cs.AI - 人工智能

    cs.CG - 计算几何学 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.DB - 数据库 cs.DC - 分布式、并行与集群计算 cs.DM - 离散数学 cs.ET - 新兴技术 cs.FL - 形式语言与自动机理论 cs.HC - 人机接口 cs.IR - 信息检索 cs.IT - 信息论 cs.LG - 自动学习 cs.NE - 神经与进化计算 cs.NI - 网络和互联网体系结构 cs.RO - 机器人学 cs.SE - 软件工程 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 eess.SY - 系统和控制 math.CO - 组合数学 math.NA - 数值分析 math.NT - 数论 math.OC - 优化与控制 math.ST - 统计理论 physics.comp-ph - 计算物理学 physics.optics - 光学 physics.soc-ph - 物理学与社会 q-bio.PE - 人口与发展 q-bio.QM - 定量方法 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]Bounded Rationality in Las Vegas: Probabilistic Finite Automata PlayMulti-Armed Bandits
    • [cs.AI]Building Rule Hierarchies for Efficient Logical Rule Learning from Knowledge Graphs
    • [cs.AI]Mitigating Manipulation in Peer Review via Randomized Reviewer Assignments
    • [cs.AI]On Bellman’s Optimality Principle for zs-POSGs
    • [cs.AI]On Finite Entailment of Non-Local Queries in Description Logics
    • [cs.AI]Ontology-guided Semantic Composition for Zero-Shot Learning
    • [cs.CG]Subspace approximation with outliers
    • [cs.CL]A Data-driven Neural Network Architecture for Sentiment Analysis
    • [cs.CL]ANA at SemEval-2020 Task 4: mUlti-task learNIng for cOmmonsense reasoNing (UNION)
    • [cs.CL]Correction of Faulty Background Knowledge based on Condition Aware and Revise Transformer for Question Answering
    • [cs.CL]GShard: Scaling Giant Models with Conditional Computation and Automatic Sharding
    • [cs.CL]Improving Sequence Tagging for Vietnamese Text Using Transformer-based Neural Models
    • [cs.CL]Learning Sparse Prototypes for Text Generation
    • [cs.CL]PLATO-2: Towards Building an Open-Domain Chatbot via Curriculum Learning
    • [cs.CL]Rethinking Positional Encoding in Language Pre-training
    • [cs.CL]Technical Report: Auxiliary Tuning and its Application to Conditional Text Generation
    • [cs.CL]Universal linguistic inductive biases via meta-learning
    • [cs.CR]Adversarial Deep Ensemble: Evasion Attacks and Defenses for Malware Detection
    • [cs.CR]CyRes — Avoiding Catastrophic Failure in Connected and Autonomous Vehicles (Extended Abstract)
    • [cs.CR]Quantifying Susceptibility to Spear Phishing in a High School Environment Using Signal Detection Theory
    • [cs.CR]Towards Robust LiDAR-based Perception in Autonomous Driving: General Black-box Adversarial Sensor Attack and Countermeasures
    • [cs.CV]A Simple Domain Shifting Networkfor Generating Low Quality Images
    • [cs.CV]Actionable Attribution Maps for Scientific Machine Learning
    • [cs.CV]Asymmetric metric learning for knowledge transfer
    • [cs.CV]Beyond accuracy: quantifying trial-by-trial behaviour of CNNs and humans by measuring error consistency
    • [cs.CV]Boosting Deep Neural Networks with Geometrical Prior Knowledge: A Survey
    • [cs.CV]Can Your Face Detector Do Anti-spoofing? Face Presentation Attack Detection with a Multi-Channel Face Detector
    • [cs.CV]Classification Confidence Estimation with Test-Time Data-Augmentation
    • [cs.CV]Cross-Scale Internal Graph Neural Network for Image Super-Resolution
    • [cs.CV]Deep Isometric Learning for Visual Recognition
    • [cs.CV]ERNIE-ViL: Knowledge Enhanced Vision-Language Representations Through Scene Graph
    • [cs.CV]EasyQuant: Post-training Quantization via Scale Optimization
    • [cs.CV]FVV Live: Real-Time, Low-Cost, Free Viewpoint Video
    • [cs.CV]GramGAN: Deep 3D Texture Synthesis From 2D Exemplars
    • [cs.CV]ITSELF: Iterative Saliency Estimation fLexible Framework
    • [cs.CV]Large-scale inference of liver fat with neural networks on UK Biobank body MRI
    • [cs.CV]Learning Patterns of Tourist Movement and Photography from Geotagged Photos at Archaeological Heritage Sites in Cuzco, Peru
    • [cs.CV]Leveraging Temporal Information for 3D Detection and Domain Adaptation
    • [cs.CV]MSNet: A Multilevel Instance Segmentation Network for Natural Disaster Damage Assessment in Aerial Videos
    • [cs.CV]Material Recognition for Automated Progress Monitoring using Deep Learning Methods
    • [cs.CV]Method for the generation of depth images for view-based shape retrieval of 3D CAD model from partial point cloud
    • [cs.CV]Object Detection under Rainy Conditions for Autonomous Vehicles
    • [cs.CV]OccInpFlow: Occlusion-Inpainting Optical Flow Estimation by Unsupervised Learning
    • [cs.CV]On the Demystification of Knowledge Distillation: A Residual Network Perspective
    • [cs.CV]Primary Tumor Origin Classification of Lung Nodules in Spectral CT using Transfer Learning
    • [cs.CV]PriorGAN: Real Data Prior for Generative Adversarial Nets
    • [cs.CV]Quantitative Evaluation of Endoscopic SLAM Methods: EndoSLAM Dataset
    • [cs.CV]Rethinking CNN-Based Pansharpening: Guided Colorization of Panchromatic Images via GANs
    • [cs.CV]SAR Image Despeckling by Deep Neural Networks: from a pre-trained model to an end-to-end training strategy
    • [cs.CV]Self-Supervised Learning of a Biologically-Inspired Visual Texture Model
    • [cs.CV]Tackling Occlusion in Siamese Tracking with Structured Dropouts
    • [cs.CV]Uncertainty-aware multi-view co-training for semi-supervised medical image segmentation and domain adaptation
    • [cs.CV]Vehicle Attribute Recognition by Appearance: Computer Vision Methods for Vehicle Type, Make and Model Classification
    • [cs.CV]Vehicle Re-ID for Surround-view Camera System
    • [cs.CV]You Only Look Yourself: Unsupervised and Untrained Single Image Dehazing Neural Network
    • [cs.CY]A Systematic Review of the Digital Interventions for Fighting COVID-19: The Bangladesh Perspective
    • [cs.CY]A perspective on how to conduct responsible anti-human trafficking research in operations and analytics
    • [cs.CY]Data Science: Challenges and Directions
    • [cs.CY]Data Science: Nature and Pitfalls
    • [cs.CY]Digital Contact Tracing Service: An improved decentralized design for privacy and effectiveness
    • [cs.CY]From Simple Features to Moving Features and Beyond?
