cs.AI - 人工智能 cs.CC - 计算复杂度 cs.CL - 计算与语言 cs.CR - 加密与安全 cs.CV - 机器视觉与模式识别 cs.CY - 计算与社会 cs.DC - 分布式、并行与集群计算 cs.DL - 数字图书馆 cs.DS - 数据结构与算法 cs.HC - 人机接口 cs.IR - 信息检索 cs.IT - 信息论 cs.LG - 自动学习 cs.NE - 神经与进化计算 cs.NI - 网络和互联网体系结构 cs.RO - 机器人学 cs.SD - 声音处理 cs.SE - 软件工程 cs.SI - 社交网络与信息网络 eess.AS - 语音处理 eess.IV - 图像与视频处理 eess.SP - 信号处理 eess.SY - 系统和控制 math.OC - 优化与控制 math.ST - 统计理论 q-bio.NC - 神经元与认知 q-bio.QM - 定量方法 quant-ph - 量子物理 stat.AP - 应用统计 stat.ME - 统计方法论 stat.ML - (统计)机器学习
• [cs.AI]DeepEnroll: Patient-Trial Matching with Deep Embedding and Entailment Prediction
• [cs.AI]GLIB: Exploration via Goal-Literal Babbling for Lifted Operator Learning
• [cs.AI]I Feel I Feel You: A Theory of Mind Experiment in Games
• [cs.AI]Learning Distributional Programs for Relational Autocompletion
• [cs.AI]Model-theoretic Characterizations of Existential Rule Languages
• [cs.AI]Numerical Abstract Persuasion Argumentation for Expressing Concurrent Multi-Agent Negotiations
• [cs.AI]Proxy Tasks and Subjective Measures Can Be Misleading in Evaluating Explainable AI Systems
• [cs.CC]On the computational power and complexity of Spiking Neural Networks
• [cs.CL]A Study of the Tasks and Models in Machine Reading Comprehension
• [cs.CL]Action Recognition and State Change Prediction in a Recipe Understanding Task Using a Lightweight Neural Network Model
• [cs.CL]CheckThat! at CLEF 2020: Enabling the Automatic Identification and Verification of Claims in Social Media
• [cs.CL]Coordinated Reasoning for Cross-Lingual Knowledge Graph Alignment
• [cs.CL]Multilingual Denoising Pre-training for Neural Machine Translation
• [cs.CL]Pre-training via Leveraging Assisting Languages and Data Selection for Neural Machine Translation
• [cs.CL]Transition-Based Dependency Parsing using Perceptron Learner
• [cs.CL]Variational Hierarchical Dialog Autoencoder for Dialogue State Tracking Data Augmentation
• [cs.CR]Information set decoding of Lee-metric codes over finite rings
• [cs.CR]Nowhere to Hide: Cross-modal Identity Leakage between Biometrics and Devices
• [cs.CR]Sensor-based Continuous Authentication of Smartphones’ Users Using Behavioral Biometrics: A Survey
• [cs.CV]A Hypersensitive Breast Cancer Detector
• [cs.CV]A Large Scale Event-based Detection Dataset for Automotive
• [cs.CV]Active Perception with A Monocular Camera for Multiscopic Vision
• [cs.CV]Adaptation of a deep learning malignancy model from full-field digital mammography to digital breast tomosynthesis
• [cs.CV]Audiovisual SlowFast Networks for Video Recognition
• [cs.CV]Channel Pruning via Automatic Structure Search
• [cs.CV]Continual Local Replacement for Few-shot Image Recognition
• [cs.CV]Cross-Domain Few-Shot Classification via Learned Feature-Wise Transformation
• [cs.CV]Deformation-aware Unpaired Image Translation for Pose Estimation on Laboratory Animals
• [cs.CV]Detecting Deficient Coverage in Colonoscopies
• [cs.CV]Disassembling the Dataset: A Camera Alignment Mechanism for Multiple Tasks in Person Re-identification
• [cs.CV]Filter Sketch for Network Pruning
• [cs.CV]How Much Position Information Do Convolutional Neural Networks Encode?
• [cs.CV]ImageBERT: Cross-modal Pre-training with Large-scale Weak-supervised Image-Text Data
• [cs.CV]Learning to adapt class-specific features across domains for semantic segmentation
• [cs.CV]Lipreading using Temporal Convolutional Networks
• [cs.CV]Observer variation-aware medical image segmentation by combining deep learning and surrogate-assisted genetic algorithms
• [cs.CV]PENet: Object Detection using Points Estimation in Aerial Images
• [cs.CV]Partially-Shared Variational Auto-encoders for Unsupervised Domain Adaptation with Target Shift
• [cs.CV]Robust Explanations for Visual Question Answering
• [cs.CV]Semi-DerainGAN: A New Semi-supervised Single Image Deraining Network
• [cs.CV]Ternary Feature Masks: continual learning without any forgetting
• [cs.CV]Weakly-Supervised Lesion Segmentation on CT Scans using Co-Segmentation
• [cs.CY]ClassCode: An Interactive Teaching and Learning Environment for Programming Education in Classrooms
• [cs.CY]Online Abuse toward Candidates during the UK General Election 2019: Working Paper
• [cs.CY]Quantifying Engagement with Citations on Wikipedia
• [cs.CY]Understanding the Incel Community on YouTube
• [cs.DC]A Closer Look at Lightweight Graph Reordering
• [cs.DC]CEFIoT: A Fault-Tolerant IoT Architecture for Edge and Cloud
• [cs.DC]Coded Computing for Boolean Functions
• [cs.DC]Communication-Efficient String Sorting
• [cs.DL]Referencing Source Code Artifacts: a Separate Concern in Software Citation
• [cs.DS]Bibliography of distributed approximation on structurally sparse graph classes
• [cs.HC]Facial Feedback for Reinforcement Learning: A Case Study and Offline Analysis Using the TAMER Framework
• [cs.IR]EventMapper: Detecting Real-World Physical Events Using Corroborative and Probabilistic Sources
• [cs.IR]Experiments on Manual Thesaurus based Query Expansion for Ad-hoc Monolingual Gujarati Information Retrieval Tasks
• [cs.IR]Navigation-Based Candidate Expansion and Pretrained Language Models for Citation Recommendation
• [cs.IT]$O(\log \log n)$ Worst-Case Local Decoding and Update Efficiency for Data Compression
• [cs.IT]A Signal-Space Distance Measure for Nondispersive Optical Fiber
• [cs.IT]Communication Efficient Federated Learning over Multiple Access Channels
• [cs.IT]Convolutional Codes
• [cs.IT]Receive Antenna Selection for Secure Pre-coding Aided Spatial Modulation
• [cs.IT]The Capacity of Single-Server Weakly-Private Information Retrieval
• [cs.LG]A Deep Learning Approach to Behavior-Based Learner Modeling
• [cs.LG]Ada-LISTA: Learned Solvers Adaptive to Varying Models
• [cs.LG]Applying Recent Innovations from NLP to MOOC Student Course Trajectory Modeling
• [cs.LG]BLK-REW: A Unified Block-based DNN Pruning Framework using Reweighted Regularization Method
