cs.AI - 人工智能 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.GR - 计算机图形学 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.SI - 社交网络与信息网络 econ.EM - 计量经济学 eess.IV - 图像与视频处理 eess.SP - 信号处理 math.AP - 偏微分方程分析 math.OC - 优化与控制 math.ST - 统计理论 physics.comp-ph - 计算物理学 physics.soc-ph - 物理学与社会 quant-ph - 量子物理 stat.AP - 应用统计 stat.CO - 统计计算 stat.ME - 统计方法论 stat.ML - (统计)机器学习

    • [cs.AI]Approximating Euclidean by Imprecise Markov Decision Processes
    • [cs.AI]AvE: Assistance via Empowerment
    • [cs.AI]Does the Whole Exceed its Parts? The Effect of AI Explanations on Complementary Team Performance
    • [cs.AI]Explanation Augmented Feedback in Human-in-the-Loop Reinforcement Learning
    • [cs.CL]Dialog as a Vehicle for Lifelong Learning
    • [cs.CL]Evaluation of Text Generation: A Survey
    • [cs.CL]Graph Optimal Transport for Cross-Domain Alignment
    • [cs.CL]IIT Gandhinagar at SemEval-2020 Task 9: Code-Mixed Sentiment Classification Using Candidate Sentence Generation and Selection
    • [cs.CL]LPar — A Distributed Multi Agent platform for building Polyglot, Omni Channel and Industrial grade Natural Language Interfaces
    • [cs.CL]LSBert: A Simple Framework for Lexical Simplification
    • [cs.CL]Pre-training via Paraphrasing
    • [cs.CL]ProVe — Self-supervised pipeline for automated product replacement and cold-starting based on neural language models
    • [cs.CL]THEaiTRE: Artificial Intelligence to Write a Theatre Play
    • [cs.CL]What they do when in doubt: a study of inductive biases in seq2seq learners
    • [cs.CR]Analysis of Trending Topics and Text-based Channels of Information Delivery in Cybersecurity
    • [cs.CR]CyRes — Avoiding Catastrophic Failure in Connected and Autonomous Vehicles (Extended Abstract)
    • [cs.CR]Trust-by-Design: Evaluating Issues and Perceptions within Clinical Passporting
    • [cs.CV]An Advert Creation System for 3D Product Placements
    • [cs.CV]An Automatic Reader of Identity Documents
    • [cs.CV]AutoSNAP: Automatically Learning Neural Architectures for Instrument Pose Estimation
    • [cs.CV]Blind Image Deconvolution using Student’s-t Prior with Overlapping Group Sparsity
    • [cs.CV]CognitiveCNN: Mimicking Human Cognitive Models to resolve Texture-Shape Bias
    • [cs.CV]Cross-Ssupervised Object Detection
    • [cs.CV]Designing and Learning Trainable Priors with Non-Cooperative Games
    • [cs.CV]Domain Contrast for Domain Adaptive Object Detection
    • [cs.CV]End-to-end training of deep kernel map networks for image classification
    • [cs.CV]Ensemble Transfer Learning for Emergency Landing Field Identification on Moderate Resource Heterogeneous Kubernetes Cluster
    • [cs.CV]Expandable YOLO: 3D Object Detection from RGB-D Images
    • [cs.CV]Fast Multi-Level Foreground Estimation
    • [cs.CV]Few-Shot Anomaly Detection for Polyp Frames from Colonoscopy
    • [cs.CV]High Resolution Zero-Shot Domain Adaptation of Synthetically Rendered Face Images
    • [cs.CV]Lesion Mask-based Simultaneous Synthesis of Anatomic and MolecularMR Images using a GAN
    • [cs.CV]Person Re-identification by analyzing Dynamic Variations in Gait Sequences
    • [cs.CV]Pushing the Limit of Unsupervised Learning for Ultrasound Image Artifact Removal
    • [cs.CV]RPM-Net: Recurrent Prediction of Motion and Parts from Point Cloud
    • [cs.CV]Ricci Curvature Based Volumetric Segmentation of the Auditory Ossicles
    • [cs.CV]Suggestive Annotation of Brain Tumour Images with Gradient-guided Sampling
    • [cs.CV]Text Detection on Roughly Placed Books by Leveraging a Learning-based Model Trained with Another Domain Data
    • [cs.CV]ULSAM: Ultra-Lightweight Subspace Attention Module for Compact Convolutional Neural Networks
    • [cs.CV]Unsupervised Discovery of Object Landmarks via Contrastive Learning
    • [cs.CY]Could regulating the creators deliver trustworthy AI?
