAN learns a unified local-warping function to warp multiple identical pre-defined priors (i.e., sets of points uniformly distributed on regular 3D grids) into 3D shapes driven by local structure-aware semantics. In addition, we also ingeniously utilize the principle of the discriminator and tailor a stitching loss to eliminate the gaps between different partitions of a generated shape corresponding to different priors for boosting quality. Owing to the novel generating mechanism, WarpingGAN, a single lightweight network after one-time training, is capable of efficiently generating uniformly distributed 3D point clouds with various resolutions. Extensive experimental results demonstrate the superiority of our WarpingGAN over state-of-the-art methods in terms of quantitative metrics, visual quality, and efficiency. The source code is publicly available at https://github.com/yztang4/WarpingGAN.git. | ['Xuefei Zhe', 'Junhui Hou', 'Yiming Zeng', 'Qijian Zhang', 'Yue Qian', 'Yingzhi Tang'] | 2022-03-24 | null | http://openaccess.thecvf.com//content/CVPR2022/html/Tang_WarpingGAN_Warping_Multiple_Uniform_Priors_for_Adversarial_3D_Point_Cloud_CVPR_2022_paper.html | http://openaccess.thecvf.com//content/CVPR2022/papers/Tang_WarpingGAN_Warping_Multiple_Uniform_Priors_for_Adversarial_3D_Point_Cloud_CVPR_2022_paper.pdf | cvpr-2022-1 | ['point-cloud-generation'] | ['computer-vision'] | [ 9.48642045e-02 7.09396750e-02 -5.09540364e-02 2.29263548e-02
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674a622a-26eb-44b8-ba64-8ee40ee105f0 | gimme-signals-discriminative-signal-encoding | 2003.06156 | null | https://arxiv.org/abs/2003.06156v2 | https://arxiv.org/pdf/2003.06156v2.pdf | Gimme Signals: Discriminative signal encoding for multimodal activity recognition | We present a simple, yet effective and flexible method for action recognition supporting multiple sensor modalities. Multivariate signal sequences are encoded in an image and are then classified using a recently proposed EfficientNet CNN architecture. Our focus was to find an approach that generalizes well across different sensor modalities without specific adaptions while still achieving good results. We apply our method to 4 action recognition datasets containing skeleton sequences, inertial and motion capturing measurements as well as \wifi fingerprints that range up to 120 action classes. Our method defines the current best CNN-based approach on the NTU RGB+D 120 dataset, lifts the state of the art on the ARIL Wi-Fi dataset by +6.78%, improves the UTD-MHAD inertial baseline by +14.4%, the UTD-MHAD skeleton baseline by 1.13% and achieves 96.11% on the Simitate motion capturing data (80/20 split). We further demonstrate experiments on both, modality fusion on a signal level and signal reduction to prevent the representation from overloading. | ['Raphael Memmesheimer', 'Nick Theisen', 'Dietrich Paulus'] | 2020-03-13 | null | null | null | null | ['multimodal-activity-recognition'] | ['computer-vision'] | [ 8.87539029e-01 -1.25154123e-01 -5.51961660e-01 -2.96718270e-01
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1a36f895-51b6-4db5-bfa6-cdb33fd39c0b | camera-relocalization-by-computing-pairwise | 1707.09733 | null | http://arxiv.org/abs/1707.09733v2 | http://arxiv.org/pdf/1707.09733v2.pdf | Camera Relocalization by Computing Pairwise Relative Poses Using Convolutional Neural Network | We propose a new deep learning based approach for camera relocalization. Our
approach localizes a given query image by using a convolutional neural network
(CNN) for first retrieving similar database images and then predicting the
relative pose between the query and the database images, whose poses are known.
The camera location for the query image is obtained via triangulation from two
relative translation estimates using a RANSAC based approach. Each relative
pose estimate provides a hypothesis for the camera orientation and they are
fused in a second RANSAC scheme. The neural network is trained for relative
pose estimation in an end-to-end manner using training image pairs. In contrast
to previous work, our approach does not require scene-specific training of the
network, which improves scalability, and it can also be applied to scenes which
are not available during the training of the network. As another main
contribution, we release a challenging indoor localisation dataset covering 5
different scenes registered to a common coordinate frame. We evaluate our
approach using both our own dataset and the standard 7 Scenes benchmark. The
results show that the proposed approach generalizes well to previously unseen
scenes and compares favourably to other recent CNN-based methods. | ['Zakaria Laskar', 'Surya Kalia', 'Juho Kannala', 'Iaroslav Melekhov'] | 2017-07-31 | null | null | null | null | ['camera-relocalization'] | ['computer-vision'] | [ 1.76489264e-01 -3.21382374e-01 -8.17841142e-02 -5.01803339e-01
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e8ba63fa-4b6d-49ec-89ff-6767a5e869ce | towards-more-robust-and-accurate-sequential | 2304.05492 | null | https://arxiv.org/abs/2304.05492v1 | https://arxiv.org/pdf/2304.05492v1.pdf | Towards More Robust and Accurate Sequential Recommendation with Cascade-guided Adversarial Training | Sequential recommendation models, models that learn from chronological user-item interactions, outperform traditional recommendation models in many settings. Despite the success of sequential recommendation models, their robustness has recently come into question. Two properties unique to the nature of sequential recommendation models may impair their robustness - the cascade effects induced during training and the model's tendency to rely too heavily on temporal information. To address these vulnerabilities, we propose Cascade-guided Adversarial training, a new adversarial training procedure that is specifically designed for sequential recommendation models. Our approach harnesses the intrinsic cascade effects present in sequential modeling to produce strategic adversarial perturbations to item embeddings during training. Experiments on training state-of-the-art sequential models on four public datasets from different domains show that our training approach produces superior model ranking accuracy and superior model robustness to real item replacement perturbations when compared to both standard model training and generic adversarial training. | ['Huan Wang', 'Yongfeng Zhang', 'Yongjun Chen', 'Zhiwei Liu', 'Shelby Heinecke', 'Juntao Tan'] | 2023-04-11 | null | null | null | null | ['sequential-recommendation'] | ['miscellaneous'] | [ 2.01157078e-01 -2.42223442e-01 -3.37725967e-01 1.63430739e-02
