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.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.LO - 计算逻辑 cs.NE - 神经与进化计算 cs.RO - 机器人学 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.FA - 泛函演算 math.OC - 优化与控制 math.PR - 概率 math.ST - 统计理论 physics.med-ph - 医学物理学 physics.soc-ph - 物理学与社会 q-bio.NC - 神经元与认知 quant-ph - 量子物理 stat.ME - 统计方法论 stat.ML - (统计)机器学习
• [cs.AI]Contextual and Possibilistic Reasoning for Coalition Formation
• [cs.AI]Learn to Earn: Enabling Coordination within a Ride Hailing Fleet
• [cs.AI]Learning Optimal Power Flow: Worst-Case Guarantees for Neural Networks
• [cs.AI]Representing Pure Nash Equilibria in Argumentation
• [cs.CL]A Survey of Syntactic-Semantic Parsing Based on Constituent and Dependency Structures
• [cs.CL]Dataset for Automatic Summarization of Russian News
• [cs.CL]Explainable and Discourse Topic-aware Neural Language Understanding
• [cs.CL]Exploring Processing of Nested Dependencies in Neural-Network Language Models and Humans
• [cs.CL]Neural Topic Modeling with Continual Lifelong Learning
• [cs.CL]New Vietnamese Corpus for Machine ReadingComprehension of Health News Articles
• [cs.CL]Sentiment Frames for Attitude Extraction in Russian
• [cs.CR]Analyzing the Real-World Applicability of DGA Classifiers
• [cs.CR]Backdoor Attacks to Graph Neural Networks
• [cs.CR]Systematic Attack Surface Reduction For Deployed Sentiment Analysis Models
• [cs.CV]Adaptive feature recombination and recalibration for semantic segmentation with Fully Convolutional Networks
• [cs.CV]Attention Mesh: High-fidelity Face Mesh Prediction in Real-time
• [cs.CV]Center-based 3D Object Detection and Tracking
• [cs.CV]Compositional Learning of Image-Text Query for Image Retrieval
• [cs.CV]Consistency Guided Scene Flow Estimation
• [cs.CV]Cross-denoising Network against Corrupted Labels in Medical Image Segmentation with Domain Shift
• [cs.CV]Deep Image Translation for Enhancing Simulated Ultrasound Images
• [cs.CV]Deep Learning-based Single Image Face Depth Data Enhancement
• [cs.CV]Deep Transformation-Invariant Clustering
• [cs.CV]Deep Transformation-Invariant Clustering
• [cs.CV]Emotion Recognition on large video dataset based on Convolutional Feature Extractor and Recurrent Neural Network
• [cs.CV]Evaluation Of Hidden Markov Models Using Deep CNN Features In Isolated Sign Recognition
• [cs.CV]Frustratingly Simple Domain Generalization via Image Stylization
• [cs.CV]Generative Patch Priors for Practical Compressive Image Recovery
• [cs.CV]Hyperparameter Analysis for Image Captioning
• [cs.CV]Image classification in frequency domain with 2SReLU: a second harmonics superposition activation function
• [cs.CV]Keep Your AI-es on the Road: Tackling Distracted Driver Detection with Convolutional Neural Networks and Targetted Data Augmentation
• [cs.CV]Learning non-rigid surface reconstruction from spatio-temporal image patches
• [cs.CV]Lookahead Adversarial Semantic Segmentation
• [cs.CV]Melanoma Diagnosis with Spatio-Temporal Feature Learning on Sequential Dermoscopic Images
• [cs.CV]Pupil Center Detection Approaches: A comparative analysis
• [cs.CV]Shop The Look: Building a Large Scale Visual Shopping System at Pinterest
• [cs.CV]Unified Representation Learning for Efficient Medical Image Analysis
• [cs.CV]Wave Propagation of Visual Stimuli in Focus of Attention
• [cs.CY]A Methodology for Assessing the Environmental Effects Induced by ICT Services. Part I: Single Services
• [cs.CY]A Methodology for Assessing the Environmental Effects Induced by ICT Services. Part II: Multiple Services and Companies
• [cs.CY]All you can stream: Investigating the role of user behavior for greenhouse gas intensity of video streaming
• [cs.CY]Counting Risk Increments to Make Decisions During an Epidemic
• [cs.CY]Dashboard of sentiment in Austrian social media during COVID-19
• [cs.CY]Extracting Topics from Open Educational Resources
• [cs.CY]N=1 Modelling of Lifestyle Impact on SleepPerformance
• [cs.CY]Pervasive Communications Technologies For Managing Pandemics
• [cs.CY]Recommendations for Emerging Air Taxi Network Operations based on Online Review Analysis of Helicopter Services
• [cs.CY]SSHealth: Toward Secure, Blockchain-Enabled Healthcare Systems
• [cs.CY]The EuroSys 2020 Online Conference: Experience and lessons learned
• [cs.DC]Influence of Incremental Constraints on Energy Consumption and Static Scheduling Time for Moldable Tasks with Deadline
• [cs.DC]Is Network the Bottleneck of Distributed Training?
• [cs.DS]$λ$-Regularized A-Optimal Design and its Approximation by $λ$-Regularized Proportional Volume Sampling
• [cs.HC]On the Principle of Accountability: Challenges for Smart Homes & Cybersecurity
• [cs.IR]A Qualitative Evaluation of Language Models on Automatic Question-Answering for COVID-19
• [cs.IR]Convolutional Gaussian Embeddings for Personalized Recommendation with Uncertainty
• [cs.IR]Disentangling User Interest and Popularity Bias for Recommendation with Causal Embedding
• [cs.IR]MIMICS: A Large-Scale Data Collection for Search Clarification
• [cs.IT]Entropy and relative entropy from information-theoretic principles
• [cs.IT]List decoding of Convolutional Codes over integer residue rings
• [cs.LG]A Better Alternative to Error Feedback for Communication-Efficient Distributed Learning
• [cs.LG]A Reinforcement Learning Approach for Transient Control of Liquid Rocket Engines
• [cs.LG]A general framework for defining and optimizing robustness
• [cs.LG]AI Feynman 2.0: Pareto-optimal symbolic regression exploiting graph modularity
• [cs.LG]Abstract Diagrammatic Reasoning with Multiplex Graph Networks
• [cs.LG]Adversarial Attacks for Multi-view Deep Models
• [cs.LG]An operator view of policy gradient methods
• [cs.LG]Bayesian Optimization with Missing Inputs
• [cs.LG]Denoising Diffusion Probabilistic Models
• [cs.LG]Differentiable Language Model Adversarial Attacks on Categorical Sequence Classifiers
• [cs.LG]Differentially Private Variational Autoencoders with Term-wise Gradient Aggregation
• [cs.LG]Does Explainable Artificial Intelligence Improve Human Decision-Making?