    • [cs.CY]New Metrics for Learning Evaluation in Digital Education Platforms
    • [cs.CY]The State of AI Ethics Report (June 2020)
    • [cs.CY]Towards an automated repository for indexing, analysis and characterization of municipal e-government websites in Mexico
    • [cs.DC]Local-Search Based Heuristics for Advertisement Scheduling
    • [cs.DC]Self-Scaling Clusters and Reproducible Containers to Enable Scientific Computing
    • [cs.DC]The TRaCaR Ratio: Selecting the Right Storage Technology for Active Dataset-Serving Databases
    • [cs.DL]The Sci-hub Effect: Sci-hub downloads lead to more article citations
    • [cs.DS]Constant-Depth and Subcubic-Size Threshold Circuits for Matrix Multiplication
    • [cs.GR]Computing Light Transport Gradients using the Adjoint Method
    • [cs.HC]An Interactive Data Visualization and Analytics Tool to Evaluate Mobility and Sociability Trends During COVID-19
    • [cs.HC]Exploratory Study of Young Children’s Social Media Needs and Requirements
    • [cs.IR]Memory-efficient Embedding for Recommendations
    • [cs.IR]TURL: Table Understanding through Representation Learning
    • [cs.IT]Deep-learning Autoencoder for Coherent and Nonlinear Optical Communication
    • [cs.IT]On Dynamic Time Division Duplex Transmissions for Small Cell Networks
    • [cs.IT]Recovery of Binary Sparse Signals from Structured Biased Measurements
    • [cs.IT]Tensor estimation with structured priors
    • [cs.IT]Two-Way Source-Channel Coding
    • [cs.LG]A Framework for Reinforcement Learning and Planning
    • [cs.LG]A Loss Function for Generative Neural Networks Based on Watson’s Perceptual Model
    • [cs.LG]A Unified Framework for Analyzing and Detecting Malicious Examples of DNN Models
    • [cs.LG]Asynchronous Multi Agent Active Search
    • [cs.LG]Building powerful and equivariant graph neural networks with message-passing
    • [cs.LG]Can Autonomous Vehicles Identify, Recover From, and Adapt to Distribution Shifts?
    • [cs.LG]Continual Learning from the Perspective of Compression
    • [cs.LG]Critic Regularized Regression
    • [cs.LG]Deep Partition Aggregation: Provable Defense against General Poisoning Attacks
    • [cs.LG]DeltaGrad: Rapid retraining of machine learning models
    • [cs.LG]Does the $\ell1$-norm Learn a Sparse Graph under Laplacian Constrained Graphical Models?
    • [cs.LG]E2GC: Energy-efficient Group Convolution in Deep Neural Networks
    • [cs.LG]Intrinsic Reward Driven Imitation Learning via Generative Model
    • [cs.LG]Learning predictive representations in autonomous driving to improve deep reinforcement learning
    • [cs.LG]MMF: A loss extension for feature learning in open set recognition
    • [cs.LG]Nearest Neighbour Based Estimates of Gradients: Sharp Nonasymptotic Bounds and Applications
    • [cs.LG]Not all Failure Modes are Created Equal: Training Deep Neural Networks for Explicable (Mis)Classification
    • [cs.LG]Object-Centric Learning with Slot Attention
    • [cs.LG]On the Generalization Benefit of Noise in Stochastic Gradient Descent
    • [cs.LG]Online 3D Bin Packing with Constrained Deep Reinforcement Learning
    • [cs.LG]Orthogonal Deep Models As Defense Against Black-Box Attacks
    • [cs.LG]PAC-Bayesian Bound for the Conditional Value at Risk
    • [cs.LG]Policy-GNN: Aggregation Optimization for Graph Neural Networks
    • [cs.LG]Proper Network Interpretability Helps Adversarial Robustness in Classification
    • [cs.LG]Q-Learning with Differential Entropy of Q-Tables
    • [cs.LG]Relative Deviation Margin Bounds
    • [cs.LG]Supermasks in Superposition
    • [cs.LG]The Ramifications of Making Deep Neural Networks Compact
    • [cs.LG]Training Convolutional ReLU Neural Networks in Polynomial Time: Exact Convex Optimization Formulations
    • [cs.LG]What can I do here? A Theory of Affordances in Reinforcement Learning
    • [cs.NE]Biologically Plausible Learning of Text Representation with Spiking Neural Networks
    • [cs.SI]Resilience in urban networked infrastructure: the case of Water Distribution Systems
    • [econ.EM]Endogenous Treatment Effect Estimation with some Invalid and Irrelevant Instruments
    • [econ.EM]Identification and Formal Privacy Guarantees
    • [eess.IV]A survey of loss functions for semantic segmentation
    • [eess.IV]Learning Diverse Latent Representations for Improving the Resilience to Adversarial Attacks
    • [eess.IV]SAR2SAR: a self-supervised despeckling algorithm for SAR images
    • [eess.SP]Co-Designing Statistical MIMO Radar and In-band Full-Duplex Multi-User MIMO Communications
    • [eess.SP]DRACO: Co-Optimizing Hardware Utilization, and Performance of DNNs on Systolic Accelerator
    • [eess.SP]Distributed Uplink Beamforming in Cell-Free Networks Using Deep Reinforcement Learning
    • [eess.SP]Graph modelling approaches for motorway traffic flow prediction
    • [eess.SP]On the Feasibility of Exploiting Traffic Collision Avoidance System Vulnerabilities
    • [eess.SP]Resource Allocation via Graph Neural Networks in Free Space Optical Fronthaul Networks
    • [math.AP]Computing the full signature kernel as the solution of a Goursat problem
