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