• [cs.LG]Best Arm Identification for Cascading Bandits in the Fixed Confidence Setting
• [cs.LG]Compositional properties of emergent languages in deep learning
• [cs.LG]Data Selection for Federated Learning with Relevant and Irrelevant Data at Clients
• [cs.LG]Deep Transformer Models for Time Series Forecasting: The Influenza Prevalence Case
• [cs.LG]Expected Information Maximization: Using the I-Projection for Mixture Density Estimation
• [cs.LG]From abstract items to latent spaces to observed data and back: Compositional Variational Auto-Encoder
• [cs.LG]FsNet: Feature Selection Network on High-dimensional Biological Data
• [cs.LG]Information Compensation for Deep Conditional Generative Networks
• [cs.LG]Intelligent Chest X-ray Worklist Prioritization by CNNs: A Clinical Workflow Simulation
• [cs.LG]Intermittent Pulling with Local Compensation for Communication-Efficient Federated Learning
• [cs.LG]Low-Complexity LSTM Training and Inference with FloatSD8 Weight Representation
• [cs.LG]Massif: Interactive Interpretation of Adversarial Attacks on Deep Learning
• [cs.LG]Multi-objective Neural Architecture Search via Non-stationary Policy Gradient
• [cs.LG]RPN: A Residual Pooling Network for Efficient Federated Learning
• [cs.LG]Random Matrix Theory Proves that Deep Learning Representations of GAN-data Behave as Gaussian Mixtures
• [cs.LG]Representation Learning for Medical Data
• [cs.LG]Scaling Laws for Neural Language Models
• [cs.LG]Semi-supervised Grasp Detection by Representation Learning in a Vector Quantized Latent Space
• [cs.LG]Stacked Boosters Network Architecture for Short Term Load Forecasting in Buildings
• [cs.LG]Structured Compression and Sharing of Representational Space for Continual Learning
• [cs.LG]Towards A Controllable Disentanglement Network
• [cs.LG]Towards Automatic Clustering Analysis using Traces of Information Gain: The InfoGuide Method
• [cs.LG]Towards Robust DNNs: An Taylor Expansion-Based Method for Generating Powerful Adversarial Examples
• [cs.LG]Visual Summary of Value-level Feature Attribution in Prediction Classes with Recurrent Neural Networks
• [cs.NE]DCT-Conv: Coding filters in convolutional networks with Discrete Cosine Transform
• [cs.NI]Synchronous Transmissions in Low-Power Wireless: A Survey of Communication Protocols and Network Services
• [cs.RO]2D-VSR-Sim: an Optimization-friendly Simulator of 2-D Voxel-based Soft Robots
• [cs.RO]A Probabilistic Framework for Imitating Human Race Driver Behavior
• [cs.RO]Automatic Differentiation and Continuous Sensitivity Analysis of Rigid Body Dynamics
• [cs.RO]Fast, Compact and Highly Scalable Visual Place Recognition through Sequence-based Matching of Overloaded Representations
• [cs.RO]Impact-aware humanoid robot motion generation with a quadratic optimization controller
• [cs.RO]Interpretable End-to-end Urban Autonomous Driving with Latent Deep Reinforcement Learning
• [cs.RO]Learning Object Placements For Relational Instructions by Hallucinating Scene Representations
• [cs.RO]Socially intelligent task and motion planning for human-robot interaction
• [cs.RO]Trajectory Planning for Connected and Automated Vehicles: Cruising, Lane Changing, and Platooning
• [cs.SD]The INTERSPEECH 2020 Deep Noise Suppression Challenge: Datasets, Subjective Speech Quality and Testing Framework
• [cs.SE]Search-Based Software Engineering for Self-Adaptive Systems: One Survey, Five Disappointments and Six Opportunities
• [cs.SI]Joint Inference on Truth/Rumor and Their Sources in Social Networks
• [cs.SI]Relational Thematic Clustering with Mutually Preferred Neighbors
• [cs.SI]The Pushshift Reddit Dataset
• [cs.SI]The Pushshift Telegram Dataset
• [eess.AS]Improving speaker discrimination of target speech extraction with time-domain SpeakerBeam
• [eess.AS]On the human evaluation of audio adversarial examples
• [eess.IV]A One-Shot Learning Framework for Assessment of Fibrillar Collagen from Second Harmonic Generation Images of an Infarcted Myocardium
• [eess.IV]A multi-site study of a breast density deep learning model for full-field digital mammography and digital breast tomosynthesis exams
• [eess.IV]CNN-CASS: CNN for Classification of Coronary Artery Stenosis Score in MPR Images
• [eess.IV]MRI Banding Removal via Adversarial Training
• [eess.IV]Segmentation of Retinal Low-Cost Optical Coherence Tomography Images using Deep Learning
• [eess.IV]Tensor-Based Grading: A Novel Patch-Based Grading Approach for the Analysis of Deformation Fields in Huntington’s Disease
• [eess.SP]A hemodynamic decomposition model for detecting cognitive load using functional near-infrared spectroscopy
• [eess.SP]Inference over Wireless IoT Links with Importance-Filtered Updates
• [eess.SP]Reconfigurable Intelligent Surface assisted Two-Way Communications: Performance Analysis and Optimization
• [eess.SY]Counter-example Guided Learning of Bounds on Environment Behavior
• [math.OC]Replica Exchange for Non-Convex Optimization
• [math.ST]A precise local limit theorem for the multinomial distribution
• [math.ST]Geometric Conditions for the Discrepant Posterior Phenomenon and Connections to Simpson’s Paradox
• [q-bio.NC]Towards naturalistic human neuroscience and neuroengineering: behavior mining in long-term video and neural recordings
• [q-bio.QM]Deep learning-based prediction of response to HER2-targeted neoadjuvant chemotherapy from pre-treatment dynamic breast MRI: A multi-institutional validation study
• [quant-ph]On lower semicontinuity of the quantum conditional mutual information and its corollaries
• [stat.AP]A covariance-enhanced approach to multi-tissue joint eQTL mapping with application to transcriptome-wide association studies
• [stat.AP]Bayesian estimates of transmission line outage rates that consider line dependencies
• [stat.AP]Statistical post-processing of heat index ensemble forecasts: is there a royal road?