    • [math.OC]Understanding Notions of Stationarity in Non-Smooth Optimization
    • [math.ST]Convergence Rates of Two-Component MCMC Samplers
    • [math.ST]Empirical MSE Minimization to Estimate a Scalar Parameter
    • [math.ST]Prediction in polynomial errors-in-variables models
    • [math.ST]Stochastic Approximation Algorithm for Estimating Mixing Distribution for Dependent Observations
    • [physics.comp-ph]GINNs: Graph-Informed Neural Networks for Multiscale Physics
    • [physics.soc-ph]Statistical inference of assortative community structures
    • [quant-ph]Can Quantum Computers Learn Like Classical Computers? A Co-Design Framework for Machine Learning and Quantum Circuits
    • [quant-ph]Layerwise learning for quantum neural networks
    • [stat.AP]Deriving information from missing data: implications for mood prediction
    • [stat.CO]Conditional particle filters with diffuse initial distributions
    • [stat.ME]A modified Armitage test for more than a linear trend on proportions
    • [stat.ME]Monitoring of process and risk-adjusted medical outcomes using a multi-stage MEWMA chart
    • [stat.ME]Parametric Bootstrap Confidence Intervals for the Multivariate Fay-Herriot Model
    • [stat.ME]Properties of restricted randomization with implications for experimental design
    • [stat.ME]Stable Feature Selection with Applications to MALDI Imaging Mass Spectrometry Data
    • [stat.ML]Covariance-engaged Classification of Sets via Linear Programming
    • [stat.ML]Hop Sampling: A Simple Regularized Graph Learning for Non-Stationary Environments
    • [stat.ML]Incremental inference of collective graphical models
    • [stat.ML]Learning Optimal Distributionally Robust Individualized Treatment Rules
    • [stat.ML]On Regret with Multiple Best Arms
    • [stat.ML]On the convergence of the Metropolis algorithm with fixed-order updates for multivariate binary probability distributions
    • [stat.ML]Relative gradient optimization of the Jacobian term in unsupervised deep learning
    • [stat.ML]Stochastic Differential Equations with Variational Wishart Diffusions
    • [stat.ML]The Gaussian equivalence of generative models for learning with two-layer neural networks
    • [stat.ML]The huge Package for High-dimensional Undirected Graph Estimation in R
    • [stat.ML]Transfer Learning via $\ell_1$ Regularization
    • [stat.ML]Unlabelled Data Improves Bayesian Uncertainty Calibration under Covariate Shift
    ·····································
    • [cs.AI]Approximating Euclidean by Imprecise Markov Decision Processes
    _Manfred Jaeger, Giorgio Bacci, Giovanni Bacci, Kim Guldstrand Larsen, Peter Gjøl Jensen
    http://arxiv.org/abs/2006.14923v1
    • [cs.AI]AvE: Assistance via Empowerment
    Yuqing Du, Stas Tiomkin, Emre Kiciman, Daniel Polani, Pieter Abbeel, Anca Dragan
    http://arxiv.org/abs/2006.14796v1
    • [cs.AI]Does the Whole Exceed its Parts? The Effect of AI Explanations on Complementary Team Performance
    Gagan Bansal, Tongshuang Wu, Joyce Zhu, Raymond Fok, Besmira Nushi, Ece Kamar, Marco Tulio Ribeiro, Daniel S. Weld
    http://arxiv.org/abs/2006.14779v1
    • [cs.AI]Explanation Augmented Feedback in Human-in-the-Loop Reinforcement Learning
    Lin Guan, Mudit Verma, Subbarao Kambhampati
    http://arxiv.org/abs/2006.14804v1
    • [cs.CL]Dialog as a Vehicle for Lifelong Learning
    Aishwarya Padmakumar, Raymond J. Mooney
    http://arxiv.org/abs/2006.14767v1
    • [cs.CL]Evaluation of Text Generation: A Survey
    Asli Celikyilmaz, Elizabeth Clark, Jianfeng Gao
    http://arxiv.org/abs/2006.14799v1
    • [cs.CL]Graph Optimal Transport for Cross-Domain Alignment
    Liqun Chen, Zhe Gan, Yu Cheng, Linjie Li, Lawrence Carin, Jingjing Liu
    http://arxiv.org/abs/2006.14744v1
    • [cs.CL]IIT Gandhinagar at SemEval-2020 Task 9: Code-Mixed Sentiment Classification Using Candidate Sentence Generation and Selection
    Vivek Srivastava, Mayank Singh
    http://arxiv.org/abs/2006.14465v2
    • [cs.CL]LPar — A Distributed Multi Agent platform for building Polyglot, Omni Channel and Industrial grade Natural Language Interfaces
    Pranav Sharma
    http://arxiv.org/abs/2006.14666v1
    • [cs.CL]LSBert: A Simple Framework for Lexical Simplification
    Jipeng Qiang, Yun Li, Yi Zhu, Yunhao Yuan, Xindong Wu
    http://arxiv.org/abs/2006.14939v1
    • [cs.CL]Pre-training via Paraphrasing
    Mike Lewis, Marjan Ghazvininejad, Gargi Ghosh, Armen Aghajanyan, Sida Wang, Luke Zettlemoyer
    http://arxiv.org/abs/2006.15020v1
    • [cs.CL]ProVe — Self-supervised pipeline for automated product replacement and cold-starting based on neural language models
    Andrei Ionut Damian, Laurentiu Piciu, Cosmin Mihai Marinescu
    http://arxiv.org/abs/2006.14994v1
    • [cs.CL]THEaiTRE: Artificial Intelligence to Write a Theatre Play
    Rudolf Rosa, Ondřej Dušek, Tom Kocmi, David Mareček, Tomáš Musil, Patrícia Schmidtová, Dominik Jurko, Ondřej Bojar, Daniel Hrbek, David Košťák, Martina Kinská, Josef Doležal, Klára Vosecká
    http://arxiv.org/abs/2006.14668v1
    • [cs.CL]What they do when in doubt: a study of inductive biases in seq2seq learners
    Eugene Kharitonov, Rahma Chaabouni