• [cs.LG]Evaluating Prediction-Time Batch Normalization for Robustness under Covariate Shift
• [cs.LG]Fair clustering via equitable group representations
• [cs.LG]Fast Matrix Square Roots with Applications to Gaussian Processes and Bayesian Optimization
• [cs.LG]FedFMC: Sequential Efficient Federated Learning on Non-iid Data
• [cs.LG]From Discrete to Continuous Convolution Layers
• [cs.LG]Gradient boosting machine with partially randomized decision trees
• [cs.LG]Gradient descent follows the regularization path for general losses
• [cs.LG]Graph Pooling with Node Proximity for Hierarchical Representation Learning
• [cs.LG]Monash University, UEA, UCR Time Series Regression Archive
• [cs.LG]NROWAN-DQN: A Stable Noisy Network with Noise Reduction and Online Weight Adjustment for Exploration
• [cs.LG]On Reward-Free Reinforcement Learning with Linear Function Approximation
• [cs.LG]Open Problem: Model Selection for Contextual Bandits
• [cs.LG]Pervasive Lying Posture Tracking
• [cs.LG]Poisson Learning: Graph Based Semi-Supervised Learning At Very Low Label Rates
• [cs.LG]Probabilistic Fair Clustering
• [cs.LG]Recovering Petaflops in Contrastive Semi-Supervised Learning of Visual Representations
• [cs.LG]SOLA: Continual Learning with Second-Order Loss Approximation
• [cs.LG]Sparse GPU Kernels for Deep Learning
• [cs.LG]Statistical and Algorithmic Insights for Semi-supervised Learning with Self-training
• [cs.LG]Subgraph Neural Networks
• [cs.LG]Towards an Adversarially Robust Normalization Approach
• [cs.LG]Understanding Anomaly Detection with Deep Invertible Networks through Hierarchies of Distributions and Features
• [cs.LO]Common equivalence and size after forgetting
• [cs.LO]Graphs with Multiple Sources per Vertex
• [cs.LO]Learning to Prove from Synthetic Theorems
• [cs.NE]Exploratory Landscape Analysis is Strongly Sensitive to the Sampling Strategy
• [cs.NE]Genetic Programming visitation scheduling solution can deliver a less austere COVID-19 pandemic population lockdown
• [cs.NE]Hybridizing the 1/5-th Success Rule with Q-Learning for Controlling the Mutation Rate of an Evolutionary Algorithm
• [cs.NE]Particle Swarm Optimization with Velocity Restriction and Evolutionary Parameters Selection for Scheduling Problem
• [cs.NE]The cyclic job-shop scheduling problem: The new subclass of the job-shop problem and applying the Simulated annealing to solve it
• [cs.RO]A Computational Multi-Criteria Optimization Approach to Controller Design for Physical Human-Robot Interaction
• [cs.RO]Distributed prediction of unsafe reconfiguration scenarios of modular-robotic Programmable Matter
• [cs.RO]Low-cost Retina-like Robotic Lidars Based on Incommensurable Scanning
• [cs.RO]Semantic Linking Maps for Active Visual Object Search
• [cs.SE]A First Look at Android Applications in Google Play related to Covid-19
• [cs.SI]HPRA: Hyperedge Prediction using Resource Allocation
• [cs.SI]Measuring Adolescents’ Well-being: Correspondence of Naive Digital Traces to Survey Data
• [cs.SI]Opinion Maximization in Social Trust Networks
• [cs.SI]Rumor source detection with multiple observations under adaptive diffusions
• [cs.SI]SenWave: Monitoring the Global Sentiments under the COVID-19 Pandemic
• [eess.AS]Efficient Active Learning for Automatic Speech Recognition via Augmented Consistency Regularization
• [eess.AS]Joint Speaker Counting, Speech Recognition, and Speaker Identification for Overlapped Speech of Any Number of Speakers
• [eess.AS]Towards Reliable Real-time Opera Tracking: Combining Alignment with Audio Event Detectors to Increase Robustness
• [eess.IV]A machine learning-based method for estimating the number and orientations of major fascicles in diffusion-weighted magnetic resonance imaging
• [eess.IV]Concatenated Attention Neural Network for Image Restoration
• [eess.IV]Model-Aware Regularization For Learning Approaches To Inverse Problems
• [eess.SP]A Deep Learning Framework for Hybrid Beamforming Without Instantaneous CSI Feedback
• [eess.SP]Localized Spectral Graph Filter Frames: A Unifying Framework, Survey of Design Considerations, and Numerical Comparison
• [eess.SP]Reconfigurable Intelligent Surfaces and Metamaterials: The Potential of Wave Propagation Control for 6G Wireless Communications
• [eess.SY]Learning Controllers for Unstable Linear Quadratic Regulators from a Single Trajectory
• [math.FA]The uncertainty principle: variations on a theme
• [math.OC]Apollonius Allocation Algorithm for Heterogeneous Pursuers to Capture Multiple Evaders
• [math.OC]How Does Momentum Help Frank Wolfe?