    • [cs.CY]Human Mobility during COVID-19 in the Context of Mild Social Distancing: Implications for Technological Interventions
    • [cs.CY]I call BS: Fraud Detection in Crowdfunding Campaigns
    • [cs.CY]Reading Between the Demographic Lines: Resolving Sources of Bias in Toxicity Classifiers
    • [cs.CY]The COVID-19 pandemic’s impact on U.S. electricity demand and supply: an early view from the data
    • [cs.DB]Lachesis: Automated Generation of Persistent Partitionings for Big Data Applications
    • [cs.DB]Leveraging Soft Functional Dependencies for Indexing Multi-dimensional Data
    • [cs.DC]Accelerating Binarized Neural Networks via Bit-Tensor-Cores in Turing GPUs
    • [cs.DC]Adaptive SpMV/SpMSpV on GPUs for Input Vectors of Varied Sparsity
    • [cs.DC]Extending the OpenCHK Model with Advanced Checkpoint Features
    • [cs.DC]Parallel Betweenness Computation in Graph Database for Contingency Selection
    • [cs.DC]Revisiting Asynchronous Fault Tolerant Computation with Optimal Resilience
    • [cs.DC]Transactions on Red-black and AVL trees in NVRAM
    • [cs.DM]Concave Aspects of Submodular Functions
    • [cs.ET]Towards analyzing large graphs with quantum annealing and quantum gate computers
    • [cs.FL]Binary intersection formalized
    • [cs.HC]Human Trust-based Feedback Control: Dynamically varying automation transparency to optimize human-machine interactions
    • [cs.IR]Fairness-aware News Recommendation with Decomposed Adversarial Learning
    • [cs.IR]Learning Post-Hoc Causal Explanations for Recommendation
    • [cs.IT]Deep reinforcement learning approach to MIMO precoding problem: Optimality and Robustness
    • [cs.IT]Deep-learning Autoencoder for Coherent and Nonlinear Optical Communication
    • [cs.IT]Delay Violation Probability and Effective Rate of Downlink NOMA over $α$-$μ$ Fading Channels
    • [cs.IT]Fundamental Limits of Cache-Aided Broadcast Networks with User Cooperation
    • [cs.IT]Multilinear Algebra for Minimum Storage Regenerating Codes
    • [cs.IT]On the Fundamental Limits of Coded Caching Systems with Restricted Demand Types
    • [cs.IT]Symbol-Level Precoding Made Practical for Multi-Level Modulations via Block-Level Rescaling
    • [cs.LG]AdaSGD: Bridging the gap between SGD and Adam
    • [cs.LG]Adai: Separating the Effects of Adaptive Learning Rate and Momentum Inertia
    • [cs.LG]Adversarial Learning for Debiasing Knowledge Graph Embeddings
    • [cs.LG]An EM Approach to Non-autoregressive Conditional Sequence Generation
    • [cs.LG]Associative Memory in Iterated Overparameterized Sigmoid Autoencoders
    • [cs.LG]Biologically Inspired Mechanisms for Adversarial Robustness
    • [cs.LG]Classification of cancer pathology reports: a large-scale comparative study
    • [cs.LG]Conditional GAN for timeseries generation
    • [cs.LG]Constructive Universal High-Dimensional Distribution Generation through Deep ReLU Networks
    • [cs.LG]Deriving Neural Network Design and Learning from the Probabilistic Framework of Chain Graphs
    • [cs.LG]Dynamic Knapsack Optimization Towards Efficient Multi-Channel Sequential Advertising
    • [cs.LG]Efficient Algorithms for Device Placement of DNN Graph Operators
    • [cs.LG]Efficient Continuous Pareto Exploration in Multi-Task Learning
    • [cs.LG]Efficient Nonmyopic Bayesian Optimization via One-Shot Multi-Step Trees
    • [cs.LG]Enabling Continual Learning with Differentiable Hebbian Plasticity
    • [cs.LG]Evaluating the Performance of Reinforcement Learning Algorithms
    • [cs.LG]Fast OSCAR and OWL Regression via Safe Screening Rules
    • [cs.LG]Forced-exploration free Strategies for Unimodal Bandits
    • [cs.LG]Graph Clustering with Graph Neural Networks
    • [cs.LG]Graph Neural Networks for Leveraging Industrial Equipment Structure: An application to Remaining Useful Life Estimation
    • [cs.LG]Guided Learning of Nonconvex Models through Successive Functional Gradient Optimization
    • [cs.LG]Handling Missing Data in Decision Trees: A Probabilistic Approach
    • [cs.LG]Hierarchical Qualitative Clustering — clustering mixed datasets with critical qualitative information
    • [cs.LG]Hypergraph Random Walks, Laplacians, and Clustering
    • [cs.LG]Improving Uncertainty Estimates through the Relationship with Adversarial Robustness
    • [cs.LG]Improving robustness against common corruptions by covariate shift adaptation
    • [cs.LG]Incremental Training of a Recurrent Neural Network Exploiting a Multi-Scale Dynamic Memory
    • [cs.LG]Involutive MCMC: a Unifying Framework
    • [cs.LG]Learning and Planning in Average-Reward Markov Decision Processes
    • [cs.LG]Learning to Combine Top-Down and Bottom-Up Signals in Recurrent Neural Networks with Attention over Modules
    • [cs.LG]Learning to Read through Machine Teaching
    • [cs.LG]Lipschitzness Is All You Need To Tame Off-policy Generative Adversarial Imitation Learning
    • [cs.LG]MDP Homomorphic Networks: Group Symmetries in Reinforcement Learning
    • [cs.LG]Maximum Entropy Models for Fast Adaptation
    • [cs.LG]Mining Documentation to Extract Hyperparameter Schemas
    • [cs.LG]Model-Targeted Poisoning Attacks: Provable Convergence and Certified Bounds
    • [cs.LG]Model-based Reinforcement Learning: A Survey
    • [cs.LG]Multi-Head Attention: Collaborate Instead of Concatenate
    • [cs.LG]Multi-Partition Embedding Interaction with Block Term Format for Knowledge Graph Completion
    • [cs.LG]Neural Datalog Through Time: Informed Temporal Modeling via Logical Specification
    • [cs.LG]On the Applicability of ML Fairness Notions
    • [cs.LG]Online Dynamic Network Embedding
    • [cs.LG]Optimal Rates of Distributed Regression with Imperfect Kernels
    • [cs.LG]Optimization Landscape of Tucker Decomposition
    • [cs.LG]Overview of Gaussian process based multi-fidelity techniques with variable relationship between fidelities
    • [cs.LG]Path Integral Based Convolution and Pooling for Graph Neural Networks
    • [cs.LG]Policy Gradient Optimization of Thompson Sampling Policies
    • [cs.LG]Provable Online CP/PARAFAC Decomposition of a Structured Tensor via Dictionary Learning
    • [cs.LG]R2-B2: Recursive Reasoning-Based Bayesian Optimization for No-Regret Learning in Games
    • [cs.LG]Random Partitioning Forest for Point-Wise and Collective Anomaly Detection — Application to Intrusion Detection
    • [cs.LG]SCE: Scalable Network Embedding from Sparsest Cut
    • [cs.LG]Sampling from a $k$-DPP without looking at all items
    • [cs.LG]Scaling Symbolic Methods using Gradients for Neural Model Explanation
    • [cs.LG]Sliced Kernelized Stein Discrepancy
    • [cs.LG]Theory-Inspired Path-Regularized Differential Network Architecture Search
    • [cs.LG]Tomographic Auto-Encoder: Unsupervised Bayesian Recovery of Corrupted Data
    • [cs.LG]Training highly effective connectivities within neural networks with randomly initialized, fixed weights
    • [cs.LG]Transformers are RNNs: Fast Autoregressive Transformers with Linear Attention
    • [cs.LG]Understanding Diversity based Pruning of Neural Networks — Statistical Mechanical Analysis
    • [cs.LG]Unsupervised Calibration under Covariate Shift
    • [cs.NE]A Compact Gated-Synapse Model for Neuromorphic Circuits
    • [cs.NE]A Framework for Learning Invariant Physical Relations in Multimodal Sensory Processing
    • [cs.NE]A Survey on Recent Progress in the Theory of Evolutionary Algorithms for Discrete Optimization
    • [cs.NE]Accelerating Reinforcement Learning Agent with EEG-based Implicit Human Feedback
    • [cs.NE]QPSO-CD: Quantum-behaved Particle Swarm Optimization Algorithm with Cauchy Distribution
    • [cs.NE]Spiking Associative Memory for Spatio-Temporal Patterns
    • [cs.NI]A Comparative Study of Network Traffic Representations for Novelty Detection
    • [cs.NI]Investigating the Effects of Mobility Metrics in Mobile Ad Hoc Networks
    • [cs.RO]Formalizing and Guaranteeing Human-Robot Interaction
    • [cs.RO]Multi-sensory Integration in a Quantum-Like Robot Perception Model
    • [cs.RO]Online Exploration and Coverage Planning in Unknown Obstacle-Cluttered Environments
    • [cs.RO]Predicting Sample Collision with Neural Networks
    • [cs.SE]SE3M: A Model for Software Effort Estimation Using Pre-trained Embedding Models
    • [cs.SI]Approximating Network Centrality Measures Using Node Embedding and Machine Learning
    • [cs.SI]Bucking the Trend: An Agentive Perspective of Managerial Influence on Blogs Attractiveness
    • [cs.SI]Link Prediction Using Supervised Machine Learning based on Aggregated and Topological Features
    • [cs.SI]Mixed Logit Models and Network Formation
    • [cs.SI]Mobile Link Prediction: Automated Creation and Crowd-sourced Validation of Knowledge Graphs
    • [cs.SI]Social Distancing 2.0 with Privacy-Preserving Contact Tracing to Avoid a Second Wave of COVID-19
    • [cs.SI]TweetsCOV19 — A Knowledge Base of Semantically Annotated Tweets about the COVID-19 Pandemic
    • [econ.EM]Inference in Bayesian Additive Vector Autoregressive Tree Models