    http://arxiv.org/abs/2006.14953v1
    • [cs.CR]Analysis of Trending Topics and Text-based Channels of Information Delivery in Cybersecurity
    Tingmin Wu, Wanlun Ma, Sheng Wen, Xin Xia, Cecile Paris, Surya Nepal, Yang Xiang
    http://arxiv.org/abs/2006.14765v1
    • [cs.CR]CyRes — Avoiding Catastrophic Failure in Connected and Autonomous Vehicles (Extended Abstract)
    Carsten Maple, Peter Davies, Kerstin Eder, Chris Hankin, Greg Chance, Gregory Ephiphaniou
    http://arxiv.org/abs/2006.14890v1
    • [cs.CR]Trust-by-Design: Evaluating Issues and Perceptions within Clinical Passporting
    Will Abramson, Nicole E. van Deursen, William J Buchanan
    http://arxiv.org/abs/2006.14864v1
    • [cs.CV]An Advert Creation System for 3D Product Placements
    Ivan Bacher, Hossein Javidnia, Soumyabrata Dev, Rahul Agrahari, Murhaf Hossari, Matthew Nicholson, Clare Conran, Jian Tang, Peng Song, David Corrigan, François Pitié
    http://arxiv.org/abs/2006.15131v1
    • [cs.CV]An Automatic Reader of Identity Documents
    Filippo Attivissimo, Nicola Giaquinto, Marco Scarpetta, Maurizio Spadavecchia
    http://arxiv.org/abs/2006.14853v1
    • [cs.CV]AutoSNAP: Automatically Learning Neural Architectures for Instrument Pose Estimation
    David Kügler, Marc Uecker, Arjan Kuijper, Anirban Mukhopadhyay
    http://arxiv.org/abs/2006.14858v1
    • [cs.CV]Blind Image Deconvolution using Student’s-t Prior with Overlapping Group Sparsity
    In S. Jeon, Deokyoung Kang, Suk I. Yoo
    http://arxiv.org/abs/2006.14780v1
    • [cs.CV]CognitiveCNN: Mimicking Human Cognitive Models to resolve Texture-Shape Bias
    Satyam Mohla, Anshul Nasery, Biplab Banerjee, Subhasis Chaudhari
    http://arxiv.org/abs/2006.14722v1
    • [cs.CV]Cross-Ssupervised Object Detection
    Zitian Chen, Zhiqiang Shen, Jiahui Yu, Erik Learned-Miller
    http://arxiv.org/abs/2006.15056v1
    • [cs.CV]Designing and Learning Trainable Priors with Non-Cooperative Games
    Bruno Lecouat, Jean Ponce, Julien Mairal
    http://arxiv.org/abs/2006.14859v1
    • [cs.CV]Domain Contrast for Domain Adaptive Object Detection
    Feng Liu, Xiaoxong Zhang, Fang Wan, Xiangyang Ji, Qixiang Ye
    http://arxiv.org/abs/2006.14863v1
    • [cs.CV]End-to-end training of deep kernel map networks for image classification
    Mingyuan Jiu, Hichem Sahbi
    http://arxiv.org/abs/2006.15088v1
    • [cs.CV]Ensemble Transfer Learning for Emergency Landing Field Identification on Moderate Resource Heterogeneous Kubernetes Cluster
    Andreas Klos, Marius Rosenbaum, Wolfram Schiffmann
    http://arxiv.org/abs/2006.14887v1
    • [cs.CV]Expandable YOLO: 3D Object Detection from RGB-D Images
    Masahiro Takahashi, Alessandro Moro, Yonghoon Ji, Kazunori Umeda
    http://arxiv.org/abs/2006.14837v1
    • [cs.CV]Fast Multi-Level Foreground Estimation
    Thomas Germer, Tobias Uelwer, Stefan Conrad, Stefan Harmeling
    http://arxiv.org/abs/2006.14970v1
    • [cs.CV]Few-Shot Anomaly Detection for Polyp Frames from Colonoscopy
    Yu Tian, Gabriel Maicas, Leonardo Zorron Cheng Tao Pu, Rajvinder Singh, Johan W. Verjans, Gustavo Carneiro
    http://arxiv.org/abs/2006.14811v1
    • [cs.CV]High Resolution Zero-Shot Domain Adaptation of Synthetically Rendered Face Images
    Stephan J. Garbin, Marek Kowalski, Matthew Johnson, Jamie Shotton
    http://arxiv.org/abs/2006.15031v1
    • [cs.CV]Lesion Mask-based Simultaneous Synthesis of Anatomic and MolecularMR Images using a GAN
    Pengfei Guo, Puyang Wang, Jinyuan Zhou, Vishal Patel, Shanshan Jiang
    http://arxiv.org/abs/2006.14761v1
    • [cs.CV]Person Re-identification by analyzing Dynamic Variations in Gait Sequences
    Sandesh Bharadwaj, Kunal Chanda
    http://arxiv.org/abs/2006.15109v1
    • [cs.CV]Pushing the Limit of Unsupervised Learning for Ultrasound Image Artifact Removal
    Shujaat Khan, Jaeyoung Huh, Jong Chul Ye
    http://arxiv.org/abs/2006.14773v1
    • [cs.CV]RPM-Net: Recurrent Prediction of Motion and Parts from Point Cloud
    Zihao Yan, Ruizhen Hu, Xingguang Yan, Luanmin Chen, Oliver van Kaick, Hao Zhang, Hui Huang
    http://arxiv.org/abs/2006.14865v1
    • [cs.CV]Ricci Curvature Based Volumetric Segmentation of the Auditory Ossicles
    Na Lei, Jisui Huang, Yuxue Ren, Emil Saucan, Zhenchang Wang
    http://arxiv.org/abs/2006.14788v1
    • [cs.CV]Suggestive Annotation of Brain Tumour Images with Gradient-guided Sampling
    Chengliang Dai, Shuo Wang, Yuanhan Mo, Elsa Angelini, Yike Guo, Wenjia Bai
    http://arxiv.org/abs/2006.14984v1
    • [cs.CV]Text Detection on Roughly Placed Books by Leveraging a Learning-based Model Trained with Another Domain Data
    Riku Anegawa, Masayoshi Aritsugi
    http://arxiv.org/abs/2006.14808v1
    • [cs.CV]ULSAM: Ultra-Lightweight Subspace Attention Module for Compact Convolutional Neural Networks
    Rajat Saini, Nandan Kumar Jha, Bedanta Das, Sparsh Mittal, C. Krishna Mohan
    http://arxiv.org/abs/2006.15102v1
    • [cs.CV]Unsupervised Discovery of Object Landmarks via Contrastive Learning
    Zezhou Cheng, Jong-Chyi Su, Subhransu Maji
    http://arxiv.org/abs/2006.14787v1
    • [cs.CY]Could regulating the creators deliver trustworthy AI?