• [math.OC]On the Almost Sure Convergence of Stochastic Gradient Descent in Non-Convex Problems
• [math.PR]Large-scale parallel server system with multi-component jobs
• [math.PR]The Hermitian Jacobi process: simplified formula for the moments and application to optical fibers MIMO channels
• [math.ST]Minimax rates without the fixed sample size assumption
• [math.ST]Mixture of Conditional Gaussian Graphical Models for unlabelled heterogeneous populations in the presence of co-factors
• [math.ST]Notion of information and independent component analysis
• [math.ST]Relaxing monotonicity in endogenous selection models and application to surveys
• [physics.med-ph]Using Deep Learning to Predict Beam-Tunable Pareto Optimal Dose Distribution for Intensity Modulated Radiation Therapy
• [physics.soc-ph]Computational model on COVID-19 Pandemic using Probabilistic Cellular Automata
• [physics.soc-ph]Gauging the happiness benefit of US urban parks through Twitter
• [q-bio.NC]An adversarial algorithm for variational inference with a new role for acetylcholine
• [q-bio.NC]Oscillatory background activity implements a backbone for sampling-based computations in spiking neural networks
• [quant-ph]Certified Randomness from Bell’s Theorem and Remote State Preparation Dimension Witness
• [quant-ph]Semi-supervised time series classification method for quantum computing
• [stat.ME]Bayesian analysis of mixture autoregressive models covering the complete parameter space
• [stat.ME]Proper scoring rules for evaluating asymmetry in density forecasting
• [stat.ME]Short Communication: Detecting Possibly Frequent Change-points: Wild Binary Segmentation 2
• [stat.ME]Sparse Quantile Regression
• [stat.ME]The Normal-Generalised Gamma-Pareto process: A novel pure-jump Lévy process with flexible tail and jump-activity properties
• [stat.ME]Time series copula models using d-vines and v-transforms: an alternative to GARCH modelling
• [stat.ML]An analytic theory of shallow networks dynamics for hinge loss classification
• [stat.ML]Bypassing Gradients Re-Projection with Episodic Memories in Online Continual Learning
• [stat.ML]Classifier uncertainty: evidence, potential impact, and probabilistic treatment
• [stat.ML]Fast Mixing of Multi-Scale Langevin Dynamics underthe Manifold Hypothesis
• [stat.ML]Feature Interaction Interpretability: A Case for Explaining Ad-Recommendation Systems via Neural Interaction Detection
• [stat.ML]How does this interaction affect me? Interpretable attribution for feature interactions
• [stat.ML]Independent innovation analysis for nonlinear vector autoregressive process
• [stat.ML]Latent variable modeling with random features
• [stat.ML]No one-hidden-layer neural network can represent multivariable functions
• [stat.ML]Stochastic Gradient Descent in Hilbert Scales: Smoothness, Preconditioning and Earlier Stopping
• [stat.ML]Understanding Recurrent Neural Networks Using Nonequilibrium Response Theory
• [stat.ML]Universal Lower-Bounds on Classification Error under Adversarial Attacks and Random Corruption
·····································
• [cs.AI]Contextual and Possibilistic Reasoning for Coalition Formation
Antonis Bikakis, Patrice Caire
http://arxiv.org/abs/2006.11097v1
• [cs.AI]Learn to Earn: Enabling Coordination within a Ride Hailing Fleet
Harshal A. Chaudhari, John W. Byers, Evimaria Terzi
http://arxiv.org/abs/2006.10904v1
• [cs.AI]Learning Optimal Power Flow: Worst-Case Guarantees for Neural Networks
Andreas Venzke, Guannan Qu, Steven Low, Spyros Chatzivasileiadis
http://arxiv.org/abs/2006.11029v1
• [cs.AI]Representing Pure Nash Equilibria in Argumentation
Bruno Yun, Srdjan Vesic, Nir Oren
http://arxiv.org/abs/2006.11020v1
• [cs.CL]A Survey of Syntactic-Semantic Parsing Based on Constituent and Dependency Structures
Meishan Zhang
http://arxiv.org/abs/2006.11056v1
• [cs.CL]Dataset for Automatic Summarization of Russian News
Ilya Gusev
http://arxiv.org/abs/2006.11063v1
• [cs.CL]Explainable and Discourse Topic-aware Neural Language Understanding
Yatin Chaudhary, Hinrich Schütze, Pankaj Gupta
http://arxiv.org/abs/2006.10632v2
• [cs.CL]Exploring Processing of Nested Dependencies in Neural-Network Language Models and Humans
Yair Lakretz, Dieuwke Hupkes, Alessandra Vergallito, Marco Marelli, Marco Baroni, Stanislas Dehaene
http://arxiv.org/abs/2006.11098v1
• [cs.CL]Neural Topic Modeling with Continual Lifelong Learning
Pankaj Gupta, Yatin Chaudhary, Thomas Runkler, Hinrich Schütze
http://arxiv.org/abs/2006.10909v1
• [cs.CL]New Vietnamese Corpus for Machine ReadingComprehension of Health News Articles
Kiet Van Nguyen, Duc-Vu Nguyen, Anh Gia-Tuan Nguyen, Ngan Luu-Thuy Nguyen
http://arxiv.org/abs/2006.11138v1
• [cs.CL]Sentiment Frames for Attitude Extraction in Russian
Natalia Loukachevitch, Nicolay Rusnachenko
http://arxiv.org/abs/2006.10973v1
• [cs.CR]Analyzing the Real-World Applicability of DGA Classifiers
Arthur Drichel, Ulrike Meyer, Samuel Schüppen, Dominik Teubert
http://arxiv.org/abs/2006.11103v1
• [cs.CR]Backdoor Attacks to Graph Neural Networks
Zaixi Zhang, Jinyuan Jia, Binghui Wang, Neil Zhenqiang Gong
http://arxiv.org/abs/2006.11165v1
• [cs.CR]Systematic Attack Surface Reduction For Deployed Sentiment Analysis Models
Josh Kalin, David Noever, Gerry Dozier
http://arxiv.org/abs/2006.11130v1
• [cs.CV]Adaptive feature recombination and recalibration for semantic segmentation with Fully Convolutional Networks
Sergio Pereira, Adriano Pinto, Joana Amorim, Alexandrine Ribeiro, Victor Alves, Carlos A. Silva
http://arxiv.org/abs/2006.11193v1
• [cs.CV]Attention Mesh: High-fidelity Face Mesh Prediction in Real-time
Ivan Grishchenko, Artsiom Ablavatski, Yury Kartynnik, Karthik Raveendran, Matthias Grundmann
http://arxiv.org/abs/2006.10962v1
• [cs.CV]Center-based 3D Object Detection and Tracking
Tianwei Yin, Xingyi Zhou, Philipp Krähenbühl
http://arxiv.org/abs/2006.11275v1