    • [eess.IV]BitMix: Data Augmentation for Image Steganalysis
    • [eess.IV]Early Exit Or Not: Resource-Efficient Blind Quality Enhancement for Compresse
    1027
    d Images
    • [eess.IV]Fabric Image Representation Encoding Networks for Large-scale 3D Medical Image Analysis
    • [eess.IV]Hand-drawn Symbol Recognition of Surgical Flowsheet Graphs with Deep Image Segmentation
    • [eess.IV]Image-level Harmonization of Multi-Site Data using Image-and-Spatial Transformer Networks
    • [eess.IV]Large Deformation Diffeomorphic Image Registration with Laplacian Pyramid Networks
    • [eess.IV]Multi-level colonoscopy malignant tissue detection with adversarial CAC-UNet
    • [eess.IV]Needle tip force estimation by deep learning from raw spectral OCT data
    • [eess.IV]Ultra2Speech — A Deep Learning Framework for Formant Frequency Estimation and Tracking from Ultrasound Tongue Images
    • [eess.SP]A Novel Bistatic Joint Radar-Communication System in Multi-path Environments
    • [eess.SP]Beamspace Channel Estimation in Terahertz Communications: A Model-Driven Unsupervised Learning Approach
    • [eess.SP]Modeling and Uncertainty Analysis of Groundwater Level Using Six Evolutionary Optimization Algorithms Hybridized with ANFIS, SVM, and ANN
    • [eess.SP]Real Elliptically Skewed Distributions and Their Application to Robust Cluster Analysis
    • [eess.SP]User Selection in Millimeter Wave Massive MIMO System using Convolutional Neural Networks
    • [eess.SY]Estimation and Decomposition of Rack Force for Driving on Uneven Roads
    • [math.CO]Graph Laplacians, Riemannian Manifolds and their Machine-Learning
    • [math.NA]Bayesian Sparse learning with preconditioned stochastic gradient MCMC and its applications
    • [math.NT]Number Theory meets Wireless Communications: an introduction for dummies like us
    • [math.OC]Deep Learning Based Anticipatory Multi-Objective Eco-Routing Strategies for Connected and Automated Vehicles
    • [math.ST]Exponential inequalities for sampling designs
    • [math.ST]Filtering of stationary Gaussian statistical experiments
    • [math.ST]Partial Recovery for Top-$k$ Ranking: Optimality of MLE and Sub-Optimality of Spectral Method
    • [math.ST]Robust Kernel Density Estimation with Median-of-Means principle
    • [physics.comp-ph]GPU-Accelerated Discontinuous Galerkin Methods: 30x Speedup on 345 Billion Unknowns
    • [physics.optics]Terahertz Pulse Shaping Using Diffractive Legos
    • [physics.soc-ph]Distributed Link Removal Strategy for Networked Meta-Population Epidemics and its Application to the Control of the COVID-19 Pandemic
    • [physics.soc-ph]Non-backtracking Operator for Community Detection in Signed Networks
    • [physics.soc-ph]Statistical inference of assortative community structures
    • [q-bio.PE]On the derivation of the renewal equation from an age-dependent branching process: an epidemic modelling perspective
    • [q-bio.QM]Associations between finger tapping, gait and fall risk with application to fall risk assessment
    • [stat.AP]Do not forget interaction: Predicting fatality of COVID-19 patients using logistic regression
    • [stat.AP]From predictions to prescriptions: A data-driven response to COVID-19
    • [stat.AP]Individual-level Modeling of COVID-19 Epidemic Risk
    • [stat.AP]When and where: estimating the date and location of introduction for exotic pests and pathogens
    • [stat.CO]Hierarchical sparse Cholesky decomposition with applications to high-dimensional spatio-temporal filtering
    • [stat.CO]Scalable Bayesian Multiple Changepoint Detection via Auxiliary Uniformization
    • [stat.CO]Spatiotemporal Multi-Resolution Approximations for Analyzing Global Environmental Data
    • [stat.ME]A Robust Adaptive Modified Maximum Likelihood Estimator for the Linear Regression Model
    • [stat.ME]Autoregressive Mixture Models for Serial Correlation Clustering of Time Series Data
    • [stat.ME]Bayesian Analysis of Social Influence
    • [stat.ME]Data integration in high dimension with multiple quantiles
    • [stat.ME]Discussion of the paper “Optimal treatment allocations in space and time for on-line control of an emerging infectious disease’’ by E. B. Laber, N. J. Meyer, B. J. Reich, K. Pacifici, J. A. Collazo and J. Drake
    • [stat.ME]Exploring Consequences of Simulation Design for Apparent Performance of Statistical Methods. 1: Results from simulations with constant sample sizes
    • [stat.ME]G-computation and inverse probability weighting for time-to-event outcomes: a comparative study
    • [stat.ME]Penalized regression with multiple loss functions and selection by vote
    • [stat.ME]Testing and Support Recovery of Correlation Structures for Matrix-Valued Observations with an Application to Stock Market Data
    • [stat.ML]Black-box Certification and Learning under Adversarial Perturbations
    • [stat.ML]Conformal Prediction Intervals for Neural Networks Using Cross Validation
    • [stat.ML]Consistency of Anchor-based Spectral Clustering
    • [stat.ML]Counterfactual Predictions under Runtime Confounding
    • [stat.ML]Guarantees for Tuning the Step Size using a Learning-to-Learn Approach
    • [stat.ML]Recovering Joint Probability of Discrete Random Variables from Pairwise Marginals
    • [stat.ML]Recovery of Sparse Signals from a Mixture of Linear Samples
    • [stat.ML]Regression with reject option and application to kNN
    • [stat.ML]Sharp Statistical Guarantees for Adversarially Robust Gaussian Classification
    • [stat.ML]Sinkhorn EM: An Expectation-Maximization algorithm based on entropic optimal transport
    • [stat.ML]Sparse Gaussian Processes with Spherical Harmonic Features
    • [stat.ML]VAE-KRnet and its applications to variational Bayes
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    • [cs.AI]Bounded Rationality in Las Vegas: Probabilistic Finite Automata PlayMulti-Armed Bandits
    Xinming Liu, Joseph Y. Halpern
    http://arxiv.org/abs/2006.16950v1
    • [cs.AI]Building Rule Hierarchies for Efficient Logical Rule Learning from Knowledge Graphs
    Yulong Gu, Yu Guan, Paolo Missier
    http://arxiv.org/abs/2006.16171v2
    • [cs.AI]Mitigating Manipulation in Peer Review via Randomized Reviewer Assignments
    Steven Jecmen, Hanrui Zhang, Ryan Liu, Nihar B. Shah, Vincent Conitzer, Fei Fang
    http://arxiv.org/abs/2006.16437v1
    • [cs.AI]On Bellman’s Optimality Principle for zs-POSGs
    Olivier Buffet, Jilles Dibangoye, Aurélien Delage, Abdallah Saffidine, Vincent Thomas
    http://arxiv.org/abs/2006.16395v1
    • [cs.AI]On Finite Entailment of Non-Local Queries in Description Logics
    Tomasz Gogacz, Víctor Gutiérrez-Basulto, Albert Gutowski, Yazmín Ibáñez-García, Filip Murlak
    http://arxiv.org/abs/2006.16869v1
    • [cs.AI]Ontology-guided Semantic Composition for Zero-Shot Learning
    Jiaoyan Chen, Freddy Lecue, Yuxia Geng, Jeff Z. Pan, Huajun Chen
    http://arxiv.org/abs/2006.16917v1
    • [cs.CG]Subspace approximation with outliers
    Amit Deshpande, Rameshwar Pratap
    http://arxiv.org/abs/2006.16573v1
    • [cs.CL]A Data-driven Neural Network Architecture for Sentiment Analysis
    Erion Çano, Maurizio Morisio
    http://arxiv.org/abs/2006.16642v1
    • [cs.CL]ANA at SemEval-2020 Task 4: mUlti-task learNIng for cOmmonsense reasoNing (UNION)
    Anandh Perumal, Chenyang Huang, Amine Trabelsi, Osmar R. Zaïane
    http://arxiv.org/abs/2006.16403v1
    • [cs.CL]Correction of Faulty Background Knowledge based on Condition Aware and Revise Transformer for Question Answering
    Xinyan Zhao, Xiao Feng, Haoming Zhong, Jun Yao, Huanhuan Chen
    http://arxiv.org/abs/2006.16722v1
    • [cs.CL]GShard: Scaling Giant Models with Conditional Computation and Automatic Sharding
    Dmitry Lepikhin, HyoukJoong Lee, Yuanzhong Xu, Dehao Chen, Orhan Firat, Yanping Huang, Maxim Krikun, Noam Shazeer, Zhifeng Chen
    http://arxiv.org/abs/2006.16668v1
    • [cs.CL]Improving Sequence Tagging for Vietnamese Text Using Transformer-based Neural Models
    Viet Bui The, Oanh Tran Thi, Phuong Le-Hong
    http://arxiv.org/abs/2006.15994v2
    • [cs.CL]Learning Sparse Prototypes for Text Generation
    Junxian He, Taylor Berg-Kirkpatrick, Graham Neubig
    http://arxiv.org/abs/2006.16336v1
    • [cs.CL]PLATO-2: Towards Building an Open-Domain Chatbot via Curriculum Learning
    Siqi Bao, Huang He, Fan Wang, Hua Wu, Haifeng Wang, Wenquan Wu, Zhen Guo, Zhibin Liu, Xinchao Xu
    http://arxiv.org/abs/2006.16779v1
    • [cs.CL]Rethinking Positional Encoding in Language Pre-training
    Guolin Ke, Di He, Tie-Yan Liu
    http://arxiv.org/abs/2006.15595v2
    • [cs.CL]Technical Report: Auxiliary Tuning and its Application to Conditional Text Generation
    Yoel Zeldes, Dan Padnos, Or Sharir, Barak Peleg
    http://arxiv.org/abs/2006.16823v1
    • [cs.CL]Universal linguistic inductive biases via meta-learning
    R. Thomas McCoy, Erin Grant, Paul Smolensky, Thomas L. Griffiths, Tal Linzen
    http://arxiv.org/abs/2006.16324v1
    • [cs.CR]Adversarial Deep Ensemble: Evasion Attacks and Defenses for Malware Detection
    Deqiang Li, Qianmu Li