    Labhaoise Ni Fhaolain, Andrew Hines
    http://arxiv.org/abs/2006.14750v1
    • [cs.CY]New Metrics for Learning Evaluation in Digital Education Platforms
    Gabriel Leitão, Juan Colonna, Edwin Monteiro, Elaine Oliveira, Raimundo Barreto
    http://arxiv.org/abs/2006.14711v1
    • [cs.CY]The State of AI Ethics Report (June 2020)
    Abhishek Gupta, Camylle Lanteigne, Victoria Heath, Marianna Bergamaschi Ganapini, Erick Galinkin, Allison Cohen, Tania De Gasperis, Mo Akif, Renjie Butalid
    http://arxiv.org/abs/2006.14662v1
    • [cs.CY]Towards an automated repository for indexing, analysis and characterization of municipal e-government websites in Mexico
    Sergio R. Coria, Leonardo Marcos-Santiago, Christian A. Cruz-Melendez, Juan M. Jimenez-Canseco
    http://arxiv.org/abs/2006.14746v1
    • [cs.DC]Local-Search Based Heuristics for Advertisement Scheduling
    Mauro R. C. da Silva, Rafael C. S. Schouery
    http://arxiv.org/abs/2006.13432v2
    • [cs.DC]Self-Scaling Clusters and Reproducible Containers to Enable Scientific Computing
    Peter Z. Vaillancourt, J. Eric Coulter, Richard Knepper, Brandon Barker
    http://arxiv.org/abs/2006.14784v1
    • [cs.DC]The TRaCaR Ratio: Selecting the Right Storage Technology for Active Dataset-Serving Databases
    Francisco Romero, Benjamin Braun, David Cheriton
    http://arxiv.org/abs/2006.14793v1
    • [cs.DL]The Sci-hub Effect: Sci-hub downloads lead to more article citations
    J. C. Correa, H. Laverde-Rojas, F. Marmolejo-Ramos, J. Tejada, Š. Bahník
    http://arxiv.org/abs/2006.14979v1
    • [cs.DS]Constant-Depth and Subcubic-Size Threshold Circuits for Matrix Multiplication
    Ojas Parekh, Cynthia A. Phillips, Conrad D. James, James B. Aimone
    http://arxiv.org/abs/2006.14652v1
    • [cs.GR]Computing Light Transport Gradients using the Adjoint Method
    Jos Stam
    http://arxiv.org/abs/2006.15059v1
    • [cs.HC]An Interactive Data Visualization and Analytics Tool to Evaluate Mobility and Sociability Trends During COVID-19
    Fan Zuo, Jingxing Wang, Jingqin Gao, Kaan Ozbay, Xuegang Jeff Ban, Yubin Shen, Hong Yang, Shri Iyer
    http://arxiv.org/abs/2006.14882v1
    • [cs.HC]Exploratory Study of Young Children’s Social Media Needs and Requirements
    Di “Chelsea” Sun, Vaishnavi Melkote, Ahmed Sabbir Arif
    http://arxiv.org/abs/2006.14654v1
    • [cs.IR]Memory-efficient Embedding for Recommendations
    Xiangyu Zhao, Haochen Liu, Hui Liu, Jiliang Tang, Weiwei Guo, Jun Shi, Sida Wang, Huiji Gao, Bo Long
    http://arxiv.org/abs/2006.14827v1
    • [cs.IR]TURL: Table Understanding through Representation Learning
    Xiang Deng, Huan Sun, Alyssa Lees, You Wu, Cong Yu
    http://arxiv.org/abs/2006.14806v1
    • [cs.IT]Deep-learning Autoencoder for Coherent and Nonlinear Optical Communication
    Tim Uhlemann, Sebastian Cammerer, Alexander Span, Sebastian Dörner, Stephan ten Brink
    http://arxiv.org/abs/2006.15027v1
    • [cs.IT]On Dynamic Time Division Duplex Transmissions for Small Cell Networks
    Ming Ding, David Lopez-Perez, Ruiqi Xue, Athanasios V. Vasilakos, Wen Chen
    http://arxiv.org/abs/2006.14829v1
    • [cs.IT]Recovery of Binary Sparse Signals from Structured Biased Measurements
    Sandra Keiper
    http://arxiv.org/abs/2006.14835v1
    • [cs.IT]Tensor estimation with structured priors
    Clément Luneau, Nicolas Macris
    http://arxiv.org/abs/2006.14989v1
    • [cs.IT]Two-Way Source-Channel Coding
    Jian-Jia Weng, Fady Alajaji, Tamás Linder
    http://arxiv.org/abs/2006.14913v1
    • [cs.LG]A Framework for Reinforcement Learning and Planning
    Thomas M. Moerland, Joost Broekens, Catholijn M. Jonker
    http://arxiv.org/abs/2006.15009v1
    • [cs.LG]A Loss Function for Generative Neural Networks Based on Watson’s Perceptual Model
    Steffen Czolbe, Oswin Krause, Ingemar Cox, Christian Igel
    http://arxiv.org/abs/2006.15057v1
    • [cs.LG]A Unified Framework for Analyzing and Detecting Malicious Examples of DNN Models
    Kaidi Jin, Tianwei Zhang, Chao Shen, Yufei Chen, Ming Fan, Chenhao Lin, Ting Liu
    http://arxiv.org/abs/2006.14871v1
    • [cs.LG]Asynchronous Multi Agent Active Search
    Ramina Ghods, Arundhati Banerjee, Jeff Schneider
    http://arxiv.org/abs/2006.14718v1
    • [cs.LG]Building powerful and equivariant graph neural networks with message-passing
    Clement Vignac, Andreas Loukas, Pascal Frossard
    http://arxiv.org/abs/2006.15107v1
    • [cs.LG]Can Autonomous Vehicles Identify, Recover From, and Adapt to Distribution Shifts?