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70934a6a-398a-41fc-8775-52f93d1b16e2 | conformal-regression-in-calorie-prediction | 2304.03778 | null | https://arxiv.org/abs/2304.03778v2 | https://arxiv.org/pdf/2304.03778v2.pdf | Conformal Regression in Calorie Prediction for Team Jumbo-Visma | UCI WorldTour races, the premier men's elite road cycling tour, are grueling events that put physical fitness and endurance of riders to the test. The coaches of Team Jumbo-Visma have long been responsible for predicting the energy needs of each rider of the Dutch team for every race on the calendar. Those must be estimated to ensure riders have the energy and resources necessary to maintain a high level of performance throughout a race. This task, however, is both time-consuming and challenging, as it requires precise estimates of race speed and power output. Traditionally, the approach to predicting energy needs has relied on judgement and experience of coaches, but this method has its limitations and often leads to inaccurate predictions. In this paper, we propose a new, more effective approach to predicting energy needs for cycling races. By predicting the speed and power with regression models, we provide the coaches with calorie needs estimates for each individual rider per stage instantly. In addition, we compare methods to quantify uncertainty using conformal prediction. The empirical analysis of the jackknife+, jackknife-minmax, jackknife-minmax-after-bootstrap, CV+, CV-minmax, conformalized quantile regression, and inductive conformal prediction methods in conformal prediction reveals that all methods achieve valid prediction intervals. All but minmax-based methods also produce produce sufficiently narrow prediction intervals for decision-making. Furthermore, methods computing prediction intervals of fixed size produce tighter intervals for low significance values. Among the methods computing intervals of varying length across the input space, inductive conformal prediction computes narrower prediction intervals at larger significance level. | ['Christof Seiler', 'Mark Dirksen', 'Kristian van Kuijk'] | 2023-04-06 | null | null | null | null | ['prediction-intervals'] | ['miscellaneous'] | [-2.32041761e-01 -1.77641600e-01 -7.48080075e-01 -2.38046080e-01
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0c857af7-e737-4d6c-9f7e-e37d634636a3 | stackelberg-decision-transformer-for | 2305.07856 | null | https://arxiv.org/abs/2305.07856v1 | https://arxiv.org/pdf/2305.07856v1.pdf | Stackelberg Decision Transformer for Asynchronous Action Coordination in Multi-Agent Systems | Asynchronous action coordination presents a pervasive challenge in Multi-Agent Systems (MAS), which can be represented as a Stackelberg game (SG). However, the scalability of existing Multi-Agent Reinforcement Learning (MARL) methods based on SG is severely constrained by network structures or environmental limitations. To address this issue, we propose the Stackelberg Decision Transformer (STEER), a heuristic approach that resolves the difficulties of hierarchical coordination among agents. STEER efficiently manages decision-making processes in both spatial and temporal contexts by incorporating the hierarchical decision structure of SG, the modeling capability of autoregressive sequence models, and the exploratory learning methodology of MARL. Our research contributes to the development of an effective and adaptable asynchronous action coordination method that can be widely applied to various task types and environmental configurations in MAS. Experimental results demonstrate that our method can converge to Stackelberg equilibrium solutions and outperforms other existing methods in complex scenarios. | ['Guoliang Fan', 'Rui Zhao', 'Dapeng Li', 'Zhiwei Xu', 'Lijuan Li', 'Hangyu Mao', 'Bin Zhang'] | 2023-05-13 | null | null | null | null | ['multi-agent-reinforcement-learning'] | ['methodology'] | [-2.08371341e-01 -2.01748803e-01 4.03247997e-02 2.30107158e-01
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a815e407-1bc4-4399-9e15-f73f3452ee9a | latent-variable-generative-models-for-data | 1910.00382 | null | https://arxiv.org/abs/1910.00382v1 | https://arxiv.org/pdf/1910.00382v1.pdf | Latent-Variable Generative Models for Data-Efficient Text Classification | Generative classifiers offer potential advantages over their discriminative counterparts, namely in the areas of data efficiency, robustness to data shift and adversarial examples, and zero-shot learning (Ng and Jordan,2002; Yogatama et al., 2017; Lewis and Fan,2019). In this paper, we improve generative text classifiers by introducing discrete latent variables into the generative story, and explore several graphical model configurations. We parameterize the distributions using standard neural architectures used in conditional language modeling and perform learning by directly maximizing the log marginal likelihood via gradient-based optimization, which avoids the need to do expectation-maximization. We empirically characterize the performance of our models on six text classification datasets. The choice of where to include the latent variable has a significant impact on performance, with the strongest results obtained when using the latent variable as an auxiliary conditioning variable in the generation of the textual input. This model consistently outperforms both the generative and discriminative classifiers in small-data settings. We analyze our model by using it for controlled generation, finding that the latent variable captures interpretable properties of the data, even with very small training sets. | ['Xiaoan Ding', 'Kevin Gimpel'] | 2019-10-01 | latent-variable-generative-models-for-data-1 | https://aclanthology.org/D19-1048 | https://aclanthology.org/D19-1048.pdf | ijcnlp-2019-11 | ['small-data'] | ['computer-vision'] | [ 2.68333942e-01 1.61466494e-01 -3.53108197e-01 -3.85425717e-01
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646982de-52c9-46e1-87b4-4a9585ea15b0 | controllable-attention-for-structured-layered-1 | 1910.11306 | null | https://arxiv.org/abs/1910.11306v1 | https://arxiv.org/pdf/1910.11306v1.pdf | Controllable Attention for Structured Layered Video Decomposition | The objective of this paper is to be able to separate a video into its natural layers, and to control which of the separated layers to attend to. For example, to be able to separate reflections, transparency or object motion. We make the following three contributions: (i) we introduce a new structured neural network architecture that explicitly incorporates layers (as spatial masks) into its design. This improves separation performance over previous general purpose networks for this task; (ii) we demonstrate that we can augment the architecture to leverage external cues such as audio for controllability and to help disambiguation; and (iii) we experimentally demonstrate the effectiveness of our approach and training procedure with controlled experiments while also showing that the proposed model can be successfully applied to real-word applications such as reflection removal and action recognition in cluttered scenes. | ['Relja Arandjelović', 'João Carreira', 'Jean-Baptiste Alayrac', 'Andrew Zisserman'] | 2019-10-24 | controllable-attention-for-structured-layered | http://openaccess.thecvf.com/content_ICCV_2019/html/Alayrac_Controllable_Attention_for_Structured_Layered_Video_Decomposition_ICCV_2019_paper.html | http://openaccess.thecvf.com/content_ICCV_2019/papers/Alayrac_Controllable_Attention_for_Structured_Layered_Video_Decomposition_ICCV_2019_paper.pdf | iccv-2019-10 | ['reflection-removal'] | ['computer-vision'] | [ 7.16139197e-01 1.75653502e-01 1.92700788e-01 -1.91152081e-01
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5a39daaa-c9ac-477d-9c74-6ea21874119c | social-sparsity-brain-decoders-faster-spatial | 1606.06439 | null | http://arxiv.org/abs/1606.06439v1 | http://arxiv.org/pdf/1606.06439v1.pdf | Social-sparsity brain decoders: faster spatial sparsity | Spatially-sparse predictors are good models for brain decoding: they give
accurate predictions and their weight maps are interpretable as they focus on a
small number of regions. However, the state of the art, based on total
variation or graph-net, is computationally costly. Here we introduce sparsity
in the local neighborhood of each voxel with social-sparsity, a structured
shrinkage operator. We find that, on brain imaging classification problems,