    http://arxiv.org/abs/2006.16545v1
    • [cs.CR]CyRes — Avoiding Catastrophic Failure in Connected and Autonomous Vehicles (Extended Abstract)
    Carsten Maple, Peter Davies, Kerstin Eder, Chris Hankin, Greg Chance, Gregory Epiphaniou
    http://arxiv.org/abs/2006.14890v2
    • [cs.CR]Quantifying Susceptibility to Spear Phishing in a High School Environment Using Signal Detection Theory
    Ploy Unchit, Sanchari Das, Andrew Kim, L. Jean Camp
    http://arxiv.org/abs/2006.16380v1
    • [cs.CR]Towards Robust LiDAR-based Perception in Autonomous Driving: General Black-box Adversarial Sensor Attack and Countermeasures
    Jiachen Sun, Yulong Cao, Qi Alfred Chen, Z. Morley Mao
    http://arxiv.org/abs/2006.16974v1
    • [cs.CV]A Simple Domain Shifting Networkfor Generating Low Quality Images
    Guruprasad Hegde, Avinash Nittur Ramesh, Kanchana Vaishnavi Gandikota, Roman Obermaisser, Michael Moeller
    http://arxiv.org/abs/2006.16621v1
    • [cs.CV]Actionable Attribution Maps for Scientific Machine Learning
    Shusen Liu, Bhavya Kailkhura, Jize Zhang, Anna M. Hiszpanski, Emily Robertson, Donald Loveland, T. Yong-Jin Han
    http://arxiv.org/abs/2006.16533v1
    • [cs.CV]Asymmetric metric learning for knowledge transfer
    Mateusz Budnik, Yannis Avrithis
    http://arxiv.org/abs/2006.16331v1
    • [cs.CV]Beyond accuracy: quantifying trial-by-trial behaviour of CNNs and humans by measuring error consistency
    Robert Geirhos, Kristof Meding, Felix A. Wichmann
    http://arxiv.org/abs/2006.16736v1
    • [cs.CV]Boosting Deep Neural Networks with Geometrical Prior Knowledge: A Survey
    Matthias Rath, Alexandru Paul Condurache
    http://arxiv.org/abs/2006.16867v1
    • [cs.CV]Can Your Face Detector Do Anti-spoofing? Face Presentation Attack Detection with a Multi-Channel Face Detector
    Anjith George, Sebastien Marcel
    http://arxiv.org/abs/2006.16836v1
    • [cs.CV]Classification Confidence Estimation with Test-Time Data-Augmentation
    Yuval Bahat, Gregory Shakhnarovich
    http://arxiv.org/abs/2006.16705v1
    • [cs.CV]Cross-Scale Internal Graph Neural Network for Image Super-Resolution
    Shangchen Zhou, Jiawei Zhang, Wangmeng Zuo, Chen Change Loy
    http://arxiv.org/abs/2006.16673v1
    • [cs.CV]Deep Isometric Learning for Visual Recognition
    Haozhi Qi, Chong You, Xiaolong Wang, Yi Ma, Jitendra Malik
    http://arxiv.org/abs/2006.16992v1
    • [cs.CV]ERNIE-ViL: Knowledge Enhanced Vision-Language Representations Through Scene Graph
    Fei Yu, Jiji Tang, Weichong Yin, Yu Sun, Hao Tian, Hua Wu, Haifeng Wang
    http://arxiv.org/abs/2006.16934v1
    • [cs.CV]EasyQuant: Post-training Quantization via Scale Optimization
    Di Wu, Qi Tang, Yongle Zhao, Ming Zhang, Ying Fu, Debing Zhang
    http://arxiv.org/abs/2006.16669v1
    • [cs.CV]FVV Live: Real-Time, Low-Cost, Free Viewpoint Video
    Daniel Berjón, Pablo Carballeira, Julián Cabrera, Carlos Carmona, Daniel Corregidor, César Díaz, Francisco Morán, Narciso García
    http://arxiv.org/abs/2006.16893v1
    • [cs.CV]GramGAN: Deep 3D Texture Synthesis From 2D Exemplars
    Tiziano Portenier, Siavash Bigdeli, Orcun Goksel
    http://arxiv.org/abs/2006.16112v2
    • [cs.CV]ITSELF: Iterative Saliency Estimation fLexible Framework
    Leonardo de Melo Joao, Felipe de Castro Belem, Alexandre Xavier Falcao
    http://arxiv.org/abs/2006.16956v1
    • [cs.CV]Large-scale inference of liver fat with neural networks on UK Biobank body MRI
    Taro Langner, Robin Strand, Håkan Ahlström, Joel Kullberg
    http://arxiv.org/abs/2006.16777v1
    • [cs.CV]Learning Patterns of Tourist Movement and Photography from Geotagged Photos at Archaeological Heritage Sites in Cuzco, Peru
    Nicole D. Payntar, Wei-Lin Hsiao, R. Alan Covey, Kristen Grauman
    http://arxiv.org/abs/2006.16424v1
    • [cs.CV]Leveraging Temporal Information for 3D Detection and Domain Adaptation
    Cunjun Yu, Zhongang Cai, Daxuan Ren, Haiyu Zhao
    http://arxiv.org/abs/2006.16796v1
    • [cs.CV]MSNet: A Multilevel Instance Segmentation Network for Natural Disaster Damage Assessment in Aerial Videos
    Xiaoyu Zhu, Junwei Liang, Alexander Hauptmann
    http://arxiv.org/abs/2006.16479v1
    • [cs.CV]Material Recognition for Automated Progress Monitoring using Deep Learning Methods
    Navid Ghassemi, Hadi Mahami, Mohammad Tayarani Darbandi, Afshin Shoeibi, Sadiq Hussain, Farnad Nasirzadeh, Roohallah Alizadehsani, Darius Nahavandi, Abbas Khosravi, Saeid Nahavandi
    http://arxiv.org/abs/2006.16344v1
    • [cs.CV]Method for the generation of depth images for view-based shape retrieval of 3D CAD model from partial point cloud
    Hyungki Kim, Moohyun Cha, Duhwan Mun
    http://arxiv.org/abs/2006.16500v1
    • [cs.CV]Object Detection under Rainy Conditions for Autonomous Vehicles
    Mazin Hnewa, Hayder Radha
    http://arxiv.org/abs/2006.16471v1
    • [cs.CV]OccInpFlow: Occlusion-Inpainting Optical Flow Estimation by Unsupervised Learning
    Kunming Luo, Chuan Wang, Nianjin Ye, Shuaicheng Liu, Jue Wang
    http://arxiv.org/abs/2006.16637v1
    • [cs.CV]On the Demystification of Knowledge Distillation: A Residual Network Perspective
    Nandan Kumar Jha, Rajat Saini, Sparsh Mittal
    http://arxiv.org/abs/2006.16589v1
    • [cs.CV]Primary Tumor Origin Classification of Lung Nodules in Spectral CT using Transfer Learning
    Linde S. Hesse, Pim A. de Jong, Josien P. W. Pluim, Veronika Cheplygina
    http://arxiv.org/abs/2006.16633v1
    • [cs.CV]PriorGAN: Real Data Prior for Generative Adversarial Nets
    Shuyang Gu, Jianmin Bao, Dong Chen, Fang Wen
    http://arxiv.org/abs/2006.16990v1
    • [cs.CV]Quantitative Evaluation of Endoscopic SLAM Methods: EndoSLAM Dataset
    Kutsev Bengisu Ozyoruk, Kagan Incetan, Gulfize Coskun, Guliz Irem Gokceler, Yasin Almalioglu, Faisal Mahmood, Nicholas J. Durr, Eva Curto, Luis Perdigoto, Marina Oliveira, Helder Araujo, Henrique Alexandrino, Hunter B. Gilbert, Mehmet Turan
    http://arxiv.org/abs/2006.16670v1
    • [cs.CV]Rethinking CNN-Based Pansharpening: Guided Colorization of Panchromatic Images via GANs
    Furkan Ozcelik, Ugur Alganci, Elif Sertel, Gozde Unal
    http://arxiv.org/abs/2006.16644v1
    • [cs.CV]SAR Image Despeckling by Deep Neural Networks: from a pre-trained model to an end-to-end training strategy
    Emanuele Dalsasso, Xiangli Yang, Loïc Denis, Florence Tupin, Wen Yang
    http://arxiv.org/abs/2006.15559v2
    • [cs.CV]Self-Supervised Learning of a Biologically-Inspired Visual Texture Model
    Nikhil Parthasarathy, Eero P. Simoncelli
    http://arxiv.org/abs/2006.16976v1
    • [cs.CV]Tackling Occlusion in Siamese Tracking with Structured Dropouts
    Deepak K. Gupta, Efstratios Gavves, Arnold W. M. Smeulders
    http://arxiv.org/abs/2006.16571v1
    • [cs.CV]Uncertainty-aware multi-view co-training for semi-supervised medical image segmentation and domain adaptation
    Yingda Xia, Dong Yang, Zhiding Yu, Fengze Liu, Jinzheng Cai, Lequan Yu, Zhuotun Zhu, Daguang Xu, Alan Yuille, Holger Roth
    http://arxiv.org/abs/2006.16806v1
    • [cs.CV]Vehicle Attribute Recognition by Appearance: Computer Vision Methods for Vehicle Type, Make and Model Classification
    Xingyang Ni, Heikki Huttunen
    http://arxiv.org/abs/2006.16400v1
    • [cs.CV]Vehicle Re-ID for Surround-view Camera System
    Zizhang Wu, Man Wang, Lingxiao Yin, Weiwei Sun, Jason Wang, Huangbin Wu
    http://arxiv.org/abs/2006.16503v1
    • [cs.CV]You Only Look Yourself: Unsupervised and Untrained Single Image Dehazing Neural Network
    Boyun Li, Yuanbiao Gou, Shuhang Gu, Jerry Zitao Liu, Joey Tianyi Zhou, Xi Peng
    http://arxiv.org/abs/2006.16829v1
    • [cs.CY]A Systematic Review of the Digital Interventions for Fighting COVID-19: The Bangladesh Perspective
    Muhammad Nazrul Islam, A. K. M. Najmul Islam
    http://arxiv.org/abs/2006.16882v1
    • [cs.CY]A perspective on how to conduct responsible anti-human trafficking research in operations and analytics
    Renata Konrad, Kayse Lee Maass, Andrew C. Trapp
    http://arxiv.org/abs/2006.16445v1
    • [cs.CY]Data Science: Challenges and Directions
    Longbing Cao
    http://arxiv.org/abs/2006.16966v1
    • [cs.CY]Data Science: Nature and Pitfalls
    Longbing Cao
    http://arxiv.org/abs/2006.16964v1
    • [cs.CY]Digital Contact Tracing Service: An improved decentralized design for privacy and effectiveness
    Kilian Holzapfel, Martina Karl, Linus Lotz, Georg Carle, Christian Djeffal, Christian Fruck, Christian Haack, Dirk Heckmann, Philipp H. Kindt, Michael Köppl, Patrick Krause, Lolian Shtembari, Lorenz Marx, Stephan Meighen-Berger, Birgit Neumair, Matthias Neumair, Julia Pollmann, Tina Pollmann, Elisa Resconi, Stefan Schönert, Andrea Turcati, Christoph Wiesinger, Giovanni Zattera, Christopher Allan, Esteban Barco, Kai Bitterschulte, Jörn Buchwald, Clara Fischer, Judith Gampe, Martin Häcker, Jasin Islami, Anatol Pomplun, Sebastian Preisner, Nele Quast, Christian Romberg, Christoph Steinlehner, Tjark Ziehm
    http://arxiv.org/abs/2006.16960v1
    • [cs.CY]From Simple Features to Moving Features and Beyond?