• [cs.CV]Compositional Learning of Image-Text Query for Image Retrieval
Muhammad Umer Anwaar, Egor Labintcev, Martin Kleinsteuber
http://arxiv.org/abs/2006.11149v1
• [cs.CV]Consistency Guided Scene Flow Estimation
Yuhua Chen, Luc Van Gool, Cordelia Schmid, Cristian Sminchisescu
http://arxiv.org/abs/2006.11242v1
• [cs.CV]Cross-denoising Network against Corrupted Labels in Medical Image Segmentation with Domain Shift
Qinming Zhang, Luyan Liu, Kai Ma, Cheng Zhuo, Yefeng Zheng
http://arxiv.org/abs/2006.10990v1
• [cs.CV]Deep Image Translation for Enhancing Simulated Ultrasound Images
Lin Zhang, Tiziano Portenier, Christoph Paulus, Orcun Goksel
http://arxiv.org/abs/2006.10850v1
• [cs.CV]Deep Learning-based Single Image Face Depth Data Enhancement
Torsten Schlett, Christian Rathgeb, Christoph Busch
http://arxiv.org/abs/2006.11091v1
• [cs.CV]Deep Transformation-Invariant Clustering
Tom Monnier, Thibault Groueix, Mathieu Aubry
http://arxiv.org/abs/2006.11132v1
• [cs.CV]Deep Transformation-Invariant Clustering
Tom Monnier, Thibault Groueix, Mathieu Aubry
http://arxiv.org/abs/2
1000
006.11132v1
1000
006.11132v1)
• [cs.CV]Emotion Recognition on large video dataset based on Convolutional Feature Extractor and Recurrent Neural Network
Denis Rangulov, Muhammad Fahim
http://arxiv.org/abs/2006.11168v1
• [cs.CV]Evaluation Of Hidden Markov Models Using Deep CNN Features In Isolated Sign Recognition
Anil Osman Tur, Hacer Yalim Keles
http://arxiv.org/abs/2006.11183v1
• [cs.CV]Frustratingly Simple Domain Generalization via Image Stylization
Nathan Somavarapu, Chih-Yao Ma, Zsolt Kira
http://arxiv.org/abs/2006.11207v1
• [cs.CV]Generative Patch Priors for Practical Compressive Image Recovery
Rushil Anirudh, Suhas Lohit, Pavan Turaga
http://arxiv.org/abs/2006.10873v1
• [cs.CV]Hyperparameter Analysis for Image Captioning
Amish Patel, Aravind Varier
http://arxiv.org/abs/2006.10923v1
• [cs.CV]Image classification in frequency domain with 2SReLU: a second harmonics superposition activation function
Thomio Watanabe, Denis F. Wolf
http://arxiv.org/abs/2006.10853v1
• [cs.CV]Keep Your AI-es on the Road: Tackling Distracted Driver Detection with Convolutional Neural Networks and Targetted Data Augmentation
Nikka Mofid, Jasmine Bayrooti, Shreya Ravi
http://arxiv.org/abs/2006.10955v1
• [cs.CV]Learning non-rigid surface reconstruction from spatio-temporal image patches
Matteo Pedone, Abdelrahman Mostafa, Janne heikkilä
http://arxiv.org/abs/2006.10841v1
• [cs.CV]Lookahead Adversarial Semantic Segmentation
Hadi Jamali-Rad, Attila Szabo, Matteo Presutto
http://arxiv.org/abs/2006.11227v1
• [cs.CV]Melanoma Diagnosis with Spatio-Temporal Feature Learning on Sequential Dermoscopic Images
Zhen Yu, Jennifer Nguyen, Xiaojun Chang, John Kelly, Catriona Mclean, Lei Zhang, Victoria Mar, Zongyuan Ge
http://arxiv.org/abs/2006.10950v1
• [cs.CV]Pupil Center Detection Approaches: A comparative analysis
Talía Vázquez Romaguera, Liset Vázquez Romaguera, David Castro Piñol, Carlos Román Vázquez Seisdedos
http://arxiv.org/abs/2006.11147v1
• [cs.CV]Shop The Look: Building a Large Scale Visual Shopping System at Pinterest
Raymond Shiau, Hao-Yu Wu, Eric Kim, Yue Li Du, Anqi Guo, Zhiyuan Zhang, Eileen Li, Kunlong Gu, Charles Rosenberg, Andrew Zhai
http://arxiv.org/abs/2006.10866v1
• [cs.CV]Unified Representation Learning for Efficient Medical Image Analysis
Ghada Zamzmi, Sivaramakrishnan Rajaraman, Sameer Antani
http://arxiv.org/abs/2006.11223v1
• [cs.CV]Wave Propagation of Visual Stimuli in Focus of Attention
Lapo Faggi, Alessandro Betti, Dario Zanca, Stefano Melacci, Marco Gori
http://arxiv.org/abs/2006.11035v1
• [cs.CY]A Methodology for Assessing the Environmental Effects Induced by ICT Services. Part I: Single Services
Vlad C. Coroamă, Pernilla Bergmark, Mattias Höjer, Jens Malmodin
http://arxiv.org/abs/2006.10831v1
• [cs.CY]A Methodology for Assessing the Environmental Effects Induced by ICT Services. Part II: Multiple Services and Companies
Pernilla Bergmark, Vlad C. Coroamă, Mattias Höjer, Craig Donovan
http://arxiv.org/abs/2006.10838v1
• [cs.CY]All you can stream: Investigating the role of user behavior for greenhouse gas intensity of video streaming
Paul Suski, Johanna Pohl, Vivian Frick
http://arxiv.org/abs/2006.11129v1
• [cs.CY]Counting Risk Increments to Make Decisions During an Epidemic
Lucien Hardy
http://arxiv.org/abs/2006.11244v1
• [cs.CY]Dashboard of sentiment in Austrian social media during COVID-19
Max Pellert, Jana Lasser, Hannah Metzler, David Garcia
http://arxiv.org/abs/2006.11158v1
• [cs.CY]Extracting Topics from Open Educational Resources
Mohammadreza Molavi, Mohammadreza Tavakoli, Gábor Kismihók
http://arxiv.org/abs/2006.11109v1
• [cs.CY]N=1 Modelling of Lifestyle Impact on SleepPerformance
Dhruv Upadhyay, Vaibhav Pandey, Nitish Nag, Ramesh Jain
http://arxiv.org/abs/2006.10884v1
• [cs.CY]Pervasive Communications Technologies For Managing Pandemics
Muhammad Ilyas, Basit Qureshi
http://arxiv.org/abs/2006.10805v1
• [cs.CY]Recommendations for Emerging Air Taxi Network Operations based on Online Review Analysis of Helicopter Services
Suchithra Rajendran, Emily Pagel
http://arxiv.org/abs/2006.10898v1
• [cs.CY]SSHealth: Toward Secure, Blockchain-Enabled Healthcare Systems
Alaa Awad Abdellatif, Abeer Z. Al-Marridi, Amr Mohamed, Aiman Erbad, Carla Fabiana Chiasserini, Ahmed Refaey
http://arxiv.org/abs/2006.10843v1
• [cs.CY]The EuroSys 2020 Online Conference: Experience and lessons learned
Angelos Bilas, Dejan Kostic, Kostas Magoutis, Evangelos Markatos, Dushyanth Narayanan, Peter Pietzuch, Margo Seltzer
http://arxiv.org/abs/2006.11068v1
• [cs.DC]Influence of Incremental Constraints on Energy Consumption and Static Scheduling Time for Moldable Tasks with Deadline
Jörg Keller, Sebastian Litzinger
http://arxiv.org/abs/2006.11062v1
• [cs.DC]Is Network the Bottleneck of Distributed Training?