social-sparsity performs almost as well as total-variation models and better
than graph-net, for a fraction of the computational cost. It also very clearly
outlines predictive regions. We give details of the model and the algorithm. | ['Matthieu Kowalski', 'Gaël Varoquaux', 'Bertrand Thirion'] | 2016-06-21 | null | null | null | null | ['brain-decoding', 'brain-decoding'] | ['medical', 'miscellaneous'] | [ 1.59612507e-01 4.67968494e-01 -5.14498293e-01 -6.64773464e-01
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8f8edf34-3dc0-4615-92d8-6a0c8d10e36f | scenesqueezer-learning-to-compress-scene-for | null | null | http://openaccess.thecvf.com//content/CVPR2022/html/Yang_SceneSqueezer_Learning_To_Compress_Scene_for_Camera_Relocalization_CVPR_2022_paper.html | http://openaccess.thecvf.com//content/CVPR2022/papers/Yang_SceneSqueezer_Learning_To_Compress_Scene_for_Camera_Relocalization_CVPR_2022_paper.pdf | SceneSqueezer: Learning To Compress Scene for Camera Relocalization | Standard visual localization methods build a priori 3D model of a scene which is used to establish correspondences against the 2D keypoints in a query image. Storing these pre-built 3D scene models can be prohibitively expensive for large-scale environments, especially on mobile devices with limited storage and communication bandwidth. We design a novel framework that compresses a scene while still maintaining localization accuracy. The scene is compressed in three stages: first, the database frames are clustered using pairwise co-visibility information. Then, a learned point selection module prunes the points in each cluster taking into account the final pose estimation accuracy. In the final stage, the features of the selected points are further compressed using learned quantization. Query image registration is done using only the compressed scene points. To the best of our knowledge, we are the first to propose learned scene compression for visual localization. We also demonstrate the effectiveness and efficiency of our method on various outdoor datasets where it can perform accurate localization with low memory consumption. | ['Ping Tan', 'Zhaopeng Cui', 'Guofeng Zhang', 'Shuaicheng Liu', 'Wenbo Li', 'Rakesh Shrestha', 'Luwei Yang'] | 2022-01-01 | null | null | null | cvpr-2022-1 | ['camera-relocalization'] | ['computer-vision'] | [ 1.34689257e-01 -5.07922053e-01 -2.28666112e-01 -4.10401493e-01
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7b714f35-41ca-468b-b2db-2a2757018a62 | towards-a-foundation-model-for-neural-network | 2303.09949 | null | https://arxiv.org/abs/2303.09949v1 | https://arxiv.org/pdf/2303.09949v1.pdf | Towards a Foundation Model for Neural Network Wavefunctions | Deep neural networks have become a highly accurate and powerful wavefunction ansatz in combination with variational Monte Carlo methods for solving the electronic Schr\"odinger equation. However, despite their success and favorable scaling, these methods are still computationally too costly for wide adoption. A significant obstacle is the requirement to optimize the wavefunction from scratch for each new system, thus requiring long optimization. In this work, we propose a novel neural network ansatz, which effectively maps uncorrelated, computationally cheap Hartree-Fock orbitals, to correlated, high-accuracy neural network orbitals. This ansatz is inherently capable of learning a single wavefunction across multiple compounds and geometries, as we demonstrate by successfully transferring a wavefunction model pre-trained on smaller fragments to larger compounds. Furthermore, we provide ample experimental evidence to support the idea that extensive pre-training of a such a generalized wavefunction model across different compounds and geometries could lead to a foundation wavefunction model. Such a model could yield high-accuracy ab-initio energies using only minimal computational effort for fine-tuning and evaluation of observables. | ['Philipp Grohs', 'Leon Gerard', 'Michael Scherbela'] | 2023-03-17 | null | null | null | null | ['variational-monte-carlo'] | ['miscellaneous'] | [ 1.42637268e-01 -3.92742187e-01 -6.76711500e-02 -4.82911170e-01
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499f0e57-b02b-4c67-87ba-250621ce7a8b | a-neural-network-solves-and-generates | 2112.15594 | null | https://arxiv.org/abs/2112.15594v4 | https://arxiv.org/pdf/2112.15594v4.pdf | A Neural Network Solves, Explains, and Generates University Math Problems by Program Synthesis and Few-Shot Learning at Human Level | We demonstrate that a neural network pre-trained on text and fine-tuned on code solves mathematics course problems, explains solutions, and generates new questions at a human level. We automatically synthesize programs using few-shot learning and OpenAI's Codex transformer and execute them to solve course problems at 81% automatic accuracy. We curate a new dataset of questions from MIT's largest mathematics courses (Single Variable and Multivariable Calculus, Differential Equations, Introduction to Probability and Statistics, Linear Algebra, and Mathematics for Computer Science) and Columbia University's Computational Linear Algebra. We solve questions from a MATH dataset (on Prealgebra, Algebra, Counting and Probability, Intermediate Algebra, Number Theory, and Precalculus), the latest benchmark of advanced mathematics problems designed to assess mathematical reasoning. We randomly sample questions and generate solutions with multiple modalities, including numbers, equations, and plots. The latest GPT-3 language model pre-trained on text automatically solves only 18.8% of these university questions using zero-shot learning and 30.8% using few-shot learning and the most recent chain of thought prompting. In contrast, program synthesis with few-shot learning using Codex fine-tuned on code generates programs that automatically solve 81% of these questions. Our approach improves the previous state-of-the-art automatic solution accuracy on the benchmark topics from 8.8% to 81.1%. We perform a survey to evaluate the quality and difficulty of generated questions. This work is the first to automatically solve university-level mathematics course questions at a human level and the first work to explain and generate university-level mathematics course questions at scale, a milestone for higher education. | ['Linda Chen', 'Kevin Liu', 'Albert Lu', 'Reece Shuttleworth', 'Sarah Zhang', 'Gilbert Strang', 'Eugene Wu', 'Nakul Verma', 'Avi Shporer', 'Jayson Lynch', 'Taylor L. Patti', 'Nikhil Singh', 'Elizabeth Ke', 'Leonard Tang', 'Newman Cheng', 'Roman Wang', 'Sunny Tran', 'Iddo Drori'] | 2021-12-31 | null | null | null | null | ['mathematical-reasoning'] | ['natural-language-processing'] | [-3.73719856e-02 2.58621335e-01 5.73776895e-03 -6.91292575e-03