• [stat.ME]Maximum Likelihood Estimation of Spatially Varying Coefficient Models for Large Data with an Application to Real Estate Price Prediction
• [stat.ME]On the Hauck-Donner Effect in Wald Tests: Detection, Tipping Points, and Parameter Space Characterization
• [stat.ME]Shrinkage with Robustness: Log-Adjusted Priors for Sparse Signals
• [stat.ME]The Reciprocal Bayesian LASSO
• [stat.ML]A Bayesian Long Short-Term Memory Model for Value at Risk and Expected Shortfall Joint Forecasting
• [stat.ML]A Multi-Scale Tensor Network Architecture for Classification and Regression
• [stat.ML]Best Principal Submatrix Selection for the Maximum Entropy Sampling Problem: Scalable Algorithms and Performance Guarantees
• [stat.ML]Linking Bank Clients using Graph Neural Networks Powered by Rich Transactional Data
• [stat.ML]Stratified cross-validation for unbiased and privacy-preserving federated learning
• [stat.ML]Target-Embedding Autoencoders for Supervised Representation Learning
·····································
• [cs.AI]DeepEnroll: Patient-Trial Matching with Deep Embedding and Entailment Prediction
Xingyao Zhang, Cao Xiao, Lucas M. Glass, Jimeng Sun
http://arxiv.org/abs/2001.08179v2
• [cs.AI]GLIB: Exploration via Goal-Literal Babbling for Lifted Operator Learning
Rohan Chitnis, Tom Silver, Joshua Tenenbaum, Leslie Pack Kaelbling, Tomas Lozano-Perez
http://arxiv.org/abs/2001.08299v1
• [cs.AI]I Feel I Feel You: A Theory of Mind Experiment in Games
David Melhart, Georgios N. Yannakakis, Antonios Liapis
http://arxiv.org/abs/2001.08656v1
• [cs.AI]Learning Distributional Programs for Relational Autocompletion
Kumar Nitesh, Kuzelka Ondrej, De Raedt Luc
http://arxiv.org/abs/2001.08603v1
• [cs.AI]Model-theoretic Characterizations of Existential Rule Languages
Heng Zhang, Yan Zhang, Guifei Jiang
http://arxiv.org/abs/2001.08688v1
• [cs.AI]Numerical Abstract Persuasion Argumentation for Expressing Concurrent Multi-Agent Negotiations
Ryuta Arisaka, Takayuki Ito
http://arxiv.org/abs/2001.08335v1
• [cs.AI]Proxy Tasks and Subjective Measures Can Be Misleading in Evaluating Explainable AI Systems
Zana Buçinca, Phoebe Lin, Krzysztof Z. Gajos, Elena L. Glassman
http://arxiv.org/abs/2001.08298v1
• [cs.CC]On the computational power and complexity of Spiking Neural Networks
Johan Kwisthout, Nils Donselaar
http://arxiv.org/abs/2001.08439v1
• [cs.CL]A Study of the Tasks and Models in Machine Reading Comprehension
Chao Wang
http://arxiv.org/abs/2001.08635v1
• [cs.CL]Action Recognition and State Change Prediction in a Recipe Understanding Task Using a Lightweight Neural Network Model
Qing Wan, Yoonsuck Choe
http://arxiv.org/abs/2001.08665v1
• [cs.CL]CheckThat! at CLEF 2020: Enabling the Automatic Identification and Verification of Claims in Social Media
Alberto Barron-Cedeno, Tamer Elsayed, Preslav Nakov, Giovanni Da San Martino, Maram Hasanain, Reem Suwaileh, Fatima Haouari
http://arxiv.org/abs/2001.08546v1
• [cs.CL]Coordinated Reasoning for Cross-Lingual Knowledge Graph Alignment
Kun Xu, Linfeng Song, Yansong Feng, Yan Song, Dong Yu
http://arxiv.org/abs/2001.08728v1
• [cs.CL]Multilingual Denoising Pre-training for Neural Machine Translation
Yinhan Liu, Jiatao Gu, Naman Goyal, Xian Li, Sergey Edunov, Marjan Ghazvininejad, Mike Lewis, Luke Zettlemoyer
http://arxiv.org/abs/2001.08210v2
• [cs.CL]Pre-training via Leveraging Assisting Languages and Data Selection for Neural Machine Translation
Haiyue Song, Raj Dabre, Zhuoyuan Mao, Fei Cheng, Sadao Kurohashi, Eiichiro Sumita