Zhen Zhang, Chaokun Chang, Haibin Lin, Yida Wang, Raman Arora, Xin Jin
http://arxiv.org/abs/2006.10103v2
• [cs.DS]$λ$-Regularized A-Optimal Design and its Approximation by $λ$-Regularized Proportional Volume Sampling
Uthaipon Tantipongpipat
http://arxiv.org/abs/2006.11182v1
• [cs.HC]On the Principle of Accountability: Challenges for Smart Homes & Cybersecurity
Lachlan Urquhart, Jiahong Chen
http://arxiv.org/abs/2006.11043v1
• [cs.IR]A Qualitative Evaluation of Language Models on Automatic Question-Answering for COVID-19
David Oniani, Yanshan Wang
http://arxiv.org/abs/2006.10964v1
• [cs.IR]Convolutional Gaussian Embeddings for Personalized Recommendation with Uncertainty
Junyang Jiang, Deqing Yang, Yanghua Xiao, Chenlu Shen
http://arxiv.org/abs/2006.10932v1
• [cs.IR]Disentangling User Interest and Popularity Bias for Recommendation with Causal Embedding
Yu Zheng, Chen Gao, Xiang Li, Xiangnan He, Yong Li, Depeng Jin
http://arxiv.org/abs/2006.11011v1
• [cs.IR]MIMICS: A Large-Scale Data Collection for Search Clarification
Hamed Zamani, Gord Lueck, Everest Chen, Rodolfo Quispe, Flint Luu, Nick Craswell
http://arxiv.org/abs/10a3
s/2006.10174v1
s/2006.10174v1)
• [cs.IT]Entropy and relative entropy from information-theoretic principles
Gilad Gour, Marco Tomamichel
http://arxiv.org/abs/2006.11164v1
• [cs.IT]List decoding of Convolutional Codes over integer residue rings
Julia Lieb, Diego Napp, Raquel Pinto
http://arxiv.org/abs/2006.11245v1
• [cs.LG]A Better Alternative to Error Feedback for Communication-Efficient Distributed Learning
Samuel Horváth, Peter Richtárik
http://arxiv.org/abs/2006.11077v1
• [cs.LG]A Reinforcement Learning Approach for Transient Control of Liquid Rocket Engines
Günther Waxenegger-Wilfing, Kai Dresia, Jan Christian Deeken, Michael Oschwald
http://arxiv.org/abs/2006.11108v1
• [cs.LG]A general framework for defining and optimizing robustness
Alessandro Tibo, Manfred Jaeger, Kim G. Larsen
http://arxiv.org/abs/2006.11122v1
• [cs.LG]AI Feynman 2.0: Pareto-optimal symbolic regression exploiting graph modularity
Silviu-Marian Udrescu, Andrew Tan, Jiahai Feng, Orisvaldo Neto, Tailin Wu, Max Tegmark
http://arxiv.org/abs/2006.10782v1
• [cs.LG]Abstract Diagrammatic Reasoning with Multiplex Graph Networks
Duo Wang, Mateja Jamnik, Pietro Lio
http://arxiv.org/abs/2006.11197v1
• [cs.LG]Adversarial Attacks for Multi-view Deep Models
Xuli Sun, Shiliang Sun
http://arxiv.org/abs/2006.11004v1
• [cs.LG]An operator view of policy gradient methods
Dibya Ghosh, Marlos C. Machado, Nicolas Le Roux
http://arxiv.org/abs/2006.11266v1
• [cs.LG]Bayesian Optimization with Missing Inputs
Phuc Luong, Dang Nguyen, Sunil Gupta, Santu Rana, Svetha Venkatesh
http://arxiv.org/abs/2006.10948v1
• [cs.LG]Denoising Diffusion Probabilistic Models
Jonathan Ho, Ajay Jain, Pieter Abbeel
http://arxiv.org/abs/2006.11239v1
• [cs.LG]Differentiable Language Model Adversarial Attacks on Categorical Sequence Classifiers
I. Fursov, A. Zaytsev, N. Kluchnikov, A. Kravchenko, E. Burnaev
http://arxiv.org/abs/2006.11078v1
• [cs.LG]Differentially Private Variational Autoencoders with Term-wise Gradient Aggregation
Tsubasa Takahashi, Shun Takagi, Hajime Ono, Tatsuya Komatsu
http://arxiv.org/abs/2006.11204v1
• [cs.LG]Does Explainable Artificial Intelligence Improve Human Decision-Making?