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f89103f6-2d63-4c0e-a837-cb8da1e8ba61 | early-predictions-for-medical-crowdfunding-a | 1911.05702 | null | https://arxiv.org/abs/1911.05702v1 | https://arxiv.org/pdf/1911.05702v1.pdf | Early Predictions for Medical Crowdfunding: A Deep Learning Approach Using Diverse Inputs | Medical crowdfunding is a popular channel for people needing financial help paying medical bills to collect donations from large numbers of people. However, large heterogeneity exists in donations across cases, and fundraisers face significant uncertainty in whether their crowdfunding campaigns can meet fundraising goals. Therefore, it is important to provide early warnings for fundraisers if such a channel will eventually fail. In this study, we aim to develop novel algorithms to provide accurate and timely predictions of fundraising performance, to better inform fundraisers. In particular, we propose a new approach to combine time-series features and time-invariant features in the deep learning model, to process diverse sources of input data. Compared with baseline models, our model achieves better accuracy and requires a shorter observation window of the time-varying features from the campaign launch to provide robust predictions with high confidence. To extract interpretable insights, we further conduct a multivariate time-series clustering analysis and identify four typical temporal donation patterns. This demonstrates the heterogeneity in the features and how they relate to the fundraising outcome. The prediction model and the interpretable insights can be applied to assist fundraisers with better promoting their fundraising campaigns and can potentially help crowdfunding platforms to provide more timely feedback to all fundraisers. Our proposed framework is also generalizable to other fields where diverse structured and unstructured data are valuable for predictions. | ['Hu', 'Yu', 'Tong Wang', 'Fujie Jin', 'Yuan Cheng'] | 2019-11-09 | null | null | null | null | ['time-series-clustering'] | ['time-series'] | [-3.88349652e-01 -1.77184790e-01 -5.73992431e-01 -5.78033745e-01
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200aa7b5-8a83-4e89-8c14-a4844c3e3b0c | prototype-based-interpretable-graph-neural | null | null | https://ieeexplore.ieee.org/document/9953541 | https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=9953541&tag=1 | Prototype-based Interpretable Graph Neural Networks | Graph neural networks have proved to be a key tool for dealing with many problems and domains such as chemistry, natural language processing and social networks. While the structure of the layers is simple, it is difficult to identify the patterns learned by the graph neural network. Several works propose post-hoc methods to explain graph predictions, but few of them try to generate interpretable models. Conversely, the topic of the interpretable models is highly investigated in image recognition. Given the similarity between image and graph domains, we analyze the adaptability of prototype-based neural networks for graph and node classification. In particular, we investigate the use of two interpretable networks, ProtoPNet and TesNet, in the graph domain. We show that the adapted networks manage to reach better or higher accuracy scores than their respective black-box models and comparable performances with state-of-the-art self-explainable models. Showing how to extract ProtoPNet and TesNet explanations on graph neural networks, we further study how to obtain global and local explanations for the trained models. We then evaluate the explanations of the interpretable models by comparing them with post-hoc approaches and self-explainable models. Our findings show that the application of TesNet and ProtoPNet to the graph domain produces qualitative predictions while improving their reliability and transparency. | ['Roberto Capobianco', 'Biagio La Rosa', 'Alessio Ragno'] | 2022-11-16 | null | null | null | ieee-transactions-on-artificial-intelligence | ['explainable-models'] | ['computer-vision'] | [ 4.22051549e-01 1.35744953e+00 -2.26609543e-01 -4.32946861e-01
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c60843f7-73df-42cc-b7d0-1625875a4998 | tarc-tunisian-arabish-corpus-first-complete | 2207.04796 | null | https://arxiv.org/abs/2207.04796v1 | https://arxiv.org/pdf/2207.04796v1.pdf | TArC: Tunisian Arabish Corpus First complete release | In this paper we present the final result of a project on Tunisian Arabic encoded in Arabizi, the Latin-based writing system for digital conversations. The project led to the creation of two integrated and independent resources: a corpus and a NLP tool created to annotate the former with various levels of linguistic information: word classification, transliteration, tokenization, POS-tagging, lemmatization. We discuss our choices in terms of computational and linguistic methodology and the strategies adopted to improve our results. We report on the experiments performed in order to outline our research path. Finally, we explain why we believe in the potential of these resources for both computational and linguistic researches. Keywords: Tunisian Arabizi, Annotated Corpus, Neural Network Architecture | ['Marco Dinarelli', 'Elisa Gugliotta'] | 2022-07-11 | null | null | null | null | ['transliteration'] | ['natural-language-processing'] | [-8.08972418e-02 1.79717138e-01 -1.66419491e-01 -4.83733535e-01
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4148fdc1-2a83-41fd-8d44-2251b3362e7e | uncertainty-based-traffic-accident | 2008.00334 | null | https://arxiv.org/abs/2008.00334v1 | https://arxiv.org/pdf/2008.00334v1.pdf | Uncertainty-based Traffic Accident Anticipation with Spatio-Temporal Relational Learning | Traffic accident anticipation aims to predict accidents from dashcam videos as early as possible, which is critical to safety-guaranteed self-driving systems. With cluttered traffic scenes and limited visual cues, it is of great challenge to predict how long there will be an accident from early observed frames. Most existing approaches are developed to learn features of accident-relevant agents for accident anticipation, while ignoring the features of their spatial and temporal relations. Besides, current deterministic deep neural networks could be overconfident in false predictions, leading to high risk of traffic accidents caused by self-driving systems. In this paper, we propose an uncertainty-based accident anticipation model with spatio-temporal relational learning. It sequentially predicts the probability of traffic accident occurrence with dashcam videos. Specifically, we propose to take advantage of graph convolution and recurrent networks for relational feature learning, and leverage Bayesian neural networks to address the intrinsic variability of latent relational representations. The derived uncertainty-based ranking loss is found to significantly boost model performance by improving the quality of relational features. In addition, we collect a new Car Crash Dataset (CCD) for traffic accident anticipation which contains environmental attributes and accident reasons annotations. Experimental results on both public and the newly-compiled datasets show state-of-the-art performance of our model. Our code and CCD dataset are available at https://github.com/Cogito2012/UString. | ['Yu Kong', 'Qi Yu', 'Wentao Bao'] | 2020-08-01 | null | null | null | null | ['accident-anticipation', 'activity-prediction', 'time-to-event-prediction', 'activity-prediction'] | ['computer-vision', 'computer-vision', 'time-series', 'time-series'] | [-1.50204478e-02 3.06325685e-02 -2.67962784e-01 -5.09524047e-01