http://arxiv.org/abs/2001.08353v1
• [cs.CL]Transition-Based Dependency Parsing using Perceptron Learner
Rahul Radhakrishnan Iyer, Miguel Ballesteros, Chris Dyer, Robert Frederking
http://arxiv.org/abs/2001.08279v1
• [cs.CL]Variational Hierarchical Dialog Autoencoder for Dialogue State Tracking Data Augmentation
Kang Min Yoo, Hanbit Lee, Franck Dernoncourt, Trung Bui, Walter Chang, Sang-goo Lee
http://arxiv.org/abs/2001.08604v1
• [cs.CR]Information set decoding of Lee-metric codes over finite rings
Violetta Weger, Massimo Battaglioni, Paolo Santini, Franco Chiaraluce, Marco Baldi, Edoardo Persichetti
http://arxiv.org/abs/2001.08425v1
• [cs.CR]Nowhere to Hide: Cross-modal Identity Leakage between Biometrics and Devices
Chris Xiaoxuan Lu, Yang Li, Yuanbo Xiangli, Zhengxiong Li
http://arxiv.org/abs/2001.08211v1
• [cs.CR]Sensor-based Continuous Authentication of Smartphones’ Users Using Behavioral Biometrics: A Survey
Mohammed Abuhamad, Ahmed Abusnaina, DaeHun Nyang, David Mohaisen
http://arxiv.org/abs/2001.08578v1
• [cs.CV]A Hypersensitive Breast Cancer Detector
Stefano Pedemonte, Brent Mombourquette, Alexis Goh, Trevor Tsue, Aaron Long, Sadanand Singh, Thomas Paul Matthews, Meet Shah, Jason Su
http://arxiv.org/abs/2001.08382v1
• [cs.CV]A Large Scale Event-based Detection Dataset for Automotive
Pierre de Tournemire. Davide Nitti, Etienne Perot, Davide Migliore, Amos Sironi
http://arxiv.org/abs/2001.08499v1
• [cs.CV]Active Perception with A Monocular Camera for Multiscopic Vision
Weihao Yuan, Rui Fan, Michael Yu Wang, Qifeng Chen
http://arxiv.org/abs/2001.08212v1
• [cs.CV]Adaptation of a deep learning malignancy model from full-field digital mammography to digital breast tomosynthesis
Sadanand Singh, Thomas Paul Matthews, Meet Shah, Brent Mombourquette, Trevor Tsue, Aaron Long, Ranya Almohsen, Stefano Pedemonte, Jason Su
http://arxiv.org/abs/2001.08381v1
• [cs.CV]Audiovisual SlowFast Networks for Video Recognition
Fanyi Xiao, Yong Jae Lee, Kristen Grauman, Jitendra Malik, Christoph Feichtenhofer
http://arxiv.org/abs/2001.08740v1
• [cs.CV]Channel Pruning via Automatic Structure Search
Mingbao Lin, Rongrong Ji, Yuxin Zhang, Baochang Zhang, Yongjian Wu, Yonghong Tian
http://arxiv.org/abs/2001.08565v1
• [cs.CV]Continual Local Replacement for Few-shot Image Recognition
Canyu Le, Zhonggui Chen, Xihan Wei, Biao Wang, Lei Zhang
http://arxiv.org/abs/2001.08366v1
• [cs.CV]Cross-Domain Few-Shot Classification via Learned Feature-Wise Transformation
Hung-Yu Tseng, Hsin-Ying Lee, Jia-Bin Huang, Ming-Hsuan Yang
http://arxiv.org/abs/2001.08735v1
• [cs.CV]Deformation-aware Unpaired Image Translation for Pose Estimation on Laboratory Animals
Siyuan Li, Semih Günel, Mirela Ostrek, Pavan Ramdya, Pascal Fua, Helge Rhodin
http://arxiv.org/abs/2001.08601v1
• [cs.CV]Detecting Deficient Coverage in Colonoscopies
Daniel Freedman, Yochai Blau, Liran Katzir, Amit Aides, Ilan Shimshoni, Danny Veikherman, Tomer Golany, Ariel Gordon, Greg Corrado, Yossi Matias, Ehud Rivlin
http://arxiv.org/abs/2001.08589v1
• [cs.CV]Disassembling the Dataset: A Camera Alignment Mechanism for Multiple Tasks in Person Re-identification
Zijie Zhuang, Longhui Wei, Lingxi Xie, Hengheng Zhang, Tianyu Zhang, Haozhe Wu, Haizhou Ai, Qi Tian
http://arxiv.org/abs/2001.08680v1
• [cs.CV]Filter Sketch for Network Pruning
Mingbao Lin, Rongrong Ji, Shaojie Li, Qixiang Ye, Yonghong Tian, Jianzhuang Liu, Qi Tian
http://arxiv.org/abs/2001.08514v1
• [cs.CV]How Much Position Information Do Convolutional Neural Networks Encode?