Yasmeen Alufaisan, Laura R. Marusich, Jonathan Z. Bakdash, Yan Zhou, Murat Kantarcioglu
http://arxiv.org/abs/2006.11194v1
• [cs.LG]Evaluating Prediction-Time Batch Normalization for Robustness under Covariate Shift
Zachary Nado, Shreyas Padhy, D. Sculley, Alexander D’Amour, Balaji Lakshminarayanan, Jasper Snoek
http://arxiv.org/abs/2006.10963v1
• [cs.LG]Fair clustering via equitable group representations
Mohsen Abbasi, Aditya Bhaskara, Suresh Venkatasubramanian
http://arxiv.org/abs/2006.11009v1
• [cs.LG]Fast Matrix Square Roots with Applications to Gaussian Processes and Bayesian Optimization
Geoff Pleiss, Martin Jankowiak, David Eriksson, Anil Damle, Jacob R. Gardner
http://arxiv.org/abs/2006.11267v1
• [cs.LG]FedFMC: Sequential Efficient Federated Learning on Non-iid Data
Kavya Kopparapu, Eric Lin
http://arxiv.org/abs/2006.10937v1
• [cs.LG]From Discrete to Continuous Convolution Layers
Assaf Shocher, Ben Feinstein, Niv Haim, Michal Irani
http://arxiv.org/abs/2006.11120v1
• [cs.LG]Gradient boosting machine with partially randomized decision trees
Andrei V. Konstantinov, Lev V. Utkin
http://arxiv.org/abs/2006.11014v1
• [cs.LG]Gradient descent follows the regularization path for general losses
Ziwei Ji, Miroslav Dudík, Robert E. Schapire, Matus Telgarsky
http://arxiv.org/abs/2006.11226v1
• [cs.LG]Graph Pooling with Node Proximity for Hierarchical Representation Learning
Xing Gao, Wenrui Dai, Chenglin Li, Hongkai Xiong, Pascal Frossard
http://arxiv.org/abs/2006.11118v1
• [cs.LG]Monash University, UEA, UCR Time Series Regression Archive
Chang Wei Tan, Christoph Bergmeir, Francois Petitjean, Geoffrey I. Webb
http://arxiv.org/abs/2006.10996v1
• [cs.LG]NROWAN-DQN: A Stable Noisy Network with Noise Reduction and Online Weight Adjustment for Exploration
Shuai Han, Wenbo Zhou, Jing Liu, Shuai Lü
http://arxiv.org/abs/2006.10980v1
• [cs.LG]On Reward-Free Reinforcement Learning with Linear Function Approximation
Ruosong Wang, Simon S. Du, Lin F. Yang, Ruslan Salakhutdinov
http://arxiv.org/abs/2006.11274v1
• [cs.LG]Open Problem: Model Selection for Contextual Bandits
Dylan J. Foster, Akshay Krishnamurthy, Haipeng Luo
http://arxiv.org/abs/2006.10940v1
• [cs.LG]Pervasive Lying Posture Tracking
Paratoo Alinia, Ali Samadani, Mladen Milosevic, Hassan Ghasemzadeh, Saman Parvaneh
http://arxiv.org/abs/2006.10931v1
• [cs.LG]Poisson Learning: Graph Based Semi-Supervised Learning At Very Low Label Rates
Jeff Calder, Brendan Cook, Matthew Thorpe, Dejan Slepcev
http://arxiv.org/abs/2006.11184v1
• [cs.LG]Probabilistic Fair Clustering
Seyed A. Esmaeili, Brian Brubach, Leonidas Tsepenekas, John P. Dickerson
http://arxiv.org/abs/2006.10916v1
• [cs.LG]Recovering Petaflops in Contrastive Semi-Supervised Learning of Visual Representations
Mahmoud Assran, Nicolas Ballas, Lluis Castrejon, Michael Rabbat
http://arxiv.org/abs/2006.10803v1
• [cs.LG]SOLA: Continual Learning with Second-Order Loss Approximation
Dong Yin, Mehrdad Farajtabar, Ang Li
http://arxiv.org/abs/2006.10974v1
• [cs.LG]Sparse GPU Kernels for Deep Learning
Trevor Gale, Matei Zaharia, Cliff Young, Erich Elsen
http://arxiv.org/abs/2006.10901v1
• [cs.LG]Statistical and Algorithmic Insights for Semi-supervised Learning with Self-training
Samet Oymak, Talha Cihad Gulcu
http://arxiv.org/abs/2006.11006v1
• [cs.LG]Subgraph Neural Networks
Emily Alsentzer, Samuel G. Finlayson, Michelle M. Li, Marinka Zitnik
http://arxiv.org/abs/2006.10538v2
• [cs.LG]Towards an Adversarially Robust Normalization Approach
Muhammad Awais, Fahad Shamshad, Sung-Ho Bae
http://arxiv.org/abs/2006.11007v1
• [cs.LG]Understanding Anomaly Detection with Deep Invertible Networks through Hierarchies of Distributions and Features
Robin Tibor Schirrmeister, Yuxuan Zhou, Tonio Ball, Dan Zhang
http://arxiv.org/abs/2006.10848v1
• [cs.LO]Common equivalence and size after forgetting
Paolo Liberatore
http://arxiv.org/abs/2006.11152v1
• [cs.LO]Graphs with Multiple Sources per Vertex
Martin van Harmelen, Jonas Groschwitz
http://arxiv.org/abs/2006.11159v1
• [cs.LO]Learning to Prove from Synthetic Theorems
Eser Aygün, Zafarali Ahmed, Ankit Anand, Vlad Firoiu, Xavier Glorot, Laurent Orseau, Doina Precup, Shibl Mourad
http://arxiv.org/abs/2006.11259v1
• [cs.NE]Exploratory Landscape Analysis is Strongly Sensitive to the Sampling Strategy
Quentin Renau, Carola Doerr, Johann Dreo, Benjamin Doerr
http://arxiv.org/abs/2006.11135v1
• [cs.NE]Genetic Programming visitation scheduling solution can deliver a less austere COVID-19 pandemic population lockdown
Daniel Howard
http://arxiv.org/abs/2006.10748v1
• [cs.NE]Hybridizing the 1/5-th Success Rule with Q-Learning for Controlling the Mutation Rate of an Evolutionary Algorithm