    Angelos Filos, Panagiotis Tigas, Rowan McAllister, Nicholas Rhinehart, Sergey Levine, Yarin Gal
    http://arxiv.org/abs/2006.14911v1
    • [cs.LG]Continual Learning from the Perspective of Compression
    Xu He, Min Lin
    http://arxiv.org/abs/2006.15078v1
    • [cs.LG]Critic Regularized Regression
    Ziyu Wang, Alexander Novikov, Konrad Żołna, Jost Tobias Springenberg, Scott Reed, Bobak Shahriari, Noah Siegel, Josh Merel, Caglar Gulcehre, Nicolas Heess, Nando de Freitas
    http://arxiv.org/abs/2006.15134v1
    • [cs.LG]Deep Partition Aggregation: Provable Defense against General Poisoning Attacks
    Alexander Levine, Soheil Feizi
    http://arxiv.org/abs/2006.14768v1
    • [cs.LG]DeltaGrad: Rapid retraining of machine learning models
    Yinjun Wu, Edgar Dobriban, Susan B. Davidson
    http://arxiv.org/abs/2006.14755v1
    • [cs.LG]Does the $\ell_1$-norm Learn a Sparse Graph under Laplacian Constrained Graphical Models?
    Jiaxi Ying, José Vinícius de M. Cardoso, Daniel P. Palomar
    http://arxiv.org/abs/2006.14925v1
    • [cs.LG]E2GC: Energy-efficient Group Convolution in Deep Neural Networks
    Nandan Kumar Jha, Rajat Saini, Subhrajit Nag, Sparsh Mittal
    http://arxiv.org/abs/2006.15100v1
    • [cs.LG]Intrinsic Reward Driven Imitation Learning via Generative Model
    Xingrui Yu, Yueming Lyu, Ivor W. Tsang
    http://arxiv.org/abs/2006.15061v1
    • [cs.LG]Learning predictive representations in autonomous driving to improve deep reinforcement learning
    Daniel Graves, Nhat M. Nguyen, Kimia Hassanzadeh, Jun Jin
    http://arxiv.org/abs/2006.15110v1
    • [cs.LG]MMF: A loss extension for feature learning in open set recognition
    Jingyun Jia, Philip K. Chan
    http://arxiv.org/abs/2006.15117v1
    • [cs.LG]Nearest Neighbour Based Estimates of Gradients: Sharp Nonasymptotic Bounds and Applications
    Guillaume Ausset, Stephan Clémençon, François Portier
    http://arxiv.org/abs/2006.15043v1
    • [cs.LG]Not all Failure Modes are Created Equal: Training Deep Neural Networks for Explicable (Mis)Classification
    Alberto Olmo, Sailik Sengupta, Subbarao Kambhampati
    http://arxiv.org/abs/2006.14841v1
    • [cs.LG]Object-Centric Learning with Slot Attention
    Francesco Locatello, Dirk Weissenborn, Thomas Unterthiner, Aravindh Mahendran, Georg Heigold, Jakob Uszkoreit, Alexey Dosovitskiy, Thomas Kipf
    http://arxiv.org/abs/2006.15055v1
    • [cs.LG]On the Generalization Benefit of Noise in Stochastic Gradient Descent
    Samuel L. Smith, Erich Elsen, Soham De
    http://arxiv.org/abs/2006.15081v1
    • [cs.LG]Online 3D Bin Packing with Constrained Deep Reinforcement Learning
    Hang Zhao, Qijin She, Chenyang Zhu, Yin Yang, Kai Xu
    http://arxiv.org/abs/2006.14978v1
    • [cs.LG]Orthogonal Deep Models As Defense Against Black-Box Attacks
    Mohammad A. A. K. Jalwana, Naveed Akhtar, Mohammed Bennamoun, Ajmal Mian
    http://arxiv.org/abs/2006.14856v1
    • [cs.LG]PAC-Bayesian Bound for the Conditional Value at Risk
    Zakaria Mhammedi, Benjamin Guedj, Robert C. Williamson
    http://arxiv.org/abs/2006.14763v1
    • [cs.LG]Policy-GNN: Aggregation Optimization for Graph Neural Networks
    Kwei-Herng Lai, Daochen Zha, Kaixiong Zhou, Xia Hu
    http://arxiv.org/abs/2006.15097v1
    • [cs.LG]Proper Network Interpretability Helps Adversarial Robustness in Classification
    Akhilan Boopathy, Sijia Liu, Gaoyuan Zhang, Cynthia Liu, Pin-Yu Chen, Shiyu Chang, Luca Daniel
    http://arxiv.org/abs/2006.14748v1
    • [cs.LG]Q-Learning with Differential Entropy of Q-Tables
    Tung D. Nguyen, Kathryn E. Kasmarik, Hussein A. Abbass
    http://arxiv.org/abs/2006.14795v1
    • [cs.LG]Relative Deviation Margin Bounds
    Corinna Cortes, Mehryar Mohri, Ananda Theertha Suresh
    http://arxiv.org/abs/2006.14950v1
    • [cs.LG]Supermasks in Superposition
    Mitchell Wortsman, Vivek Ramanujan, Rosanne Liu, Aniruddha Kembhavi, Mohammad Rastegari, Jason Yosinski, Ali Farhadi
    http://arxiv.org/abs/2006.14769v1
    • [cs.LG]The Ramifications of Making Deep Neural Networks Compact