Md Amirul Islam, Sen Jia, Neil D. B. Bruce
http://arxiv.org/abs/2001.08248v1
• [cs.CV]ImageBERT: Cross-modal Pre-training with Large-scale Weak-supervised Image-Text Data
Di Qi, Lin Su, Jia Song, Edward Cui, Taroon Bharti, Arun Sacheti
http://arxiv.org/abs/2001.07966v2
• [cs.CV]Learning to adapt class-specific features across domains for semantic segmentation
Mikel Menta, Adriana Romero, Joost van de Weijer
http://arxiv.org/abs/2001.08311v1
• [cs.CV]Lipreading using Temporal Convolutional Networks
Brais Martinez, Pingchuan Ma, Stavros Petridis, Maja Pantic
http://arxiv.org/abs/2001.08702v1
• [cs.CV]Observer variation-aware medical image segmentation by combining deep learning and surrogate-assisted genetic algorithms
Arkadiy Dushatskiy, Adriënne M. Mendrik, Peter A. N. Bosman, Tanja Alderliesten
http://arxiv.org/abs/2001.08552v1
• [cs.CV]PENet: Object Detection using Points Estimation in Aerial Images
Ziyang Tang, Xiang Liu, Guangyu Shen, Baijian Yang
http://arxiv.org/abs/2001.08247v1
• [cs.CV]Partially-Shared Variational Auto-encoders for Unsupervised Domain Adaptation with Target Shift
Ryuhei Takahashi, Atsushi Hashimoto, Motoharu Sonogashira, Masaaki Iiyama
http://arxiv.org/abs/2001.07895v2
• [cs.CV]Robust Explanations for Visual Question Answering
Badri N. Patro, Shivansh Pate, Vinay P. Namboodiri
http://arxiv.org/abs/2001.08730v1
• [cs.CV]Semi-DerainGAN: A New Semi-supervised Single Image Deraining Network
Yanyan Wei, Zhao Zhang, Haijun Zhang, Jie Qin, Mingbo Zhao
http://arxiv.org/abs/2001.08388v1
• [cs.CV]Ternary Feature Masks: continual learning without any forgetting
Marc Masana, Tinne Tuytelaars, Joost van de Weijer
http://arxiv.org/abs/2001.08714v1
• [cs.CV]Weakly-Supervised Lesion Segmentation on CT Scans using Co-Segmentation
Vatsal Agarwal, Youbao Tang, Jing Xiao, Ronald M. Summers
http://arxiv.org/abs/2001.08590v1
• [cs.CY]ClassCode: An Interactive Teaching and Learning Environment for Programming Education in Classrooms
Ryo Suzuki, Jun Kato, Koji Yatani
http://arxiv.org/abs/2001.08194v1
• [cs.CY]Online Abuse toward Candidates during the UK General Election 2019: Working Paper
Genevieve Gorrell, Mehmet E Bakir, Ian Roberts, Mark A Greenwood, Kalina Bontcheva
http://arxiv.org/abs/2001.08686v1
• [cs.CY]Quantifying Engagement with Citations on Wikipedia
Tiziano Piccardi, Miriam Redi, Giovanni Colavizza, Robert West
http://arxiv.org/abs/2001.08614v1
• [cs.CY]Understanding the Incel Community on YouTube
Kostantinos Papadamou, Savvas Zannettou, Jeremy Blackburn, Emiliano De Cristofaro, Gianluca Stringhini, Michael Sirivianos
http://arxiv.org/abs/2001.08293v1
• [cs.DC]A Closer Look at Lightweight Graph Reordering
Priyank Faldu, Jeff Diamond, Boris Grot
http://arxiv.org/abs/2001.08448v1
• [cs.DC]CEFIoT: A Fault-Tolerant IoT Architecture for Edge and Cloud
Asad Javed, Keijo Heljanko, Andrea Buda, Kary Främling
http://arxiv.org/abs/2001.08433v1
• [cs.DC]Coded Computing for Boolean Functions
Chien-Sheng Yang, A. Salman Avestimehr
http://arxiv.org/abs/2001.08720v1
• [cs.DC]Communication-Efficient String Sorting
Timo Bingmann, Peter Sanders, Matthias Schimek
http://arxiv.org/abs/2001.08516v1
• [cs.DL]Referencing Source Code Artifacts: a Separate Concern in Software Citation
Roberto Di Cosmo, Morane Gruenpeter, Stefano Zacchiroli
http://arxiv.org/abs/2001.08647v1
• [cs.DS]Bibliography of distributed approximation on structurally sparse graph classes
Laurent Feuilloley
http://arxiv.org/abs/2001.08510v1
• [cs.HC]Facial Feedback for Reinforcement Learning: A Case Study and Offline Analysis Using the TAMER Framework
Guangliang Li, Hamdi Dibeklioğlu, Shimon Whiteson, Hayley Hung
http://arxiv.org/abs/2001.08703v1
• [cs.IR]EventMapper: Detecting Real-World Physical Events Using Corroborative and Probabilistic Sources
Abhijit Suprem, Calton Pu
http://arxiv.org/abs/2001.08700v1
• [cs.IR]Experiments on Manual Thesaurus based Query Expansion for Ad-hoc Monolingual Gujarati Information Retrieval Tasks
Hardik Joshi, Jyoti Pareek
http://arxiv.org/abs/2001.08085v1
• [cs.IR]Navigation-Based Candidate Expansion and Pretrained Language Models for Citation Recommendation
Rodrigo Nogueira, Zhiying Jiang, Kyunghyun Cho, Jimmy Lin
http://arxiv.org/abs/2001.08687v1
• [cs.IT]$O(\log \log n)$ Worst-Case Local Decoding and Update Efficiency for Data Compression
Shashank Vatedka, Venkat Chandar, Aslan Tchamkerten
http://arxiv.org/abs/2001.08679v1
• [cs.IT]A Signal-Space Distance Measure for Nondispersive Optical Fiber
Reza Rafie Borujeny, Frank R. Kschischang
http://arxiv.org/abs/2001.08663v1
• [cs.IT]Communication Efficient Federated Learning over Multiple Access Channels
Wei-Ting Chang, Ravi Tandon
http://arxiv.org/abs/2001.08737v1
• [cs.IT]Convolutional Codes
Julia Lieb, Raquel Pinto, Joachim Rosenthal
http://arxiv.org/abs/2001.08281v1
• [cs.IT]Receive Antenna Selection for Secure Pre-coding Aided Spatial Modulation
Lin Liu, Guiyang Xia, Jun Zou, Weibin Zhang, Feng Shu, Jiangzhou Wang