Arina Buzdalova, Carola Doerr, Anna Rodionova
http://arxiv.org/abs/2006.11026v1
• [cs.NE]Particle Swarm Optimization with Velocity Restriction and Evolutionary Parameters Selection for Scheduling Problem
Pavel Matrenin, Viktor Sekaev
http://arxiv.org/abs/2006.10935v1
• [cs.NE]The cyclic job-shop scheduling problem: The new subclass of the job-shop problem and applying the Simulated annealing to solve it
Pavel Matrenin, Vadim Manusov
http://arxiv.org/abs/2006.10938v1
• [cs.RO]A Computational Multi-Criteria Optimization Approach to Controller Design for Physical Human-Robot Interaction
Yusuf Aydin, Ozan Tokatli, Volkan Patoglu, Cagatay Basdogan
http://arxiv.org/abs/2006.11218v1
• [cs.RO]Distributed prediction of unsafe reconfiguration scenarios of modular-robotic Programmable Matter
Benoît Piranda, Paweł Chodkiewicz, Paweł Hołobut, Stéphane Bordas, Julien Bourgeois, Jakub Lengiewicz
http://arxiv.org/abs/2006.11071v1
• [cs.RO]Low-cost Retina-like Robotic Lidars Based on Incommensurable Scanning
Zheng Liu, Fu Zhang, Xiaoping Hong
http://arxiv.org/abs/2006.11034v1
• [cs.RO]Semantic Linking Maps for Active Visual Object Search
Zhen Zeng, Adrian Röfer, Odest Chadwicke Jenkins
http://arxiv.org/abs/2006.10807v1
• [cs.SE]A First Look at Android Applications in Google Play related to Covid-19
Jordan Samhi, Kevin Allix, Tegawendé F. Bissyandé, Jacques Klein
http://arxiv.org/abs/2006.11002v1
• [cs.SI]HPRA: Hyperedge Prediction using Resource Allocation
Tarun Kumar, K Darwin, Srinivasan Parthasarathy, Balaraman Ravindran
http://arxiv.org/abs/2006.11070v1
• [cs.SI]Measuring Adolescents’ Well-being: Correspondence of Naive Digital Traces to Survey Data
Elizaveta Sivak, Ivan Smirnov
http://arxiv.org/abs/2006.11176v1
• [cs.SI]Opinion Maximization in Social Trust Networks
Pinghua Xu, Wenbin Hu, Jia Wu, Weiwei Liu
http://arxiv.org/abs/2006.10961v1
• [cs.SI]Rumor source detection with multiple observations under adaptive diffusions
Miklos Z. Racz, Jacob Richey
http://arxiv.org/abs/2006.11211v1
• [cs.SI]SenWave: Monitoring the Global Sentiments under the COVID-19 Pandemic
Qiang Yang, Hind Alamro, Somayah Albaradei, Adil Salhi, Xiaoting Lv, Changsheng Ma, Manal Alshehri, Inji Jaber, Faroug Tifratene, Wei Wang, Takashi Gojobori, Carlos M. Duarte, Xin Gao, Xiangliang Zhang
http://arxiv.org/abs/2006.10842v1
• [eess.AS]Efficient Active Learning for Automatic Speech Recognition via Augmented Consistency Regularization
Jihwan Bang, Heesu Kim, YoungJoon Yoo, Jung-Woo Ha
http://arxiv.org/abs/2006.11021v1
• [eess.AS]Joint Speaker Counting, Speech Recognition, and Speaker Identification for Overlapped Speech of Any Number of Speakers
Naoyuki Kanda, Yashesh Gaur, Xiaofei Wang, Zhong Meng, Zhuo Chen, Tianyan Zhou, Takuya Yoshioka
http://arxiv.org/abs/2006.10930v1
• [eess.AS]Towards Reliable Real-time Opera Tracking: Combining Alignment with Audio Event Detectors to Increase Robustness
Charles Brazier, Gerhard Widmer
http://arxiv.org/abs/2006.11033v1
• [eess.IV]A machine learning-based method for estimating the number and orientations of major fascicles in diffusion-weighted magnetic resonance imaging
Davood Karimi, Lana Vasung, Camilo Jaimes, Fedel Machado-Rivas, Shadab Khan, Simon K. Warfield, Ali Gholipour
http://arxiv.org/abs/2006.11117v1
• [eess.IV]Concatenated Attention Neural Network for Image Restoration
Tian YingJie, Wang YiQi, Yang LinRui, Qi ZhiQuan
http://arxiv.org/abs/2006.11162v1
• [eess.IV]Model-Aware Regularization For Learning Approaches To Inverse Problems
Jaweria Amjad, Zhaoyan Lyu, Miguel R. D. Rodrigues
http://arxiv.org/abs/2006.10869v1
• [eess.SP]A Deep Learning Framework for Hybrid Beamforming Without Instantaneous CSI Feedback
Ahmet M. Elbir
http://arxiv.org/abs/2006.10971v1
• [eess.SP]Localized Spectral Graph Filter Frames: A Unifying Framework, Survey of Design Considerations, and Numerical Comparison
David I Shuman
http://arxiv.org/abs/2006.11220v1
• [eess.SP]Reconfigurable Intelligent Surfaces and Metamaterials: The Potential of Wave Propagation Control for 6G Wireless Communications
George C. Alexandropoulos, Geoffroy Lerosey, Merouane Debbah, Mathias Fink
http://arxiv.org/abs/2006.11136v1
• [eess.SY]Learning Controllers for Unstable Linear Quadratic Regulators from a Single Trajectory
Lenart Treven, Sebastian Curi, Mojmir Mutny, Andreas Krause
http://arxiv.org/abs/2006.11022v1
• [math.FA]The uncertainty principle: variations on a theme
Avi Wigderson, Yuval Wigderson
http://arxiv.org/abs/2006.11206v1
• [math.OC]Apollonius Allocation Algorithm for Heterogeneous Pursuers to Capture Multiple Evaders
Venkata Ramana Makkapati, Panagiotis Tsiotras
http://arxiv.org/abs/2006.10253v2
• [math.OC]How Does Momentum Help Frank Wolfe?