    Nandan Kumar Jha, Sparsh Mittal, Govardhan Mattela
    http://arxiv.org/abs/2006.15098v1
    • [cs.LG]Training Convolutional ReLU Neural Networks in Polynomial Time: Exact Convex Optimization Formulations
    Tolga Ergen, Mert Pilanci
    http://arxiv.org/abs/2006.14798v1
    • [cs.LG]What can I do here? A Theory of Affordances in Reinforcement Learning
    Khimya Khetarpal, Zafarali Ahmed, Gheorghe Comanici, David Abel, Doina Precup
    http://arxiv.org/abs/2006.15085v1
    • [cs.NE]Biologically Plausible Learning of Text Representation with Spiking Neural Networks
    Marcin Białas, Marc
    7776
    in Michał Mirończuk, Jacek Mańdziuk
    http://arxiv.org/abs/2006.14894v1
    • [cs.SI]Resilience in urban networked infrastructure: the case of Water Distribution Systems
    Antonio Candelieri, Ilaria Giordani, Andrea Ponti, Francesco Archetti
    http://arxiv.org/abs/2006.14622v1
    • [econ.EM]Endogenous Treatment Effect Estimation with some Invalid and Irrelevant Instruments
    Qingliang Fan, Yaqian Wu
    http://arxiv.org/abs/2006.14998v1
    • [econ.EM]Identification and Formal Privacy Guarantees
    Tatiana Komarova, Denis Nekipelov
    http://arxiv.org/abs/2006.14732v1
    • [eess.IV]A survey of loss functions for semantic segmentation
    Shruti Jadon
    http://arxiv.org/abs/2006.14822v1
    • [eess.IV]Learning Diverse Latent Representations for Improving the Resilience to Adversarial Attacks
    Ali Mirzaeian, Mohammad Sabokrou, Mohammad Khalooei, Jana Kosecka, Houman Homayoun, Tinoosh Mohsening, Avesta Sasan
    http://arxiv.org/abs/2006.15127v1
    • [eess.IV]SAR2SAR: a self-supervised despeckling algorithm for SAR images
    Emanuele Dalsasso, Loïc Denis, Florence Tupin
    http://arxiv.org/abs/2006.15037v1
    • [eess.SP]Co-Designing Statistical MIMO Radar and In-band Full-Duplex Multi-User MIMO Communications
    Jiawei Liu, Kumar Vijay Mishra, Mohammad Saquib
    http://arxiv.org/abs/2006.14774v1
    • [eess.SP]DRACO: Co-Optimizing Hardware Utilization, and Performance of DNNs on Systolic Accelerator
    Nandan Kumar Jha, Shreyas Ravishankar, Sparsh Mittal, Arvind Kaushik, Dipan Mandal, Mahesh Chandra
    http://arxiv.org/abs/2006.15103v1
    • [eess.SP]Distributed Uplink Beamforming in Cell-Free Networks Using Deep Reinforcement Learning
    Firas Fredj, Yasser Al-Eryani, Setareh Maghsudi, Mohamed Akrout, Ekram Hossain
    http://arxiv.org/abs/2006.15138v1
    • [eess.SP]Graph modelling approaches for motorway traffic flow prediction
    Adriana-Simona Mihaita, Zac Papachatgis, Marian-Andrei Rizoiu
    http://arxiv.org/abs/2006.14824v1
    • [eess.SP]On the Feasibility of Exploiting Traffic Collision Avoidance System Vulnerabilities
    Paul M. Berges, Basavesh Ammanaghatta Shivakumar, Timothy Graziano, Ryan Gerdes, Z. Berkay Celik
    http://arxiv.org/abs/2006.14679v1
    • [eess.SP]Resource Allocation via Graph Neural Networks in Free Space Optical Fronthaul Networks
    Zhan Gao, Mark Eisen, Alejandro Ribeiro
    http://arxiv.org/abs/2006.15005v1
    • [math.AP]Computing the full signature kernel as the solution of a Goursat problem
    Thomas Cass, Terry Lyons, Cristopher Salvi, Weixin Yang
    http://arxiv.org/abs/2006.14794v1
    • [math.OC]Understanding Notions of Stationarity in Non-Smooth Optimization
    Jiajin Li, Anthony Man-Cho So, Wing-Kin Ma
    http://arxiv.org/abs/2006.14901v1
    • [math.ST]Convergence Rates of Two-Component MCMC Samplers
    Qian Qin, Galin L. Jones
    http://arxiv.org/abs/2006.14801v1
    • [math.ST]Empirical MSE Minimization to Estimate a Scalar Parameter
    Clément de Chaisemartin, Xavier D’Haultfœuille
    http://arxiv.org/abs/2006.14667v1
    • [math.ST]Prediction in polynomial errors-in-variables models
    Alexander Kukush, Ivan Senko
    http://arxiv.org/abs/2006.14818v1
    • [math.ST]Stochastic Approximation Algorithm for Estimating Mixing Distribution for Dependent Observations
    Nilabja Guha, Anindya Roy
    http://arxiv.org/abs/2006.14734v1
    • [physics.comp-ph]GINNs: Graph-Informed Neural Networks for Multiscale Physics