http://arxiv.org/abs/2001.08612v1
• [cs.IT]The Capacity of Single-Server Weakly-Private Information Retrieval
Hsuan-Yin Lin, Siddhartha Kumar, Eirik Rosnes, Alexandre Graell i Amat, Eitan Yaakobi
http://arxiv.org/abs/2001.08727v1
• [cs.LG]A Deep Learning Approach to Behavior-Based Learner Modeling
Yuwei Tu, Weiyu Chen, Christopher G. Brinton
http://arxiv.org/abs/2001.08328v1
• [cs.LG]Ada-LISTA: Learned Solvers Adaptive to Varying Models
Aviad Aberdam, Alona Golts, Michael Elad
http://arxiv.org/abs/2001.08456v1
• [cs.LG]Applying Recent Innovations from NLP to MOOC Student Course Trajectory Modeling
Clarence Chen, Zachary Pardos
http://arxiv.org/abs/2001.08333v1
• [cs.LG]BLK-REW: A Unified Block-based DNN Pruning Framework using Reweighted Regularization Method
Xiaolong Ma, Zhengang Li, Yifan Gong, Tianyun Zhang, Wei Niu, Zheng Zhan, Pu Zhao, Jian Tang, Xue Lin, Bin Ren, Yanzhi Wang
http://arxiv.org/abs/2001.08357v1
• [cs.LG]Best Arm Identification for Cascading Bandits in the Fixed Confidence Setting
Zixin Zhong, Wang Chi Cheung, Vincent Y. F. Tan
http://arxiv.org/abs/2001.08655v1
• [cs.LG]Compositional properties of emergent languages in deep learning
Bence Keresztury, Elia Bruni
http://arxiv.org/abs/2001.08618v1
• [cs.LG]Data Selection for Federated Learning with Relevant and Irrelevant Data at Clients
Tiffany Tuor, Shiqiang Wang, Bong Jun Ko, Changchang Liu, Kin K. Leung
http://arxiv.org/abs/2001.08300v1
• [cs.LG]Deep Transformer Models for Time Series Forecasting: The Influenza Prevalence Case
Neo Wu, Bradley Green, Xue Ben, Shawn O’Banion
http://arxiv.org/abs/2001.08317v1
• [cs.LG]Expected Information Maximization: Using the I-Projection for Mixture Density Estimation
Philipp Becker, Oleg Arenz, Gerhard Neumann
http://arxiv.org/abs/2001.08682v1
• [cs.LG]From abstract items to latent spaces to observed data and back: Compositional Variational Auto-Encoder
Victor Berger, Michèle Sebag
http://arxiv.org/abs/2001.07910v1
• [cs.LG]FsNet: Feature Selection Network on High-dimensional Biological Data
Dinesh Singh, Makoto Yamada
http://arxiv.org/abs/2001.08322v1
• [cs.LG]Information Compensation for Deep Conditional Generative Networks
Zehao Wang, Kaili Wang, Tinne Tuytelaars, Jose Oramas
http://arxiv.org/abs/2001.08559v1
• [cs.LG]Intelligent Chest X-ray Worklist Prioritization by CNNs: A Clinical Workflow Simulation
Ivo M. Baltruschat, Leonhard Steinmeister, Hannes Nickisch, Axel Saalbach, Michael Grass, Gerhard Adam, Harald Ittrich, Tobias Knopp
http://arxiv.org/abs/2001.08625v1
• [cs.LG]Intermittent Pulling with Local Compensation for Communication-Efficient Federated Learning
Haozhao Wang, Zhihao Qu, Song Guo, Xin Gao, Ruixuan Li, Baoliu Ye
http://arxiv.org/abs/2001.08277v1
• [cs.LG]Low-Complexity LSTM Training and Inference with FloatSD8 Weight Representation
Yu-Tung Liu, Tzi-Dar Chiueh
http://arxiv.org/abs/2001.08450v1
• [cs.LG]Massif: Interactive Interpretation of Adversarial Attacks on Deep Learning
Nilaksh Das, Haekyu Park, Zijie J. Wang, Fred Hohman, Robert Firstman, Emily Rogers, Duen Horng Chau
http://arxiv.org/abs/2001.07769v2
• [cs.LG]Multi-objective Neural Architecture Search via Non-stationary Policy Gradient
Zewei Chen, Fengwei Zhou, George Trimponias, Zhenguo Li
http://arxiv.org/abs/2001.08437v1
• [cs.LG]RPN: A Residual Pooling Network for Efficient Federated Learning
Anbu Huang, Yuanyuan Chen, Yang Liu, Tianjian Chen, Qiang Yang
http://arxiv.org/abs/2001.08600v1
• [cs.LG]Random Matrix Theory Proves that Deep Learning Representations of GAN-data Behave as Gaussian Mixtures
Mohamed El Amine Seddik, Cosme Louart, Mohamed Tamaazousti, Romain Couillet
http://arxiv.org/abs/2001.08370v1
• [cs.LG]Representation Learning for Medical Data
Karol Antczak
http://arxiv.org/abs/2001.08269v1
• [cs.LG]Scaling Laws for Neural Language Models
Jared Kaplan, Sam McCandlish, Tom Henighan, Tom B. Brown, Benjamin Chess, Rewon Child, Scott Gray, Alec Radford, Jeffrey Wu, Dario Amodei
http://arxiv.org/abs/2001.08361v1
• [cs.LG]Semi-supervised Grasp Detection by Representation Learning in a Vector Quantized Latent Space
Mridul Mahajan, Tryambak Bhattacharjee, Arya Krishnan, Priya Shukla, G C Nandi
http://arxiv.org/abs/2001.08477v1
• [cs.LG]Stacked Boosters Network Architecture for Short Term Load Forecasting in Buildings
Tuukka Salmi, Jussi Kiljander, Daniel Pakkala
http://arxiv.org/abs/2001.08406v1
• [cs.LG]Structured Compression and Sharing of Representational Space for Continual Learning
Gobinda Saha, Isha Garg, Aayush Ankit, Kaushik Roy
http://arxiv.org/abs/2001.08650v1
• [cs.LG]Towards A Controllable Disentanglement Network
Zengjie Song, Oluwasanmi Koyejo, Jiangshe Zhang
http://arxiv.org/abs/2001.08572v1
• [cs.LG]Towards Automatic Clustering Analysis using Traces of Information Gain: The InfoGuide Method
Paulo Rocha, Diego Pinheiro, Martin Cadeiras, Carmelo Bastos-Filho
http://arxiv.org/abs/2001.08677v1
• [cs.LG]Towards Robust DNNs: An Taylor Expansion-Based Method for Generating Powerful Adversarial Examples