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5df1e8d0-04b6-4499-8bba-4b167ece23a2 | seatrans-learning-segmentation-assisted | 2206.05763 | null | https://arxiv.org/abs/2206.05763v2 | https://arxiv.org/pdf/2206.05763v2.pdf | SeATrans: Learning Segmentation-Assisted diagnosis model via Transformer | Clinically, the accurate annotation of lesions/tissues can significantly facilitate the disease diagnosis. For example, the segmentation of optic disc/cup (OD/OC) on fundus image would facilitate the glaucoma diagnosis, the segmentation of skin lesions on dermoscopic images is helpful to the melanoma diagnosis, etc. With the advancement of deep learning techniques, a wide range of methods proved the lesions/tissues segmentation can also facilitate the automated disease diagnosis models. However, existing methods are limited in the sense that they can only capture static regional correlations in the images. Inspired by the global and dynamic nature of Vision Transformer, in this paper, we propose Segmentation-Assisted diagnosis Transformer (SeATrans) to transfer the segmentation knowledge to the disease diagnosis network. Specifically, we first propose an asymmetric multi-scale interaction strategy to correlate each single low-level diagnosis feature with multi-scale segmentation features. Then, an effective strategy called SeA-block is adopted to vitalize diagnosis feature via correlated segmentation features. To model the segmentation-diagnosis interaction, SeA-block first embeds the diagnosis feature based on the segmentation information via the encoder, and then transfers the embedding back to the diagnosis feature space by a decoder. Experimental results demonstrate that SeATrans surpasses a wide range of state-of-the-art (SOTA) segmentation-assisted diagnosis methods on several disease diagnosis tasks. | ['Yanwu Xu', 'Yehui Yang', 'Jing Gao', 'Zhaowei Wang', 'Dalu Yang', 'Fangxin Shang', 'Huihui Fang', 'Junde Wu'] | 2022-06-12 | null | null | null | null | ['melanoma-diagnosis'] | ['computer-vision'] | [ 1.87670290e-01 2.85626948e-02 -2.93474257e-01 -2.67642885e-01
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57ad705a-7d39-4cf0-99d6-4443d10e0262 | evaluating-feature-attribution-methods-for | 2211.12702 | null | https://arxiv.org/abs/2211.12702v1 | https://arxiv.org/pdf/2211.12702v1.pdf | Evaluating Feature Attribution Methods for Electrocardiogram | The performance of cardiac arrhythmia detection with electrocardiograms(ECGs) has been considerably improved since the introduction of deep learning models. In practice, the high performance alone is not sufficient and a proper explanation is also required. Recently, researchers have started adopting feature attribution methods to address this requirement, but it has been unclear which of the methods are appropriate for ECG. In this work, we identify and customize three evaluation metrics for feature attribution methods based on the characteristics of ECG: localization score, pointing game, and degradation score. Using the three evaluation metrics, we evaluate and analyze eleven widely-used feature attribution methods. We find that some of the feature attribution methods are much more adequate for explaining ECG, where Grad-CAM outperforms the second-best method by a large margin. | ['Wonjong Rhee', 'Euna Jung', 'Jimyeong Kim', 'Jangwon Suh'] | 2022-11-23 | null | null | null | null | ['arrhythmia-detection'] | ['medical'] | [-1.00895219e-01 -4.01574403e-01 4.03914154e-02 -2.99473822e-01
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7e9e2d92-ad4f-471d-bfc3-4e64305bfe3d | joint-path-planning-and-power-allocation-of-a | 2306.10071 | null | https://arxiv.org/abs/2306.10071v1 | https://arxiv.org/pdf/2306.10071v1.pdf | Joint Path planning and Power Allocation of a Cellular-Connected UAV using Apprenticeship Learning via Deep Inverse Reinforcement Learning | This paper investigates an interference-aware joint path planning and power allocation mechanism for a cellular-connected unmanned aerial vehicle (UAV) in a sparse suburban environment. The UAV's goal is to fly from an initial point and reach a destination point by moving along the cells to guarantee the required quality of service (QoS). In particular, the UAV aims to maximize its uplink throughput and minimize the level of interference to the ground user equipment (UEs) connected to the neighbor cellular BSs, considering the shortest path and flight resource limitation. Expert knowledge is used to experience the scenario and define the desired behavior for the sake of the agent (i.e., UAV) training. To solve the problem, an apprenticeship learning method is utilized via inverse reinforcement learning (IRL) based on both Q-learning and deep reinforcement learning (DRL). The performance of this method is compared to learning from a demonstration technique called behavioral cloning (BC) using a supervised learning approach. Simulation and numerical results show that the proposed approach can achieve expert-level performance. We also demonstrate that, unlike the BC technique, the performance of our proposed approach does not degrade in unseen situations. | ['Ismail Guvenc', 'Fatemeh Afghah', 'Sajad Mousavi', 'Fatemeh Lotfi', 'Alireza Shamsoshoara'] | 2023-06-15 | null | null | null | null | ['q-learning'] | ['methodology'] | [-6.46454468e-03 2.86284417e-01 -2.52952874e-01 3.73902112e-01
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23e68a2d-6e37-4a33-9ec8-32dee1dc43f0 | fisheyemodnet-moving-object-detection-on | 1908.11789 | null | https://arxiv.org/abs/1908.11789v1 | https://arxiv.org/pdf/1908.11789v1.pdf | FisheyeMODNet: Moving Object detection on Surround-view Cameras for Autonomous Driving | Moving Object Detection (MOD) is an important task for achieving robust autonomous driving. An autonomous vehicle has to estimate collision risk with other interacting objects in the environment and calculate an optional trajectory. Collision risk is typically higher for moving objects than static ones due to the need to estimate the future states and poses of the objects for decision making. This is particularly important for near-range objects around the vehicle which are typically detected by a fisheye surround-view system that captures a 360{\deg} view of the scene. In this work, we propose a CNN architecture for moving object detection using fisheye images that were captured in autonomous driving environment. As motion geometry is highly non-linear and unique for fisheye cameras, we will make an improved version of the current dataset public to encourage further research. To target embedded deployment, we design a lightweight encoder sharing weights across sequential images. The proposed network runs at 15 fps on a 1 teraflops automotive embedded system at accuracy of 40% IoU and 69.5% mIoU. | ['Senthil Yogamani', 'Varun Ravi Kumar', 'Marie Yahiaoui', 'Letizia Mariotti', 'Ian Clancy', 'Hazem Rashed', 'Lucie Yahiaoui', 'Ganesh Sistu'] | 2019-08-30 | null | null | null | null | ['moving-object-detection'] | ['computer-vision'] | [-2.55449444e-01 -4.50013578e-02 2.43076999e-02 -5.88877678e-01