Bingcong Li, Mario Coutino, Georgios B. Giannakis, Geert Leus
http://arxiv.org/abs/2006.11116v1
• [math.OC]On the Almost Sure Convergence of Stochastic Gradient Descent in Non-Convex Problems
Panayotis Mertikopoulos, Nadav Hallak, Ali Kavis, Volkan Cevher
http://arxiv.org/abs/2006.11144v1
• [math.PR]Large-scale parallel server system with multi-component jobs
Seva Shneer, Alexander Stolyar
http://arxiv.org/abs/2006.11256v1
• [math.PR]The Hermitian Jacobi process: simplified formula for the moments and application to optical fibers MIMO channels
Nizar Demni, Tarek Hamdi, Abdessatar Souissi
http://arxiv.org/abs/2006.11187v1
• [math.ST]Minimax rates without the fixed sample size assumption
Alisa Kirichenko, Peter Grünwald
http://arxiv.org/abs/2006.11170v1
• [math.ST]Mixture of Conditional Gaussian Graphical Models for unlabelled heterogeneous populations in the presence of co-factors
Thomas Lartigue, Stanley Durrleman, Stéphanie Allassonnière
http://arxiv.org/abs/2006.11094v1
• [math.ST]Notion of information and independent component analysis
Una Radojicic, Klaus Nordhausen, Hannu Oja
http://arxiv.org/abs/2006.11123v1
• [math.ST]Relaxing monotonicity in endogenous selection models and application to surveys
Eric Gautier
http://arxiv.org/abs/2006.10997v1
• [physics.med-ph]Using Deep Learning to Predict Beam-Tunable Pareto Optimal Dose Distribution for Intensity Modulated Radiation Therapy
Gyanendra Bohara, Azar Sadeghnejad Barkousaraie, Steve Jiang, Dan Nguyen
http://arxiv.org/abs/2006.11236v1
• [physics.soc-ph]Computational model on COVID-19 Pandemic using Probabilistic Cellular Automata
Sayantari Ghosh, Saumik Bhattacharya
http://arxiv.org/abs/2006.11270v1
• [physics.soc-ph]Gauging the happiness benefit of US urban parks through Twitter
A. J. Schwartz, P. S. Dodds, J. P. M. O’Neil-Dunne, T. H. Ricketts, C. M. Danforth
http://arxiv.org/abs/2006.10658v1
• [q-bio.NC]An adversarial algorithm for variational inference with a new role for acetylcholine
Ari S. Benjamin, Konrad P. Kording
http://arxiv.org/abs/2006.10811v1
• [q-bio.NC]Oscillatory background activity implements a backbone for sampling-based computations in spiking neural networks
Michael G. Müller, Robert Legenstein
http://arxiv.org/abs/2006.11099v1
• [quant-ph]Certified Randomness from Bell’s Theorem and Remote State Preparation Dimension Witness
Xing Chen, Kai Redeker, Robert Garthoff, Wenjamin Rosenfeld, Jörg Wrachtrup, Ilja Gerhardt
http://arxiv.org/abs/2006.11137v1
• [quant-ph]Semi-supervised time series classification method for quantum computing
Sheir Yarkoni, Andrii Kleshchonok, Yury Dzerin, Florian Neukart, Marc Hilbert
http://arxiv.org/abs/2006.11031v1
• [stat.ME]Bayesian analysis of mixture autoregressive models covering the complete parameter space
Davide Ravagli, Georgi N. Boshnakov
http://arxiv.org/abs/2006.11041v1
• [stat.ME]Proper scoring rules for evaluating asymmetry in density forecasting
Matteo Iacopini, Francesco Ravazzolo, Luca Rossini
http://arxiv.org/abs/2006.11265v1
• [stat.ME]Short Communication: Detecting Possibly Frequent Change-points: Wild Binary Segmentation 2
Robert Lund, Xueheng Shi
http://arxiv.org/abs/2006.10845v1
• [stat.ME]Sparse Quantile Regression
Le-Yu Chen, Sokbae Lee
http://arxiv.org/abs/2006.11201v1
• [stat.ME]The Normal-Generalised Gamma-Pareto process: A novel pure-jump Lévy process with flexible tail and jump-activity properties
Fadhel Ayed, Juho Lee, François Caron
http://arxiv.org/abs/2006.10968v1
• [stat.ME]Time series copula models using d-vines and v-transforms: an alternative to GARCH modelling
Martin Bladt, Alexander J. McNeil
http://arxiv.org/abs/2006.11088v1
• [stat.ML]An analytic theory of shallow networks dynamics for hinge loss classification
Franco Pellegrini, Giulio Biroli
http://arxiv.org/abs/2006.11209v1
• [stat.ML]Bypassing Gradients Re-Projection with Episodic Memories in Online Continual Learning
Yu Chen, Tom Diethe, Peter Flach
http://arxiv.org/abs/2006.11234v1
• [stat.ML]Classifier uncertainty: evidence, potential impact, and probabilistic treatment
Niklas Tötsch, Daniel Hoffmann
http://arxiv.org/abs/2006.11105v1
• [stat.ML]Fast Mixing of Multi-Scale Langevin Dynamics underthe Manifold Hypothesis
Adam Block, Youssef Mroueh, Alexander Rakhlin, Jerret Ross
http://arxiv.org/abs/2006.11166v1
• [stat.ML]Feature Interaction Interpretability: A Case for Explaining Ad-Recommendation Systems via Neural Interaction Detection
Michael Tsang, Dehua Cheng, Hanpeng Liu, Xue Feng, Eric Zhou, Yan Liu
http://arxiv.org/abs/2006.10966v1
• [stat.ML]How does this interaction affect me? Interpretable attribution for feature interactions
Michael Tsang, Sirisha Rambhatla, Yan Liu
http://arxiv.org/abs/2006.10965v1
• [stat.ML]Independent innovation analysis for nonlinear vector autoregressive process
Hiroshi Morioka, Aapo Hyvärinen
http://arxiv.org/abs/2006.10944v1
• [stat.ML]Latent variable modeling with random features
Gregory W. Gundersen, Michael Minyi Zhang, Barbara E. Engelhardt
http://arxiv.org/abs/2006.11145v1
• [stat.ML]No one-hidden-layer neural network can represent multivariable functions
Masayo Inoue, Mana Futamura, Hirokazu Ninomiya
http://arxiv.org/abs/2006.10977v1
• [stat.ML]Stochastic Gradient Descent in Hilbert Scales: Smoothness, Preconditioning and Earlier Stopping
Nicole Mücke, Enrico Reiss
http://arxiv.org/abs/2006.10840v1
• [stat.ML]Understanding Recurrent Neural Networks Using Nonequilibrium Response Theory
Soon Hoe Lim
http://arxiv.org/abs/2006.11052v1
• [stat.ML]Universal Lower-Bounds on Classification Error under Adversarial Attacks and Random Corruption
Elvis Dohmatob
http://arxiv.org/abs/2006.09989v2