Ya-guan Qian, Xi-Ming Zhang, Bin Wang, Wei Li, Jian-Hai Chen, Wu-Jie Zhou, Jing-Sheng Lei
http://arxiv.org/abs/2001.08389v1
• [cs.LG]Visual Summary of Value-level Feature Attribution in Prediction Classes with Recurrent Neural Networks
Chuan Wang, Xumeng Wang, Kwan-Liu Ma
http://arxiv.org/abs/2001.08379v1
• [cs.NE]DCT-Conv: Coding filters in convolutional networks with Discrete Cosine Transform
Karol Chęciński, Paweł Wawrzyński
http://arxiv.org/abs/2001.08517v1
• [cs.NI]Synchronous Transmissions in Low-Power Wireless: A Survey of Communication Protocols and Network Services
Marco Zimmerling, Luca Mottola, Silvia Santini
http://arxiv.org/abs/2001.08557v1
• [cs.RO]2D-VSR-Sim: an Optimization-friendly Simulator of 2-D Voxel-based Soft Robots
Eric Medvet, Alberto Bartoli, Andrea De Lorenzo, Stefano Seriani
http://arxiv.org/abs/2001.08617v1
• [cs.RO]A Probabilistic Framework for Imitating Human Race Driver Behavior
Stefan Löckel, Jan Peters, Peter van Vliet
http://arxiv.org/abs/2001.08255v1
• [cs.RO]Automatic Differentiation and Continuous Sensitivity Analysis of Rigid Body Dynamics
David Millard, Eric Heiden, Shubham Agrawal, Gaurav S. Sukhatme
http://arxiv.org/abs/2001.08539v1
• [cs.RO]Fast, Compact and Highly Scalable Visual Place Recognition through Sequence-based Matching of Overloaded Representations
Sourav Garg, Michael Milford
http://arxiv.org/abs/2001.08434v1
• [cs.RO]Impact-aware humanoid robot motion generation with a quadratic optimization controller
Yuquan Wang, Arnaud Tanguy, Pierre Gergondet, Abderrahmane Kheddar
http://arxiv.org/abs/2001.08454v1
• [cs.RO]Interpretable End-to-end Urban Autonomous Driving with Latent Deep Reinforcement Learning
Jianyu Chen, Shengbo Eben Li, Masayoshi Tomizuka
http://arxiv.org/abs/2001.08726v1
• [cs.RO]Learning Object Placements For Relational Instructions by Hallucinating Scene Representations
Oier Mees, Alp Emek, Johan Vertens, Wolfram Burgard
http://arxiv.org/abs/2001.08481v1
• [cs.RO]Socially intelligent task and motion planning for human-robot interaction
Andrea Frank, Laurel Riek
http://arxiv.org/abs/2001.08398v1
• [cs.RO]Trajectory Planning for Connected and Automated Vehicles: Cruising, Lane Changing, and Platooning
Xiangguo Liu, Guangchen Zhao, Neda Masoud, Qi Zhu
http://arxiv.org/abs/2001.08620v1
• [cs.SD]The INTERSPEECH 2020 Deep Noise Suppression Challenge: Datasets, Subjective Speech Quality and Testing Framework
Chandan K. A. Reddy, Ebrahim Beyrami, Harishchandra Dubey, Vishak Gopal, Roger Cheng, Ross Cutler, Sergiy Matusevych, Robert Aichner, Ashkan Aazami, Sebastian Braun, Puneet Rana, Sriram Srinivasan, Johannes Gehrke
http://arxiv.org/abs/2001.08662v1
• [cs.SE]Search-Based Software Engineering for Self-Adaptive Systems: One Survey, Five Disappointments and Six Opportunities
Tao Chen, Miqing Li, Ke Li, Kalyanmoy Deb
http://arxiv.org/abs/2001.08236v1
• [cs.SI]Joint Inference on Truth/Rumor and Their Sources in Social Networks
Shan Qu, Ziqi Zhao, Luoyi Fu, XInbing Wang, Jun Xu
http://arxiv.org/abs/2001.08472v1
• [cs.SI]Relational Thematic Clustering with Mutually Preferred Neighbors
Tiantian He, Lu Bai, Yew-Soon Ong
http://arxiv.org/abs/2001
6a6d
.08412v1
6a6d
.08412v1)
• [cs.SI]The Pushshift Reddit Dataset
Jason Baumgartner, Savvas Zannettou, Brian Keegan, Megan Squire, Jeremy Blackburn
http://arxiv.org/abs/2001.08435v1
• [cs.SI]The Pushshift Telegram Dataset
Jason Baumgartner, Savvas Zannettou, Megan Squire, Jeremy Blackburn
http://arxiv.org/abs/2001.08438v1
• [eess.AS]Improving speaker discrimination of target speech extraction with time-domain SpeakerBeam
Marc Delcroix, Tsubasa Ochiai, Katerina Zmolikova, Keisuke Kinoshita, Naohiro Tawara, Tomohiro Nakatani, Shoko Araki
http://arxiv.org/abs/2001.08378v1
• [eess.AS]On the human evaluation of audio adversarial examples
Jon Vadillo, Roberto Santana
http://arxiv.org/abs/2001.08444v1
• [eess.IV]A One-Shot Learning Framework for Assessment of Fibrillar Collagen from Second Harmonic Generation Images of an Infarcted Myocardium
Qun Liu, Supratik Mukhopadhyay, Maria Ximena Bastidas Rodriguez, Xing Fu, Sushant Sahu, David Burk, Manas Gartia
http://arxiv.org/abs/2001.08395v1
• [eess.IV]A multi-site study of a breast density deep learning model for full-field digital mammography and digital breast tomosynthesis exams
Thomas P. Matthews, Sadanand Singh, Brent Mombourquette, Jason Su, Meet P. Shah, Stefano Pedemonte, Aaron Long, David Maffit, Jenny Gurney, Rodrigo Morales Hoil, Nikita Ghare, Douglas Smith, Stephen M. Moore, Susan C. Marks, Richard L. Wahl
http://arxiv.org/abs/2001.08383v1
• [eess.IV]CNN-CASS: CNN for Classification of Coronary Artery Stenosis Score in MPR Images
Mariia Dobko, Bohdan Petryshak, Oles Dobosevych
http://arxiv.org/abs/2001.08593v1
• [eess.IV]MRI Banding Removal via Adversarial Training
Aaron Defazio, Tullie Murrell, Michael P. Recht
http://arxiv.org/abs/2001.08699v1
• [eess.IV]Segmentation of Retinal Low-Cost Optical Coherence Tomography Images using Deep Learning