    Eric J. Hall, Søren Taverniers, Markos A. Katsoulakis, Daniel M. Tartakovsky
    http://arxiv.org/abs/2006.14807v1
    • [physics.soc-ph]Statistical inference of assortative community structures
    Lizhi Zhang, Tiago P. Peixoto
    http://arxiv.org/abs/2006.14493v2
    • [quant-ph]Can Quantum Computers Learn Like Classical Computers? A Co-Design Framework for Machine Learning and Quantum Circuits
    Weiwen Jiang, Jinjun Xiong, Yiyu Shi
    http://arxiv.org/abs/2006.14815v1
    • [quant-ph]Layerwise learning for quantum neural networks
    Andrea Skolik, Jarrod R. McClean, Masoud Mohseni, Patrick van der Smagt, Martin Leib
    http://arxiv.org/abs/2006.14904v1
    • [stat.AP]Deriving information from missing data: implications for mood prediction
    Yue Wu, Terry J. Lyons, Kate E. A. Saunders
    http://arxiv.org/abs/2006.15030v1
    • [stat.CO]Conditional particle filters with diffuse initial distributions
    Santeri Karppinen, Matti Vihola
    http://arxiv.org/abs/2006.14877v1
    • [stat.ME]A modified Armitage test for more than a linear trend on proportions
    Ludwig A. Hothorn, Frank Schaarschmidt
    http://arxiv.org/abs/2006.14880v1
    • [stat.ME]Monitoring of process and risk-adjusted medical outcomes using a multi-stage MEWMA chart
    Doaa Ayad, Nokuthaba Sibanda
    http://arxiv.org/abs/2006.14737v1
    • [stat.ME]Parametric Bootstrap Confidence Intervals for the Multivariate Fay-Herriot Model
    Takumi Saegusa, Shonosuke Sugasawa, Partha Lahiri
    http://arxiv.org/abs/2006.14820v1
    • [stat.ME]Properties of restricted randomization with implications for experimental design
    Mattias Nordin, Mårten Schultzberg
    http://arxiv.org/abs/2006.14888v1
    • [stat.ME]Stable Feature Selection with Applications to MALDI Imaging Mass Spectrometry Data
    Jonathan von Schroeder
    http://arxiv.org/abs/2006.15077v1
    • [stat.ML]Covariance-engaged Classification of Sets via Linear Programming
    Zhao Ren, Sungkyu Jung, Xingye Qiao
    http://arxiv.org/abs/2006.14831v1
    • [stat.ML]Hop Sampling: A Simple Regularized Graph Learning for Non-Stationary Environments
    Young-Jin Park, Kyuyong Shin, Kyung-Min Kim
    http://arxiv.org/abs/2006.14897v1
    • [stat.ML]Incremental inference of collective graphical models
    Rahul Singh, Isabel Haasler, Qinsheng Zhang, Johan Karlsson, Yongxin Chen
    http://arxiv.org/abs/2006.15035v1
    • [stat.ML]Learning Optimal Distributionally Robust Individualized Treatment Rules
    Weibin Mo, Zhengling Qi, Yufeng Liu
    http://arxiv.org/abs/2006.15121v1
    • [stat.ML]On Regret with Multiple Best Arms
    Yinglun Zhu, Robert Nowak
    http://arxiv.org/abs/2006.14785v1
    • [stat.ML]On the convergence of the Metropolis algorithm with fixed-order updates for multivariate binary probability distributions
    Kai Brügge, Asja Fischer, Christian Igel
    http://arxiv.org/abs/2006.14999v1
    • [stat.ML]Relative gradient optimization of the Jacobian term in unsupervised deep learning
    Luigi Gresele, Giancarlo Fissore, Adrián Javaloy, Bernhard Schölkopf, Aapo Hyvärinen
    http://arxiv.org/abs/2006.15090v1
    • [stat.ML]Stochastic Differential Equations with Variational Wishart Diffusions
    Martin Jørgensen, Marc Peter Deisenroth, Hugh Salimbeni
    http://arxiv.org/abs/2006.14895v1
    • [stat.ML]The Gaussian equivalence of generative models for learning with two-layer neural networks
    Sebastian Goldt, Galen Reeves, Marc Mézard, Florent Krzakala, Lenka Zdeborová
    http://arxiv.org/abs/2006.14709v1
    • [stat.ML]The huge Package for High-dimensional Undirected Graph Estimation in R
    Tuo Zhao, Han Liu, Kathryn Roeder, John Lafferty, Larry Wasserman
    http://arxiv.org/abs/2006.14781v1
    • [stat.ML]Transfer Learning via $\ell_1$ Regularization
    Masaaki Takada, Hironori Fujisawa
    http://arxiv.org/abs/2006.14845v1
    • [stat.ML]Unlabelled Data Improves Bayesian Uncertainty Calibration under Covariate Shift
    Alex J. Chan, Ahmed M. Alaa, Zhaozhi Qian, Mihaela van der Schaar
    http://arxiv.org/abs/2006.14988v1