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be8426aa-9225-45ce-a729-7f2907058516 | improving-robustness-of-semantic-segmentation | null | null | http://openaccess.thecvf.com//content/CVPR2023/html/Aakanksha_Improving_Robustness_of_Semantic_Segmentation_to_Motion-Blur_Using_Class-Centric_Augmentation_CVPR_2023_paper.html | http://openaccess.thecvf.com//content/CVPR2023/papers/Aakanksha_Improving_Robustness_of_Semantic_Segmentation_to_Motion-Blur_Using_Class-Centric_Augmentation_CVPR_2023_paper.pdf | Improving Robustness of Semantic Segmentation to Motion-Blur Using Class-Centric Augmentation | Semantic segmentation involves classifying each pixel into one of a pre-defined set of object/stuff classes. Such a fine-grained detection and localization of objects in the scene is challenging by itself. The complexity increases manifold in the presence of blur. With cameras becoming increasingly light-weight and compact, blur caused by motion during capture time has become unavoidable. Most research has focused on improving segmentation performance for sharp clean images and the few works that deal with degradations, consider motion-blur as one of many generic degradations. In this work, we focus exclusively on motion-blur and attempt to achieve robustness for semantic segmentation in its presence. Based on the observation that segmentation annotations can be used to generate synthetic space-variant blur, we propose a Class-Centric Motion-Blur Augmentation (CCMBA) strategy. Our approach involves randomly selecting a subset of semantic classes present in the image and using the segmentation map annotations to blur only the corresponding regions. This enables the network to simultaneously learn semantic segmentation for clean images, images with egomotion blur, as well as images with dynamic scene blur. We demonstrate the effectiveness of our approach for both CNN and Vision Transformer-based semantic segmentation networks on PASCAL VOC and Cityscapes datasets. We also illustrate the improved generalizability of our method to complex real-world blur by evaluating on the commonly used deblurring datasets GoPro and REDS. | ['A. N. Rajagopalan', 'Aakanksha'] | 2023-01-01 | null | null | null | cvpr-2023-1 | ['deblurring'] | ['computer-vision'] | [ 6.14043653e-01 -3.11540812e-01 3.06586117e-01 -2.66691715e-01
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f5bc0098-8e12-4a03-966c-9e200e10ea8f | polyphone-disambiguition-in-mandarin-chinese | 2102.00621 | null | https://arxiv.org/abs/2102.00621v2 | https://arxiv.org/pdf/2102.00621v2.pdf | Polyphone Disambiguition in Mandarin Chinese with Semi-Supervised Learning | The majority of Chinese characters are monophonic, while a special group of characters, called polyphonic characters, have multiple pronunciations. As a prerequisite of performing speech-related generative tasks, the correct pronunciation must be identified among several candidates. This process is called Polyphone Disambiguation. Although the problem has been well explored with both knowledge-based and learning-based approaches, it remains challenging due to the lack of publicly available labeled datasets and the irregular nature of polyphone in Mandarin Chinese. In this paper, we propose a novel semi-supervised learning (SSL) framework for Mandarin Chinese polyphone disambiguation that can potentially leverage unlimited unlabeled text data. We explore the effect of various proxy labeling strategies including entropy-thresholding and lexicon-based labeling. Qualitative and quantitative experiments demonstrate that our method achieves state-of-the-art performance. In addition, we publish a novel dataset specifically for the polyphone disambiguation task to promote further researches. | ['Bin Wang', 'Yu Chen', 'Congyi Wang', 'Yi Shi'] | 2021-02-01 | null | null | null | null | ['polyphone-disambiguation'] | ['natural-language-processing'] | [ 2.53499299e-01 -2.22009078e-01 -4.69026476e-01 -2.72461265e-01
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8a3e33f6-8575-4d9d-a2b4-a2d0f35f2ed4 | where-a-strong-backbone-meets-strong-features | 2211.09074 | null | https://arxiv.org/abs/2211.09074v1 | https://arxiv.org/pdf/2211.09074v1.pdf | Where a Strong Backbone Meets Strong Features -- ActionFormer for Ego4D Moment Queries Challenge | This report describes our submission to the Ego4D Moment Queries Challenge 2022. Our submission builds on ActionFormer, the state-of-the-art backbone for temporal action localization, and a trio of strong video features from SlowFast, Omnivore and EgoVLP. Our solution is ranked 2nd on the public leaderboard with 21.76% average mAP on the test set, which is nearly three times higher than the official baseline. Further, we obtain 42.54% Recall@1x at tIoU=0.5 on the test set, outperforming the top-ranked solution by a significant margin of 1.41 absolute percentage points. Our code is available at https://github.com/happyharrycn/actionformer_release. | ['Yin Li', 'Gillian Wang', 'Sicheng Mo', 'Fangzhou Mu'] | 2022-11-16 | null | null | null | null | ['action-localization'] | ['computer-vision'] | [-5.18986106e-01 -1.59878612e-01 -7.68295169e-01 -1.05069153e-01
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a8047406-a692-4426-8937-7ebfb1ac8a73 | do-we-need-to-penalize-variance-of-losses-for | 2201.12739 | null | https://arxiv.org/abs/2201.12739v1 | https://arxiv.org/pdf/2201.12739v1.pdf | Do We Need to Penalize Variance of Losses for Learning with Label Noise? | Algorithms which minimize the averaged loss have been widely designed for dealing with noisy labels. Intuitively, when there is a finite training sample, penalizing the variance of losses will improve the stability and generalization of the algorithms. Interestingly, we found that the variance should be increased for the problem of learning with noisy labels. Specifically, increasing the variance will boost the memorization effects and reduce the harmfulness of incorrect labels. By exploiting the label noise transition matrix, regularizers can be easily designed to reduce the variance of losses and be plugged in many existing algorithms. Empirically, the proposed method by increasing the variance of losses significantly improves the generalization ability of baselines on both synthetic and real-world datasets. | ['Tongliang Liu', 'Mingming Gong', 'Bo Han', 'Jun Yu', 'Yuxuan Du', 'Yu Yao', 'Yexiong Lin'] | 2022-01-30 | null | null | null | null | ['learning-with-noisy-labels', 'learning-with-noisy-labels'] | ['computer-vision', 'natural-language-processing'] | [ 2.16452137e-01 3.20450403e-02 -3.03294390e-01 -6.44796848e-01
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647eda30-9461-460d-a386-f91b530301a0 | consciousness-is-learning-predictive | 2301.07016 | null | https://arxiv.org/abs/2301.07016v2 | https://arxiv.org/pdf/2301.07016v2.pdf | Consciousness is learning: predictive processing systems that learn by binding may perceive themselves as conscious | Machine learning algorithms have achieved superhuman performance in specific complex domains. Yet learning online from few examples and efficiently generalizing across domains remains elusive. In humans such learning proceeds via declarative memory formation and is closely associated with consciousness. Predictive processing has been advanced as a principled Bayesian inference framework for understanding the cortex as implementing deep generative perceptual models for both sensory data and action control. However, predictive processing offers little direct insight into fast compositional learning or the mystery of consciousness. Here we propose that through implementing online learning by hierarchical binding of unpredicted inferences, a predictive processing system may flexibly generalize in novel situations by forming working memories for perceptions and actions from single examples, which can become short- and long-term declarative memories retrievable by associative recall. We argue that the contents of such working memories are unified yet differentiated, can be maintained by selective attention and are consistent with observations of masking, postdictive perceptual integration, and other paradigm cases of consciousness research. We describe how the brain could have evolved to use perceptual value prediction for reinforcement learning of complex action policies simultaneously implementing multiple survival and reproduction strategies. 'Conscious experience' is how such a learning system perceptually represents its own functioning, suggesting an answer to the meta problem of consciousness. Our proposal naturally unifies feature binding, recurrent processing, and predictive processing with global workspace, and, to a lesser extent, the higher order theories of consciousness. | ['V. A. Aksyuk'] | 2023-01-17 | null | null | null | null | ['value-prediction'] | ['computer-code'] | [ 2.89291978e-01 1.72713697e-01 2.84118533e-01 -3.15489829e-01