    Anita Graser, Esteban Zimányi, Krishna Chaitanya Bommakanti
    http://arxiv.org/abs/2006.16900v1
    • [cs.CY]Human Mobility during COVID-19 in the Context of Mild Social Distancing: Implications for Technological Interventions
    Myeong Lee, Seongkyu Lee, Seonghoon Kim, Noseong Park
    http://arxiv.org/abs/2006.16965v1
    • [cs.CY]I call BS: Fraud Detection in Crowdfunding Campaigns
    Beatrice Perez, Sara R. Machado, Jerone T. A. Andrews, Nicolas Kourtellis
    http://arxiv.org/abs/2006.16849v1
    • [cs.CY]Reading Between the Demographic Lines: Resolving Sources of Bias in Toxicity Classifiers
    Elizabeth Reichert, Helen Qiu, Jasmine Bayrooti
    http://arxiv.org/abs/2006.16402v1
    • [cs.CY]The COVID-19 pandemic’s impact on U.S. electricity demand and supply: an early view from the data
    Duzgun Agdas, Prabir Barooah
    http://arxiv.org/abs/2006.16504v1
    • [cs.DB]Lachesis: Automated Generation of Persistent Partitionings for Big Data Applications
    Jia Zou, Pratik Barhate, Amitabh Das, Arun Iyengar, Binhang Yuan, Dimitrije Jankov, Chis Jermaine
    http://arxiv.org/abs/2006.16529v1
    • [cs.DB]Leveraging Soft Functional Dependencies for Indexing Multi-dimensional Data
    Behzad Ghaffari, Ali Hadian, Thomas Heinis
    http://arxiv.org/abs/2006.16393v1
    • [cs.DC]Accelerating Binarized Neural Networks via Bit-Tensor-Cores in Turing GPUs
    Ang Li, Simon Su
    http://arxiv.org/abs/2006.16578v1
    • [cs.DC]Adaptive SpMV/SpMSpV on GPUs for Input Vectors of Varied Sparsity
    Min Li, Yulong Ao, Chao Yang
    http://arxiv.org/abs/2006.16767v1
    • [cs.DC]Extending the OpenCHK Model with Advanced Checkpoint Features
    Marcos Maroñas, Sergi Mateo, Kai Keller, Leonardo Bautista-Gomez, Eduard Ayguadé, Vicenç Beltran
    http://arxiv.org/abs/2006.16616v1
    • [cs.DC]Parallel Betweenness Computation in Graph Database for Contingency Selection
    Yongli Zhu, Renchang Dai, Guangyi Liu
    http://arxiv.org/abs/2006.16339v1
    • [cs.DC]Revisiting Asynchronous Fault Tolerant Computation with Optimal Resilience
    Ittai Abraham, Danny Dolev, Gilad Stern
    http://arxiv.org/abs/2006.16686v1
    • [cs.DC]Transactions on Red-black and AVL trees in NVRAM
    Thorsten Schütt, Florian Schintke, Jan Skrzypczak
    http://arxiv.org/abs/2006.16284v1
    • [cs.DM]Concave Aspects of Submodular Functions
    Rishabh Iyer, Jeff Bilmes
    http://arxiv.org/abs/2006.16784v1
    • [cs.ET]Towards analyzing large graphs with quantum annealing and quantum gate computers
    Hannu Reittu, Ville Kotovirta, Lasse Leskelä, Hannu Rummukainen, Tomi Räty
    http://arxiv.org/abs/2006.16702v1
    • [cs.FL]Binary intersection formalized
    Štěpán Holub, Štěpán Starosta
    http://arxiv.org/abs/2006.16711v1
    • [cs.HC]Human Trust-based Feedback Control: Dynamically varying automation transparency to optimize human-machine interactions
    Kumar Akash, Griffon McMahon, Tahira Reid, Neera Jain
    http://arxiv.org/abs/2006.16353v1
    • [cs.IR]Fairness-aware News Recommendation with Decomposed Adversarial Learning
    Chuhan Wu, Fangzhao Wu, Xiting Wang, Yongfeng Huang, Xing Xie
    http://arxiv.org/abs/2006.16742v1
    • [cs.IR]Learning Post-Hoc Causal Explanations for Recommendation
    Shuyuan Xu, Yunqi Li, Shuchang Liu, Zuohui Fu, Yongfeng Zhang
    http://arxiv.org/abs/2006.16977v1
    • [cs.IT]Deep reinforcement learning approach to MIMO precoding problem: Optimality and Robustness
    Heunchul Lee, Maksym Girnyk, Jaeseong Jeong
    http://arxiv.org/abs/2006.16646v1
    • [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/2006.15027v2
    • [cs.IT]Delay Violation Probability and Effective Rate of Downlink NOMA over $α$-$μ$ Fading Channels
    Vaibhav Kumar, Barry Cardiff, Shankar Prakriya, Mark F. Flanagan
    http://arxiv.org/abs/2006.16505v1
    • [cs.IT]Fundamental Limits of Cache-Aided Broadcast Networks with User Cooperation
    Jiahui Chen, Xiaowen You, Youlong Wu, Haoyu Yin
    http://arxiv.org/abs/2006.16818v1
    • [cs.IT]Multilinear Algebra for Minimum Storage Regenerating Codes
    Iwan Duursma, Hsin-Po Wang
    http://arxiv.org/abs/2006.16998v1
    • [cs.IT]On the Fundamental Limits of Coded Caching Systems with Restricted Demand Types
    Shuo Shao, Jesús Gómez-Vilardebó, Kai Zhang, Chao Tian
    http://arxiv.org/abs/2006.16557v1
    • [cs.IT]Symbol-Level Precoding Made Practical for Multi-Level Modulations via Block-Level Rescaling
    Ang Li, Fan Liu, Christos Masouros
    http://arxiv.org/abs/2006.15245v1
    • [cs.LG]AdaSGD: Bridging the gap between SGD and Adam
    Jiaxuan Wang, Jenna Wiens
    http://arxiv.org/abs/2006.16541v1
    • [cs.LG]Adai: Separating the Effects of Adaptive Learning Rate and Momentum Inertia
    Zeke Xie, Xinrui Wang, Huishuai Zhang, Issei Sato, Masashi Sugiyama
    http://arxiv.org/abs/2006.15815v2
    • [cs.LG]Adversarial Learning for Debiasing Knowledge Graph Embeddings
    Mario Arduini, Lorenzo Noci, Federico Pirovano, Ce Zhang, Yash Raj Shrestha, Bibek Paudel
    http://arxiv.org/abs/2006.16309v1
    • [cs.LG]An EM Approach to Non-autoregressive Conditional Sequence Generation
    Zhiqing Sun, Yiming Yang
    http://arxiv.org/abs/2006.16378v1
    • [cs.LG]Associative Memory in Iterated Overparameterized Sigmoid Autoencoders
    Yibo Jiang, Cengiz Pehlevan
    http://arxiv.org/abs/2006.16540v1
    • [cs.LG]Biologically Inspired Mechanisms for Adversarial Robustness
    Manish V. Reddy, Andrzej Banburski, Nishka Pant, Tomaso Poggio
    http://arxiv.org/abs/2006.16427v1
    • [cs.LG]Classification of cancer pathology reports: a large-scale comparative study
    Stefano Martina, Leonardo Ventura, Paolo Frasconi
    http://arxiv.org/abs/2006.16370v1
    • [cs.LG]Conditional GAN for timeseries generation
    Kaleb E Smith, Anthony O Smith
    http://arxiv.org/abs/2006.16477v1
    • [cs.LG]Constructive Universal High-Dimensional Distribution Generation through Deep ReLU Networks
    Dmytro Perekrestenko, Stephan Müller, Helmut Bölcskei
    http://arxiv.org/abs/2006.16664v1
    • [cs.LG]Deriving Neural Network Design and Learning from the Probabilistic Framework of Chain Graphs
    Yuesong Shen, Daniel Cremers
    http://arxiv.org/abs/2006.16856v1
    • [cs.LG]Dynamic Knapsack Optimization Towards Efficient Multi-Channel Sequential Advertising
    Xiaotian Hao, Zhaoqing Peng, Yi Ma, Guan Wang, Junqi Jin, Jianye Hao, Shan Chen, Rongquan Bai, Mingzhou Xie, Miao Xu, Zhenzhe Zheng, Chuan Yu, Han Li, Jian Xu, Kun Gai
    http://arxiv.org/abs/2006.16312v1
    • [cs.LG]Efficient Algorithms for Device Placement of DNN Graph Operators
    Jakub Tarnawski, Amar Phanishayee, Nikhil R. Devanur, Divya Mahajan, Fanny Nina Paravecino
    http://arxiv.org/abs/2006.16423v1
    • [cs.LG]Efficient Continuous Pareto Exploration in Multi-Task Learning
    Pingchuan Ma, Tao Du, Wojciech Matusik
    http://arxiv.org/abs/2006.16434v1
    • [cs.LG]Efficient Nonmyopic Bayesian Optimization via One-Shot Multi-Step Trees
    Shali Jiang, Daniel R. Jiang, Maximilian Balandat, Brian Karrer, Jacob R. Gardner, Roman Garnett
    http://arxiv.org/abs/20
    8ed
    06.15779v1
    8ed
    06.15779v1)
    • [cs.LG]Enabling Continual Learning with Differentiable Hebbian Plasticity
    Vithursan Thangarasa, Thomas Miconi, Graham W. Taylor
    http://arxiv.org/abs/2006.16558v1
    • [cs.LG]Evaluating the Performance of Reinforcement Learning Algorithms
    Scott M. Jordan, Yash Chandak, Daniel Cohen, Mengxue Zhang, Philip S. Thomas
    http://arxiv.org/abs/2006.16958v1
    • [cs.LG]Fast OSCAR and OWL Regression via Safe Screening Rules
    Runxue Bao, Bin Gu, Heng Huang
    http://arxiv.org/abs/2006.16433v1
    • [cs.LG]Forced-exploration free Strategies for Unimodal Bandits
    Hassan Saber, Pierre Ménard, Odalric-Ambrym Maillard