Timo Kepp, Helge Sudkamp, Claus von der Burchard, Hendrik Schenke, Peter Koch, Gereon Hüttmann, Johann Roider, Mattias P. Heinrich, Heinz Handels
http://arxiv.org/abs/2001.08480v1
• [eess.IV]Tensor-Based Grading: A Novel Patch-Based Grading Approach for the Analysis of Deformation Fields in Huntington’s Disease
Kilian Hett, Hans Johnson, Pierrick Coupé, Jane Paulsen, Jeffrey Long, Ipek Oguz
http://arxiv.org/abs/2001.08651v1
• [eess.SP]A hemodynamic decomposition model for detecting cognitive load using functional near-infrared spectroscopy
Marco A. Pinto-Orellana, Diego C. Nascimento, Peyman Mirtaheri, Rune Jonassen, Anis Yazidi, Hugo L. Hammer
http://arxiv.org/abs/2001.08579v1
• [eess.SP]Inference over Wireless IoT Links with Importance-Filtered Updates
Ivana Nikoloska, Josefine Holm, Anders Kalør, Petar Popovski, Nikola Zlatanov
http://arxiv.org/abs/2001.07857v1
• [eess.SP]Reconfigurable Intelligent Surface assisted Two-Way Communications: Performance Analysis and Optimization
Saman Atapattu, Rongfei Fan, Prathapasinghe Dharmawansa, Gongpu Wang, Jamie Evans, Theodoros A. Tsiftsis
http://arxiv.org/abs/2001.07907v1
• [eess.SY]Counter-example Guided Learning of Bounds on Environment Behavior
Yuxiao Chen, Sumanth Dathathri, Tung Phan-Minh, Richard M. Murray
http://arxiv.org/abs/2001.07233v2
• [math.OC]Replica Exchange for Non-Convex Optimization
Jing Dong, Xin T. Tong
http://arxiv.org/abs/2001.08356v1
• [math.ST]A precise local limit theorem for the multinomial distribution
Frédéric Ouimet
http://arxiv.org/abs/2001.08512v1
• [math.ST]Geometric Conditions for the Discrepant Posterior Phenomenon and Connections to Simpson’s Paradox
Yang Chen, Ruobin Gong, Min-ge Xie
http://arxiv.org/abs/2001.08336v1
• [q-bio.NC]Towards naturalistic human neuroscience and neuroengineering: behavior mining in long-term video and neural recordings
Satpreet H. Singh, Steven M. Peterson, Rajesh P. N. Rao, Bingni W. Brunton
http://arxiv.org/abs/2001.08349v1
• [q-bio.QM]Deep learning-based prediction of response to HER2-targeted neoadjuvant chemotherapy from pre-treatment dynamic breast MRI: A multi-institutional validation study
Nathaniel Braman, Mohammed El Adoui, Manasa Vulchi, Paulette Turk, Maryam Etesami, Pingfu Fu, Kaustav Bera, Stylianos Drisis, Vinay Varadan, Donna Plecha, Mohammed Benjelloun, Jame Abraham, Anant Madabhushi
http://arxiv.org/abs/2001.08570v1
• [quant-ph]On lower semicontinuity of the quantum conditional mutual information and its corollaries
M. E. Shirokov
http://arxiv.org/abs/2001.08691v1
• [stat.AP]A covariance-enhanced approach to multi-tissue joint eQTL mapping with application to transcriptome-wide association studies
Aaron J. Molstad, Wei Sun, Li Hsu
http://arxiv.org/abs/2001.08363v1
• [stat.AP]Bayesian estimates of transmission line outage rates that consider line dependencies
Kai Zhou, James R. Cruise, Chris J. Dent, Ian Dobson, Louis Wehenkel, Zhaoyu Wang, Amy L. Wilson
http://arxiv.org/abs/2001.08681v1
• [stat.AP]Statistical post-processing of heat index ensemble forecasts: is there a royal road?
Sándor Baran, Ágnes Baran, Florian Pappenberger, Zied Ben Bouallègue
http://arxiv.org/abs/2001.08712v1
• [stat.ME]Maximum Likelihood Estimation of Spatially Varying Coefficient Models for Large Data with an Application to Real Estate Price Prediction
Jakob A. Dambon, Fabio Sigrist, Reinhard Furrer
http://arxiv.org/abs/2001.08089v2
• [stat.ME]On the Hauck-Donner Effect in Wald Tests: Detection, Tipping Points, and Parameter Space Characterization
Thomas William Yee
http://arxiv.org/abs/2001.08431v1
• [stat.ME]Shrinkage with Robustness: Log-Adjusted Priors for Sparse Signals
Yasuyuki Hamura, Kaoru Irie, Shonosuke Sugasawa
http://arxiv.org/abs/2001.08465v1
• [stat.ME]The Reciprocal Bayesian LASSO
Himel Mallick, Rahim Alhamzawi, Vladimir Svetnik
http://arxiv.org/abs/2001.08327v1
• [stat.ML]A Bayesian Long Short-Term Memory Model for Value at Risk and Expected Shortfall Joint Forecasting
Zhengkun Li, Minh-Ngoc Tran, Chao Wang, Richard Gerlach, Junbin Gao
http://arxiv.org/abs/2001.08374v1
• [stat.ML]A Multi-Scale Tensor Network Architecture for Classification and Regression
Justin Reyes, Miles Stoudenmire
http://arxiv.org/abs/2001.08286v1
• [stat.ML]Best Principal Submatrix Selection for the Maximum Entropy Sampling Problem: Scalable Algorithms and Performance Guarantees
Yongchun Li, Weijun Xie
http://arxiv.org/abs/2001.08537v1
• [stat.ML]Linking Bank Clients using Graph Neural Networks Powered by Rich Transactional Data
Valentina Shumovskaia, Kirill Fedyanin, Ivan Sukharev, Dmitry Berestnev, Maxim Panov
http://arxiv.org/abs/2001.08427v1
• [stat.ML]Stratified cross-validation for unbiased and privacy-preserving federated learning
R. Bey, R. Goussault, M. Benchoufi, R. Porcher
http://arxiv.org/abs/2001.08090v2
• [stat.ML]Target-Embedding Autoencoders for Supervised Representation Learning
Daniel Jarrett, Mihaela van der Schaar
http://arxiv.org/abs/2001.08345v1