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9422bd8d-2790-458d-8b87-6af58006821e | pvp-pre-trained-visual-parameter-efficient | 2304.13639 | null | https://arxiv.org/abs/2304.13639v1 | https://arxiv.org/pdf/2304.13639v1.pdf | PVP: Pre-trained Visual Parameter-Efficient Tuning | Large-scale pre-trained transformers have demonstrated remarkable success in various computer vision tasks. However, it is still highly challenging to fully fine-tune these models for downstream tasks due to their high computational and storage costs. Recently, Parameter-Efficient Tuning (PETuning) techniques, e.g., Visual Prompt Tuning (VPT) and Low-Rank Adaptation (LoRA), have significantly reduced the computation and storage cost by inserting lightweight prompt modules into the pre-trained models and tuning these prompt modules with a small number of trainable parameters, while keeping the transformer backbone frozen. Although only a few parameters need to be adjusted, most PETuning methods still require a significant amount of downstream task training data to achieve good results. The performance is inadequate on low-data regimes, especially when there are only one or two examples per class. To this end, we first empirically identify the poor performance is mainly due to the inappropriate way of initializing prompt modules, which has also been verified in the pre-trained language models. Next, we propose a Pre-trained Visual Parameter-efficient (PVP) Tuning framework, which pre-trains the parameter-efficient tuning modules first and then leverages the pre-trained modules along with the pre-trained transformer backbone to perform parameter-efficient tuning on downstream tasks. Experiment results on five Fine-Grained Visual Classification (FGVC) and VTAB-1k datasets demonstrate that our proposed method significantly outperforms state-of-the-art PETuning methods. | ['Qingyong Hu', 'Peng Qiao', 'Junjie Zhu', 'Naiyang Guan', 'Ke Yang', 'Zhao Song'] | 2023-04-26 | null | null | null | null | ['fine-grained-image-classification'] | ['computer-vision'] | [ 2.33125743e-02 -3.27675283e-01 -1.85423374e-01 -2.40887225e-01
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55d55862-6830-4a31-849e-8d2edbdf8a2b | global-structure-knowledge-guided-relation | 2305.1385 | null | https://arxiv.org/abs/2305.13850v1 | https://arxiv.org/pdf/2305.13850v1.pdf | Global Structure Knowledge-Guided Relation Extraction Method for Visually-Rich Document | Visual relation extraction (VRE) aims to extract relations between entities from visuallyrich documents. Existing methods usually predict relations for each entity pair independently based on entity features but ignore the global structure information, i.e., dependencies between entity pairs. The absence of global structure information may make the model struggle to learn long-range relations and easily predict conflicted results. To alleviate such limitations, we propose a GlObal Structure knowledgeguided relation Extraction (GOSE) framework, which captures dependencies between entity pairs in an iterative manner. Given a scanned image of the document, GOSE firstly generates preliminary relation predictions on entity pairs. Secondly, it mines global structure knowledge based on prediction results of the previous iteration and further incorporates global structure knowledge into entity representations. This "generate-capture-incorporate" schema is performed multiple times so that entity representations and global structure knowledge can mutually reinforce each other. Extensive experiments show that GOSE not only outperforms previous methods on the standard fine-tuning setting but also shows promising superiority in cross-lingual learning; even yields stronger data-efficient performance in the low-resource setting. | ['Siliang Tang', 'Xiaozhong Liu', 'Jun Lin', 'Qian Xiao', 'Duo Dong', 'Juncheng Li', 'Xiangnan Chen'] | 2023-05-23 | null | null | null | null | ['relation-extraction'] | ['natural-language-processing'] | [-1.08712256e-01 4.16886449e-01 -5.95220566e-01 -4.00334358e-01
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f93359b1-027c-4f9d-8485-2586493c459f | non-contrastive-self-supervised-learning-for | 2208.05445 | null | https://arxiv.org/abs/2208.05445v1 | https://arxiv.org/pdf/2208.05445v1.pdf | Non-Contrastive Self-supervised Learning for Utterance-Level Information Extraction from Speech | In recent studies, self-supervised pre-trained models tend to outperform supervised pre-trained models in transfer learning. In particular, self-supervised learning (SSL) of utterance-level speech representation can be used in speech applications that require discriminative representation of consistent attributes within an utterance: speaker, language, emotion, and age. Existing frame-level self-supervised speech representation, e.g., wav2vec, can be used as utterance-level representation with pooling, but the models are usually large. There are also SSL techniques to learn utterance-level representation. One of the most successful is a contrastive method, which requires negative sampling: selecting alternative samples to contrast with the current sample (anchor). However, this does not ensure that all the negative samples belong to classes different from the anchor class without labels. This paper applies a non-contrastive self-supervised method to learn utterance-level embeddings. We adapted DIstillation with NO labels (DINO) from computer vision to speech. Unlike contrastive methods, DINO does not require negative sampling. We compared DINO to x-vector trained in a supervised manner. When transferred to down-stream tasks (speaker verification, speech emotion recognition (SER), and Alzheimer's disease detection), DINO outperformed x-vector. We studied the influence of several aspects during transfer learning such as dividing the fine-tuning process into steps, chunk lengths, or augmentation. During fine-tuning, tuning the last affine layers first and then the whole network surpassed fine-tuning all at once. Using shorter chunk lengths, although they generate more diverse inputs, did not necessarily improve performance, implying speech segments at least with a specific length are required for better performance per application. Augmentation was helpful in SER. | ['Najim Dehak', 'Laureano Moro-Velazquez', "Jes'us Villalba", 'Jaejin Cho'] | 2022-08-10 | null | null | null | null | ['alzheimer-s-disease-detection'] | ['medical'] | [ 2.33240187e-01 2.64639050e-01 -1.77083790e-01 -7.96267092e-01
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a3e2e822-bd8b-4754-bda2-eb8d59be5d3a | shuffle-transformer-with-feature-alignment | 2106.0865 | null | https://arxiv.org/abs/2106.08650v1 | https://arxiv.org/pdf/2106.08650v1.pdf | Shuffle Transformer with Feature Alignment for Video Face Parsing | This is a short technical report introducing the solution of the Team TCParser for Short-video Face Parsing Track of The 3rd Person in Context (PIC) Workshop and Challenge at CVPR 2021. In this paper, we introduce a strong backbone which is cross-window based Shuffle Transformer for presenting accurate face parsing representation. To further obtain the finer segmentation results, especially on the edges, we introduce a Feature Alignment Aggregation (FAA) module. It can effectively relieve the feature misalignment issue caused by multi-resolution feature aggregation. Benefiting from the stronger backbone and better feature aggregation, the proposed method achieves 86.9519% score in the Short-video Face Parsing track of the 3rd Person in Context (PIC) Workshop and Challenge, ranked the first place. | ['Bin Fu', 'Gang Yu', 'Guozhong Luo', 'Pei Cheng', 'Zilong Huang', 'Yang Han', 'Rui Zhang'] | 2021-06-16 | null | null | null | null | ['face-parsing'] | ['computer-vision'] | [ 1.44007534e-01 1.72063380e-01 1.74122006e-01 -6.66381717e-01