    http://arxiv.org/abs/2006.16569v1
    • [cs.LG]Graph Clustering with Graph Neural Networks
    Anton Tsitsulin, John Palowitch, Bryan Perozzi, Emmanuel Müller
    http://arxiv.org/abs/2006.16904v1
    • [cs.LG]Graph Neural Networks for Leveraging Industrial Equipment Structure: An application to Remaining Useful Life Estimation
    Jyoti Narwariya, Pankaj Malhotra, Vishnu TV, Lovekesh Vig, Gautam Shroff
    http://arxiv.org/abs/2006.16556v1
    • [cs.LG]Guided Learning of Nonconvex Models through Successive Functional Gradient Optimization
    Rie Johnson, Tong Zhang
    http://arxiv.org/abs/2006.16840v1
    • [cs.LG]Handling Missing Data in Decision Trees: A Probabilistic Approach
    Pasha Khosravi, Antonio Vergari, YooJung Choi, Yitao Liang, Guy Van den Broeck
    http://arxiv.org/abs/2006.16341v1
    • [cs.LG]Hierarchical Qualitative Clustering — clustering mixed datasets with critical qualitative information
    Diogo Seca, João Mendes-Moreira, Tiago Mendes-Neves, Ricardo Sousa
    http://arxiv.org/abs/2006.16701v1
    • [cs.LG]Hypergraph Random Walks, Laplacians, and Clustering
    Koby Hayashi, Sinan G. Aksoy, Cheong Hee Park, Haesun Park
    http://arxiv.org/abs/2006.16377v1
    • [cs.LG]Improving Uncertainty Estimates through the Relationship with Adversarial Robustness
    Yao Qin, Xuezhi Wang, Alex Beutel, Ed H. Chi
    http://arxiv.org/abs/2006.16375v1
    • [cs.LG]Improving robustness against common corruptions by covariate shift adaptation
    Steffen Schneider, Evgenia Rusak, Luisa Eck, Oliver Bringmann, Wieland Brendel, Matthias Bethge
    http://arxiv.org/abs/2006.16971v1
    • [cs.LG]Incremental Training of a Recurrent Neural Network Exploiting a Multi-Scale Dynamic Memory
    Antonio Carta, Alessandro Sperduti, Davide Bacciu
    http://arxiv.org/abs/2006.16800v1
    • [cs.LG]Involutive MCMC: a Unifying Framework
    Kirill Neklyudov, Max Welling, Evgenii Egorov, Dmitry Vetrov
    http://arxiv.org/abs/2006.16653v1
    • [cs.LG]Learning and Planning in Average-Reward Markov Decision Processes
    Yi Wan, Abhishek Naik, Richard S. Sutton
    http://arxiv.org/abs/2006.16318v1
    • [cs.LG]Learning to Combine Top-Down and Bottom-Up Signals in Recurrent Neural Networks with Attention over Modules
    Sarthak Mittal, Alex Lamb, Anirudh Goyal, Vikram Voleti, Murray Shanahan, Guillaume Lajoie, Michael Mozer, Yoshua Bengio
    http://arxiv.org/abs/2006.16981v1
    • [cs.LG]Learning to Read through Machine Teaching
    Ayon Sen, Christopher R. Cox, Matthew Cooper Borkenhagen, Mark S. Seidenberg, Xiaojin Zhu
    http://arxiv.org/abs/2006.16470v1
    • [cs.LG]Lipschitzness Is All You Need To Tame Off-policy Generative Adversarial Imitation Learning
    Lionel Blondé, Pablo Strasser, Alexandros Kalousis
    http://arxiv.org/abs/2006.16785v1
    • [cs.LG]MDP Homomorphic Networks: Group Symmetries in Reinforcement Learning
    Elise van der Pol, Daniel E. Worrall, Herke van Hoof, Frans A. Oliehoek, Max Welling
    http://arxiv.org/abs/2006.16908v1
    • [cs.LG]Maximum Entropy Models for Fast Adaptation
    Samarth Sinha, Anirudh Goyal, Animesh Garg
    http://arxiv.org/abs/2006.16524v1
    • [cs.LG]Mining Documentation to Extract Hyperparameter Schemas
    Guillaume Baudart, Peter D. Kirchner, Martin Hirzel, Kiran Kate
    http://arxiv.org/abs/2006.16984v1
    • [cs.LG]Model-Targeted Poisoning Attacks: Provable Convergence and Certified Bounds
    Fnu Suya, Saeed Mahloujifar, David Evans, Yuan Tian
    http://arxiv.org/abs/2006.16469v1
    • [cs.LG]Model-based Reinforcement Learning: A Survey
    Thomas M. Moerland, Joost Broekens, Catholijn M. Jonker
    http://arxiv.org/abs/2006.16712v1
    • [cs.LG]Multi-Head Attention: Collaborate Instead of Concatenate
    Jean-Baptiste Cordonnier, Andreas Loukas, Martin Jaggi
    http://arxiv.org/abs/2006.16362v1
    • [cs.LG]Multi-Partition Embedding Interaction with Block Term Format for Knowledge Graph Completion
    Hung Nghiep Tran, Atsuhiro Takasu
    http://arxiv.org/abs/2006.16365v1
    • [cs.LG]Neural Datalog Through Time: Informed Temporal Modeling via Logical Specification
    Hongyuan Mei, Guanghui Qin, Minjie Xu, Jason Eisner
    http://arxiv.org/abs/2006.16723v1
    • [cs.LG]On the Applicability of ML Fairness Notions
    Karima Makhlouf, Sami Zhioua, Catuscia Palamidessi
    http://arxiv.org/abs/2006.16745v1
    • [cs.LG]Online Dynamic Network Embedding
    Haiwei Huang, Jinlong Li, Huimin He, Huanhuan Chen
    http://arxiv.org/abs/2006.16478v1
    • [cs.LG]Optimal Rates of Distributed Regression with Imperfect Kernels
    Hongwei Sun, Qiang Wu
    http://arxiv.org/abs/2006.16744v1
    • [cs.LG]Optimization Landscape of Tucker Decomposition
    Abraham Frandsen, Rong Ge
    http://arxiv.org/abs/2006.16297v1
    • [cs.LG]Overview of Gaussian process based multi-fidelity techniques with variable relationship between fidelities
    Loïc Brevault, Mathieu Balesdent, Ali Hebbal
    http://arxiv.org/abs/2006.16728v1
    • [cs.LG]Path Integral Based Convolution and Pooling for Graph Neural Networks
    Zheng Ma, Junyu Xuan, Yu Guang Wang, Ming Li, Pietro Lio
    http://arxiv.org/abs/2006.16811v1
    • [cs.LG]Policy Gradient Optimization of Thompson Sampling Policies
    Seungki Min, Ciamac C. Moallemi, Daniel J. Russo
    http://arxiv.org/abs/2006.16507v1
    • [cs.LG]Provable Online CP/PARAFAC Decomposition of a Structured Tensor via Dictionary Learning
    Sirisha Rambhatla, Xingguo Li, Jarvis Haupt
    http://arxiv.org/abs/2006.16442v1
    • [cs.LG]R2-B2: Recursive Reasoning-Based Bayesian Optimization for No-Regret Learning in Games
    Zhongxiang Dai, Yizhou Chen, Kian Hsiang Low, Patrick Jaillet, Teck-Hua Ho
    http://arxiv.org/abs/2006.16679v1
    • [cs.LG]Random Partitioning Forest for Point-Wise and Collective Anomaly Detection — Application to Intrusion Detection
    Pierre-Francois Marteau
    http://arxiv.org/abs/2006.16801v1
    • [cs.LG]SCE: Scalable Network Embedding from Sparsest Cut
    Shengzhong Zhang, Zengfeng Huang, Haicang Zhou, Ziang Zhou
    http://arxiv.org/abs/2006.16499v1
    • [cs.LG]Sampling from a $k$-DPP without looking at all items
    Daniele Calandriello, Michał Dereziński, Michal Valko
    http://arxiv.org/abs/2006.16947v1
    • [cs.LG]Scaling Symbolic Methods using Gradients for Neural Model Explanation
    Subham Sekhar Sahoo, Subhashini Venugopalan, Li Li, Rishabh Singh, Patrick Riley
    http://arxiv.org/abs/2006.16322v1
    • [cs.LG]Sliced Kernelized Stein Discrepancy
    Wenbo Gong, Yingzhen Li, José Miguel Hernández-Lobato
    http://arxiv.org/abs/2006.16531v1
    • [cs.LG]Theory-Inspired Path-Regularized Differential Network Architecture Search
    Pan Zhou, Caiming Xiong, Richard Socher, Steven C. H. Hoi
    http://arxiv.org/abs/2006.16537v1
    • [cs.LG]Tomographic Auto-Encoder: Unsupervised Bayesian Recovery of Corrupted Data
    Francesco Tonolini, Pablo G. Moreno, Andreas Damianou, Roderick Murray-Smith
    http://arxiv.org/abs/2006.16938v1
    • [cs.LG]Training highly effective connectivities within neural networks with randomly initialized, fixed weights
    Cristian Ivan, Razvan Florian
    http://arxiv.org/abs/2006.16627v1
    • [cs.LG]Transformers are RNNs: Fast Autoregressive Transformers with Linear Attention
    Angelos Katharopoulos, Apoorv Vyas, Nikolaos Pappas, François Fleuret
    http://arxiv.org/abs/2006.16236v2
    • [cs.LG]Understanding Diversity based Pruning of Neural Networks — Statistical Mechanical Analysis
    Rupam Acharyya, Boyu Zhang, Ankani Chattoraj, Shouman Das, Daniel Stefankovic
    http://arxiv.org/abs/2006.16617v1
    • [cs.LG]Unsupervised Calibration under Covariate Shift
    Anusri Pampari, Stefano Ermon
    http://arxiv.org/abs/2006.16405v1
    • [cs.NE]A Compact Gated-Synapse Model for Neuromorphic Circuits
    Alexander Jones, Rashmi Jha
    http://arxiv.org/abs/2006.16302v1
    • [cs.NE]A Framework for Learning Invariant Physical Relations in Multimodal Sensory Processing
    Du Xiaorui, Yavuzhan Erdem, Immanuel Schweizer, Cristian Axenie
    http://arxiv.org/abs/2006.16607v1
    • [cs.NE]A Survey on Recent Progress in the Theory of Evolutionary Algorithms for Discrete Optimization
    Benjamin Doerr, Frank Neumann
    http://arxiv.org/abs/2006.16709v1
    • [cs.NE]Accelerating Reinforcement Learning Agent with EEG-based Implicit Human Feedback
    Duo Xu, Mohit Agarwal, Faramarz Fekri, Raghupathy Sivakumar
    http://arxiv.org/abs/2006.16498v1
    • [cs.NE]QPSO-CD: Quantum-behaved Particle Swarm Optimization Algorithm with Cauchy Distribution
    Amandeep Singh Bhatia, Mandeep Kaur Saggi, Shenggen Zheng, Soumya Ranjan Nayak
    http://arxiv.org/abs/2006.16989v1
    • [cs.NE]Spiking Associative Memory for Spatio-Temporal Patterns
    Simon Davidson, Stephen B. Furber, Oliver Rhodes
    http://arxiv.org/abs/2006.16684v1
    • [cs.NI]A Comparative Study of Network Traffic Representations for Novelty Detection
    Kun Yang, Samory Kpotufe, Nick Feamster
    http://arxiv.org/abs/2006.16993v1
    • [cs.NI]Investigating the Effects of Mobility Metrics in Mobile Ad Hoc Networks
    Mohsin Ur Rahman
    http://arxiv.org/abs/2006.16441v1
    • [cs.RO]Formalizing and Guaranteeing Human-Robot Interaction
    Hadas Kress-Gazit, Kerstin Eder, Guy Hoffman, Henny Admoni, Brenna Argall, Ruediger Ehlers, Christoffer Heckman, Nils Jansen, Ross Knepper, Jan Křetínský, Shelly Levy-Tzedek, Jamy Li, Todd Murphey, Laurel Riek, Dorsa Sadigh
    http://arxiv.org/abs/2006.16732v1
    • [cs.RO]    Multi-sensory Integration in a Quantum-Like Robot Perception Model
    Davide Lanza, Paolo Solinas, Fulvio Mastrogiovanni
    http://arxiv.org/abs/2006.16404v1
    • [cs.RO]    Online Exploration and Coverage Planning in Unknown Obstacle-Cluttered Environments
    Xinyue Kan, Hanzhe Teng, Konstantinos Karydis
    http://arxiv.org/abs/2006.16460v1
    • [cs.RO]    Predicting Sample Collision with Neural Networks
    Tuan Tran, Jory Denny, Chinwe Ekenna
    http://arxiv.org/abs/2006.16868v1
    • [cs.SE]    SE3M: A Model for Software Effort Estimation Using Pre-trained Embedding Models
    Eliane M. De Bortoli Fávero, Dalcimar Casanova, Andrey Ricardo Pimentel
    http://arxiv.org/abs/2006.16831v1
    • [cs.SI]    Approximating Network Centrality Measures Using Node Embedding and Machine Learning
    Matheus R. F. Mendonça, André M. S. Barreto, Artur Ziviani
    http://arxiv.org/abs/2006.16392v1
    • [cs.SI]    Bucking the Trend: An Agentive Perspective of Managerial Influence on Blogs Attractiveness
    Carlos Denner dos Santos, Isadora Castro, George Kuk, Silvia Onoyama, Marina Moreira
    http://arxiv.org/abs/2006.16944v1
    • [cs.SI]    Link Prediction Using Supervised Machine Learning based on Aggregated and Topological Features
    Mohammad G. Raeini
    http://arxiv.org/abs/2006.16327v1
    • [cs.SI]    Mixed Logit Models and Network Formation
    Harsh Gupta, Mason A. Porter
    http://arxiv.org/abs/2006.16516v1
    • [cs.SI]    Mobile Link Prediction: Automated Creation and Crowd-sourced Validation of Knowledge Graphs
    Mark Christopher Ballandies, Evangelos Pournaras
    http://arxiv.org/abs/2006.16858v1
    • [cs.SI]    Social Distancing 2.0 with Privacy-Preserving Contact Tracing to Avoid a Second Wave of COVID-19
    Yu-Chen Ho, Yi-Hsuan Chen, Shen-Hua Hung, Chien-Hao Huang, Poga Po, Chung-Hsi Chan, Di-Kai Yang, Yi-Chin Tu, Tyng-Luh Liu, Chi-Tai Fang
    http://arxiv.org/abs/2006.16611v1
    • [cs.SI]    TweetsCOV19 — A Knowledge Base of Semantically Annotated Tweets about the COVID-19 Pandemic
    Dimitar Dimitrov, Erdal Baran, Pavlos Fafalios, Ran Yu, Xiaofei Zhu, Matthäus Zloch, Stefan Dietze
    http://arxiv.org/abs/2006.14492v2
    • [econ.EM]    Inference in Bayesian Additive Vector Autoregressive Tree Models
    Florian Huber, Luca Rossini
    http://arxiv.org/abs/2006.16333v1
    • [eess.IV]    BitMix: Data Augmentation for Image Steganalysis
    In-Jae Yu, Wonhyuk Ahn, Seung-Hun Nam, Heung-Kyu Lee
    http://arxiv.org/abs/2006.16625v1
    • [eess.IV]    Early Exit Or Not: Resource-Efficient Blind Quality Enhancement for Compresse
    1027
    d Images
    Qunliang Xing, Mai Xu, Tianyi Li, Zhenyu Guan
    http://arxiv.org/abs/2006.16581v1
    • [eess.IV]    Fabric Image Representation Encoding Networks for Large-scale 3D Medical Image Analysis
    Siyu Liu, Wei Dai, Craig Engstrom, Jurgen Fripp, Peter B. Greer, Stuart Crozier, Jason A. Dowling, Shekhar S. Chandra
    http://arxiv.org/abs/2006.15578v2
    • [eess.IV]    Hand-drawn Symbol Recognition of Surgical Flowsheet Graphs with Deep Image Segmentation
    William Adorno III, Angela Yi, Marcel Durieux, Donald Brown
    http://arxiv.org/abs/2006.16546v1
    • [eess.IV]    Image-level Harmonization of Multi-Site Data using Image-and-Spatial Transformer Networks
    R. Robinson, Q. Dou, D. C. Castro, K. Kamnitsas, M. de Groot, R. M. Summers, D. Rueckert, B. Glocker
    http://arxiv.org/abs/2006.16741v1
    • [eess.IV]    Large Deformation Diffeomorphic Image Registration with Laplacian Pyramid Networks
    Tony C. W. Mok, Albert C. S. Chung
    http://arxiv.org/abs/2006.16148v2
    • [eess.IV]    Multi-level colonoscopy malignant tissue detection with adversarial CAC-UNet
    Chuang Zhu, Ke Mei, Ting Peng, Yihao Luo, Jun Liu, Ying Wang, Mulan Jin
    http://arxiv.org/abs/2006.15954v2
    • [eess.IV]    Needle tip force estimation by deep learning from raw spectral OCT data
    M. Gromniak, N. Gessert, T. Saathoff, A. Schlaefer
    http://arxiv.org/abs/2006.16675v1
    • [eess.IV]    Ultra2Speech — A Deep Learning Framework for Formant Frequency Estimation and Tracking from Ultrasound Tongue Images
    Pramit Saha, Yadong Liu, Bryan Gick, Sidney Fels
    http://arxiv.org/abs/2006.16367v1
    • [eess.SP]    A Novel Bistatic Joint Radar-Communication System in Multi-path Environments
    Yuan Quan, Longfei Shi, Jialei Liu, Jiazhi Ma
    http://arxiv.org/abs/2006.16591v1
    • [eess.SP]    Beamspace Channel Estimation in Terahertz Communications: A Model-Driven Unsupervised Learning Approach
    Hengtao He, Rui Wang, Shi Jin, Chao-Kai Wen, Geoffrey Ye Li
    http://arxiv.org/abs/2006.16628v1
    • [eess.SP]    Modeling and Uncertainty Analysis of Groundwater Level Using Six Evolutionary Optimization Algorithms Hybridized with ANFIS, SVM, and ANN
    Akram Seifi, Mohammad Ehteram, Vijay P. Singh, Amir Mosavi
    http://arxiv.org/abs/2006.16848v1
    • [eess.SP]    Real Elliptically Skewed Distributions and Their Application to Robust Cluster Analysis
    Christian A. Schroth, Michael Muma
    http://arxiv.org/abs/2006.16671v1
    • [eess.SP]    User Selection in Millimeter Wave Massive MIMO System using Convolutional Neural Networks
    Salman Khalid, Waqas bin Abbas, Farhan Khalid, Michele Zorzi
    http://arxiv.org/abs/2006.16854v1
    • [eess.SY]    Estimation and Decomposition of Rack Force for Driving on Uneven Roads
    Akshay Bhardwaj, Daniel Slavin, John Walsh, James Freudenberg, R. Brent Gillespie
    http://arxiv.org/abs/2006.16319v1
    • [math.CO]    Graph Laplacians, Riemannian Manifolds and their Machine-Learning
    Yang-Hui He, Shing-Tung Yau
    http://arxiv.org/abs/2006.16619v1
    • [math.NA]    Bayesian Sparse learning with preconditioned stochastic gradient MCMC and its applications
    Yating Wang, Wei Deng, Lin Guang
    http://arxiv.org/abs/2006.16376v1
    • [math.NT]    Number Theory meets Wireless Communications: an introduction for dummies like us
    Victor Beresnevich, Sanju Velani
    http://arxiv.org/abs/2006.16358v1
    • [math.OC]    Deep Learning Based Anticipatory Multi-Objective Eco-Routing Strategies for Connected and Automated Vehicles