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875c5b6e-9857-4cb3-b2cb-044350b80109 | rng-kbqa-generation-augmented-iterative-1 | null | null | https://openreview.net/forum?id=xEWyJkPuZlI | https://openreview.net/pdf?id=xEWyJkPuZlI | RNG-KBQA: Generation Augmented Iterative Ranking for Knowledge Base Question Answering | Existing KBQA approaches, despite achieving strong performance on i.i.d. test data, often struggle in generalizing to questions involving unseen KB schema items. Prior ranking-based approaches have shown some success in generalization, but suffer from the coverage issue. We present RnG-KBQA, a Rank-and-Generate approach for KBQA, which remedies the coverage issue with a generation model while preserving a strong generalization capability. Our approach first uses a contrastive ranker to rank a set of candidate logical forms obtained by searching over the knowledge graph. It then introduces a tailored generation model conditioned on the question and the top-ranked candidates to compose the final logical form. We achieve new state-of-the-art results on GrailQA and WebQSP datasets. In particular, our method surpasses the prior state-of-the-art by a large margin on the GrailQA leaderboard. In addition, RnG-KBQA outperforms all prior approaches on the popular WebQSP benchmark, even including the ones that use the oracle entity linking. The experimental results demonstrate the effectiveness of the interplay between ranking and generation, which leads to the superior performance of our proposed approach across all settings with especially strong improvements in zero-shot generalization. | ['Anonymous'] | 2021-11-16 | null | null | null | acl-arr-november-2021-11 | ['knowledge-base-question-answering'] | ['natural-language-processing'] | [-1.61560193e-01 1.65286317e-01 -2.91039258e-01 -4.31031048e-01
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75b5018f-857e-4c8f-9315-419fd03eac65 | webly-supervised-concept-expansion-for | 2202.02317 | null | https://arxiv.org/abs/2202.02317v2 | https://arxiv.org/pdf/2202.02317v2.pdf | Webly Supervised Concept Expansion for General Purpose Vision Models | General Purpose Vision (GPV) systems are models that are designed to solve a wide array of visual tasks without requiring architectural changes. Today, GPVs primarily learn both skills and concepts from large fully supervised datasets. Scaling GPVs to tens of thousands of concepts by acquiring data to learn each concept for every skill quickly becomes prohibitive. This work presents an effective and inexpensive alternative: learn skills from supervised datasets, learn concepts from web image search, and leverage a key characteristic of GPVs: the ability to transfer visual knowledge across skills. We use a dataset of 1M+ images spanning 10k+ visual concepts to demonstrate webly-supervised concept expansion for two existing GPVs (GPV-1 and VL-T5) on 3 benchmarks: 5 COCO-based datasets (80 primary concepts), a newly curated series of 5 datasets based on the OpenImages and VisualGenome repositories (~500 concepts), and the Web-derived dataset (10k+ concepts). We also propose a new architecture, GPV-2 that supports a variety of tasks -- from vision tasks like classification and localization to vision+language tasks like QA and captioning, to more niche ones like human-object interaction detection. GPV-2 benefits hugely from web data and outperforms GPV-1 and VL-T5 across these benchmarks. Our data, code, and web demo are available at https://prior.allenai.org/projects/gpv2. | ['Aniruddha Kembhavi', 'Derek Hoiem', 'Eric Kolve', 'Tanmay Gupta', 'Christopher Clark', 'Amita Kamath'] | 2022-02-04 | null | null | null | null | ['object-categorization'] | ['computer-vision'] | [ 4.20825146e-02 -2.24168584e-01 -7.44092464e-02 -3.17938536e-01
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30245b85-4997-4bba-a0ff-18cde9c27fea | embodied-question-answering | 1711.11543 | null | http://arxiv.org/abs/1711.11543v2 | http://arxiv.org/pdf/1711.11543v2.pdf | Embodied Question Answering | We present a new AI task -- Embodied Question Answering (EmbodiedQA) -- where
an agent is spawned at a random location in a 3D environment and asked a
question ("What color is the car?"). In order to answer, the agent must first
intelligently navigate to explore the environment, gather information through
first-person (egocentric) vision, and then answer the question ("orange").
This challenging task requires a range of AI skills -- active perception,
language understanding, goal-driven navigation, commonsense reasoning, and
grounding of language into actions. In this work, we develop the environments,
end-to-end-trained reinforcement learning agents, and evaluation protocols for
EmbodiedQA. | ['Stefan Lee', 'Samyak Datta', 'Dhruv Batra', 'Georgia Gkioxari', 'Devi Parikh', 'Abhishek Das'] | 2017-11-30 | embodied-question-answering-1 | http://openaccess.thecvf.com/content_cvpr_2018/html/Das_Embodied_Question_Answering_CVPR_2018_paper.html | http://openaccess.thecvf.com/content_cvpr_2018/papers/Das_Embodied_Question_Answering_CVPR_2018_paper.pdf | cvpr-2018-6 | ['embodied-question-answering'] | ['computer-vision'] | [ 2.79724032e-01 4.82133687e-01 6.12340391e-01 -4.17896658e-01
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11905af4-357a-4c8c-b65d-8c9e56526e77 | a-deep-tree-structured-fusion-model-for | 1811.08632 | null | http://arxiv.org/abs/1811.08632v1 | http://arxiv.org/pdf/1811.08632v1.pdf | A Deep Tree-Structured Fusion Model for Single Image Deraining | We propose a simple yet effective deep tree-structured fusion model based on
feature aggregation for the deraining problem. We argue that by effectively
aggregating features, a relatively simple network can still handle tough image
deraining problems well. First, to capture the spatial structure of rain we use
dilated convolutions as our basic network block. We then design a
tree-structured fusion architecture which is deployed within each block
(spatial information) and across all blocks (content information). Our method
is based on the assumption that adjacent features contain redundant
information. This redundancy obstructs generation of new representations and
can be reduced by hierarchically fusing adjacent features. Thus, the proposed
model is more compact and can effectively use spatial and content information.
Experiments on synthetic and real-world datasets show that our network achieves
better deraining results with fewer parameters. | ['John Paisley', 'Feng Wu', 'Xinghao Ding', 'Xueyang Fu', 'Qi Qi', 'Yue Huang'] | 2018-11-21 | null | null | null | null | ['single-image-deraining'] | ['computer-vision'] | [ 2.17119735e-02 -8.09070021e-02 1.80062205e-01 -4.57748741e-01
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8.50622833e-01 -6.99068546e-01 -3.44199955e-01 -1.50335327e-01] | [10.933924674987793, -2.988527536392212] |
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