    Lama Alfaseeh, Bilal Farooq
    http://arxiv.org/abs/2006.16472v1
    • [math.ST]    Exponential inequalities for sampling designs
    Guillaume Chauvet, Mathieu Gerber
    http://arxiv.org/abs/2006.16600v1
    • [math.ST]    Filtering of stationary Gaussian statistical experiments
    V. S. Koroliuk, D. Koroliouk
    http://arxiv.org/abs/2006.16244v1
    • [math.ST]    Partial Recovery for Top-$k$ Ranking: Optimality of MLE and Sub-Optimality of Spectral Method
    Pinhan Chen, Chao Gao, Anderson Y. Zhang
    http://arxiv.org/abs/2006.16485v1
    • [math.ST]    Robust Kernel Density Estimation with Median-of-Means principle
    Pierre Humbert, Batiste Le Bars, Ludovic Minvielle, Nicolas Vayatis
    http://arxiv.org/abs/2006.16590v1
    • [physics.comp-ph]    GPU-Accelerated Discontinuous Galerkin Methods: 30x Speedup on 345 Billion Unknowns
    Andrew C. Kirby, Dimitri J. Mavriplis
    http://arxiv.org/abs/2006.15698v2
    • [physics.optics]    Terahertz Pulse Shaping Using Diffractive Legos
    Muhammed Veli, Deniz Mengu, Nezih T. Yardimci, Yi Luo, Jingxi Li, Yair Rivenson, Mona Jarrahi, Aydogan Ozcan
    http://arxiv.org/abs/2006.16599v1
    • [physics.soc-ph]    Distributed Link Removal Strategy for Networked Meta-Population Epidemics and its Application to the Control of the COVID-19 Pandemic
    Fangzhou Liu, Yuhong Chen, Tong Liu, Zibo Zhou, Dong Xue, Martin Buss
    http://arxiv.org/abs/2006.16221v1
    • [physics.soc-ph]    Non-backtracking Operator for Community Detection in Signed Networks
    Zhaoyue Zhong, Xiangrong Wang, Cunquan Qu, Guanghui Wang
    http://arxiv.org/abs/2006.15471v1
    • [physics.soc-ph]    Statistical inference of assortative community structures
    Lizhi Zhang, Tiago P. Peixoto
    http://arxiv.org/abs/2006.14493v3
    • [q-bio.PE]    On the derivation of the renewal equation from an age-dependent branching process: an epidemic modelling perspective
    Swapnil Mishra, Tresnia Berah, Thomas A. Mellan, H. Juliette T. Unwin, Michaela A Vollmer, Kris V Parag, Axel Gandy, Seth Flaxman, Samir Bhatt
    http://arxiv.org/abs/2006.16487v1
    • [q-bio.QM]    Associations between finger tapping, gait and fall risk with application to fall risk assessment
    Jian Ma
    http://arxiv.org/abs/2006.16648v1
    • [stat.AP]    Do not forget interaction: Predicting fatality of COVID-19 patients using logistic regression
    Feng Zhou, Tao Chen, Baiying Lei
    http://arxiv.org/abs/2006.16942v1
    • [stat.AP]    From predictions to prescriptions: A data-driven response to COVID-19
    Dimitris Bertsimas, Léonard Boussioux, Ryan Cory Wright, Arthur Delarue, Vassilis Digalakis Jr., Alexandre Jacquillat, Driss Lahlou Kitane, Galit Lukin, Michael Lingzhi Li, Luca Mingardi, Omid Nohadani, Agni Orfanoudaki, Theodore Papalexopoulos, Ivan Paskov, Jean Pauphilet, Omar Skali Lami, Bartolomeo Stellato, Hamza Tazi Bouardi, Kimberly Villalobos Carballo, Holly Wiberg, Cynthia Zeng
    http://arxiv.org/abs/2006.16509v1
    • [stat.AP]    Individual-level Modeling of COVID-19 Epidemic Risk
    Andres Colubri, Kailash Yadav, Abhishek Jha, Pardis Sabeti
    http://arxiv.org/abs/2006.16761v1
    • [stat.AP]    When and where: estimating the date and location of introduction for exotic pests and pathogens
    Trevor J. Hefley, Robin E. Russell, Anne E. Ballmann, Haoyu Zhang
    http://arxiv.org/abs/2006.16982v1
    • [stat.CO]    Hierarchical sparse Cholesky decomposition with applications to high-dimensional spatio-temporal filtering
    Marcin Jurek, Matthias Katzfuss
    http://arxiv.org/abs/2006.16901v1
    • [stat.CO]    Scalable Bayesian Multiple Changepoint Detection via Auxiliary Uniformization
    Lu Shaochuan
    http://arxiv.org/abs/2006.15532v1
    • [stat.CO]    Spatiotemporal Multi-Resolution Approximations for Analyzing Global Environmental Data
    Marius Appel, Edzer Pebesma
    http://arxiv.org/abs/2006.16606v1
    • [stat.ME]    A Robust Adaptive Modified Maximum Likelihood Estimator for the Linear Regression Model
    Sukru Acitas, Peter Filzmoser, Birdal Senoglu
    http://arxiv.org/abs/2006.16657v1
    • [stat.ME]    Autoregressive Mixture Models for Serial Correlation Clustering of Time Series Data
    Benny Ren, Ian Barnett
    http://arxiv.org/abs/2006.16539v1
    • [stat.ME]    Bayesian Analysis of Social Influence
    Johan Koskinen, Galina Daraganova
    http://arxiv.org/abs/2006.16464v1
    • [stat.ME]    Data integration in high dimension with multiple quantiles
    Guorong Dai, Ursula U. Müller, Raymond J. Carroll
    http://arxiv.org/abs/2006.16357v1
    • [stat.ME]    Discussion of the paper “Optimal treatment allocations in space and time for on-line control of an emerging infectious disease’’ by E. B. Laber, N. J. Meyer, B. J. Reich, K. Pacifici, J. A. Collazo and J. Drake
    Johan Koskinen
    http://arxiv.org/abs/2006.16527v1
    • [stat.ME]    Exploring Consequences of Simulation Design for Apparent Performance of Statistical Methods. 1: Results from simulations with constant sample sizes
    Elena Kulinskaya, David C. Hoaglin, Ilyas Bakbergenuly
    http://arxiv.org/abs/2006.16638v1
    • [stat.ME]    G-computation and inverse probability weighting for time-to-event outcomes: a comparative study
    A. Chatton, F. Le Borgne, C. Leyrat, Y. Foucher
    http://arxiv.org/abs/2006.16859v1
    • [stat.ME]    Penalized regression with multiple loss functions and selection by vote
    Guorong Dai, Ursula U. Müller
    http://arxiv.org/abs/2006.16361v1
    • [stat.ME]    Testing and Support Recovery of Correlation Structures for Matrix-Valued Observations with an Application to Stock Market Data
    Xin Chen, Dan Yang, Yan Xu, Yin Xia, Dong Wang, Haipeng Shen
    http://arxiv.org/abs/2006.16501v1
    • [stat.ML]    Black-box Certification and Learning under Adversarial Perturbations
    Hassan Ashtiani, Vinayak Pathak, Ruth Urner
    http://arxiv.org/abs/2006.16520v1
    • [stat.ML]    Conformal Prediction Intervals for Neural Networks Using Cross Validation
    Saeed Khaki, Dan Nettleton
    http://arxiv.org/abs/2006.16941v1
    • [stat.ML]    Consistency of Anchor-based Spectral Clustering
    Henry-Louis de Kergorlay, Desmond John Higham
    http://arxiv.org/abs/2006.13984v2
    • [stat.ML]    Counterfactual Predictions under Runtime Confounding
    Amanda Coston, Edward H. Kennedy, Alexandra Chouldechova
    http://arxiv.org/abs/2006.16916v1
    • [stat.ML]    Guarantees for Tuning the Step Size using a Learning-to-Learn Approach
    Xiang Wang, Shuai Yuan, Chenwei Wu, Rong Ge
    http://arxiv.org/abs/2006.16495v1
    • [stat.ML]    Recovering Joint Probability of Discrete Random Variables from Pairwise Marginals
    Shahana Ibrahim, Xiao Fu
    http://arxiv.org/abs/2006.16912v1
    • [stat.ML]    Recovery of Sparse Signals from a Mixture of Linear Samples
    Arya Mazumdar, Soumyabrata Pal
    http://arxiv.org/abs/2006.16406v1
    • [stat.ML]    Regression with reject option and application to kNN
    Christophe Denis, Mohamed Hebiri, Ahmed Zaoui
    http://arxiv.org/abs/2006.16597v1
    • [stat.ML]    Sharp Statistical Guarantees for Adversarially Robust Gaussian Classification
    Chen Dan, Yuting Wei, Pradeep Ravikumar
    http://arxiv.org/abs/2006.16384v1
    • [stat.ML]    Sinkhorn EM: An Expectation-Maximization algorithm based on entropic optimal transport
    Gonzalo Mena, Amin Nejatbakhsh, Erdem Varol, Jonathan Niles-Weed
    http://arxiv.org/abs/2006.16548v1
    • [stat.ML]    Sparse Gaussian Processes with Spherical Harmonic Features
    Vincent Dutordoir, Nicolas Durrande, James Hensman
    http://arxiv.org/abs/2006.16649v1
    • [stat.ML]    VAE-KRnet and its applications to variational Bayes*
    Xiaoliang Wan, Shuangqing Wei
    http://arxiv.org/abs/2006.16431v1