astro-ph.IM - 仪器仪表和天体物理学方法 cond-mat.stat-mech - 统计数学 cs.AI - 人工智能 cs.CL - 计算与语言 cs.CR - 加密与安全 cs.CV - 机器视觉与模式识别 cs.CY - 计算与社会 cs.DC - 分布式、并行与集群计算 cs.DL - 数字图书馆 cs.DS - 数据结构与算法 cs.GR - 计算机图形学 cs.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.RO - 机器人学 cs.SD - 声音处理 cs.SE - 软件工程 cs.SI - 社交网络与信息网络 econ.EM - 计量经济学 eess.AS - 语音处理 eess.IV - 图像与视频处理 eess.SP - 信号处理 math.OC - 优化与控制 math.PR - 概率 math.ST - 统计理论 physics.acc-ph - 加速器物理学 physics.app-ph - 应用物理 q-bio.NC - 神经元与认知 q-bio.PE - 人口与发展 stat.AP - 应用统计 stat.ME - 统计方法论 stat.ML - (统计)机器学习

    • [astro-ph.IM]Photometric Data-driven Classification of Type Ia Supernovae in the Open Supernova Catalog
    • [cond-mat.stat-mech]Restricted Boltzmann Machine Flows and The Critical Temperature of Ising models
    • [cs.AI]A Study on AI-FML Robotic Agent for Student Learning Behavior Ontology Construction
    • [cs.AI]Compositional Generalization by Learning Analytical Expressions
    • [cs.AI]Practical Large-Scale Distributed Parallel Monte-Carlo Tree Search Applied to Molecular Design
    • [cs.CL]AMALGUM — A Free, Balanced, Multilayer English Web Corpus
    • [cs.CL]Automatic Speech Recognition Benchmark for Air-Traffic Communications
    • [cs.CL]Deep Encoder, Shallow Decoder: Reevaluating the Speed-Quality Tradeoff in Machine Translation
    • [cs.CL]Explainable and Discourse Topic-aware Neural Language Understanding
    • [cs.CL]Extensively Matching for Few-shot Learning Event Detection
    • [cs.CL]Extraction and Evaluation of Formulaic Expressions Used in Scholarly Papers
    • [cs.CL]Is this Dialogue Coherent? Learning from Dialogue Acts and Entities
    • [cs.CL]Multi-branch Attentive Transformer
    • [cs.CL]Octet: Online Catalog Taxonomy Enrichment with Self-Supervision
    • [cs.CL]Political Advertising Dataset: the use case of the Polish 2020 Presidential Elections
    • [cs.CL]Pre-trained Language Models as Symbolic Reasoners over Knowledge?
    • [cs.CL]SEAL: Segment-wise Extractive-Abstractive Long-form Text Summarization
    • [cs.CL]STEAM: Self-Supervised Taxonomy Expansion with Mini-Paths
    • [cs.CR]Drift with Devil: Security of Multi-Sensor Fusion based Localization in High-Level Autonomous Driving under GPS Spoofing (Extended Version)
    • [cs.CR]Lightweight Collaborative Anomaly Detection for the IoT using Blockchain
    • [cs.CR]SwissCovid: a critical analysis of risk assessment by Swiss authorities
    • [cs.CV]3D Pipe Network Reconstruction Based on Structure from Motion with Incremental Conic Shape Detection and Cylindrical Constraint
    • [cs.CV]Automated Radiological Report Generation For Chest X-Rays With Weakly-Supervised End-to-End Deep Learning
    • [cs.CV]BlazePose: On-device Real-time Body Pose tracking
    • [cs.CV]Cascaded Regression Tracking: Towards Online Hard Distractor Discrimination
    • [cs.CV]Contrastive learning of global and local features for medical image segmentation with limited annotations
    • [cs.CV]Cyclic Differentiable Architecture Search
    • [cs.CV]Deep Multitask Learning for Pervasive BMI Estimation and Identity Recognition in Smart Beds
    • [cs.CV]Deep Network for Scatterer Distribution Estimation for Ultrasound Image Simulation
    • [cs.CV]Differentiable Augmentation for Data-Efficient GAN Training
    • [cs.CV]Dissecting Deep Networks into an Ensemble of Generative Classifiers for Robust Predictions
    • [cs.CV]Diverse Image Generation via Self-Conditioned GANs
    • [cs.CV]Fourier Features Let Networks Learn High Frequency Functions in Low Dimensional Domains
    • [cs.CV]Fourth-Order Anisotropic Diffusion for Inpainting and Image Compression
    • [cs.CV]Head2Head++: Deep Facial Attributes Re-Targeting
    • [cs.CV]HyNet: Local Descriptor with Hybrid Similarity Measure and Triplet Loss
    • [cs.CV]Joint Contrastive Learning for Unsupervised Domain Adaptation
    • [cs.CV]Language Guided Networks for Cross-modal Moment Retrieval
    • [cs.CV]Latent Video Transformer
    • [cs.CV]Learning High-Resolution Domain-Specific Representations with a GAN Generator
    • [cs.CV]MOSQUITO-NET: A deep learning based CADx system for malaria diagnosis along with model interpretation using GradCam and class activation maps
    • [cs.CV]MediaPipe Hands: On-device Real-time Hand Tracking
    • [cs.CV]Multi-Density Sketch-to-Image Translation Network
    • [cs.CV]Neural Graphics Pipeline for Controllable Image Generation
    • [cs.CV]Ocean: Object-aware Anchor-free Tracking
    • [cs.CV]On the Robustness of Active Learning
    • [cs.CV]Online Deep Clustering for Unsupervised Representation Learning
    • [cs.CV]Overcoming Classifier Imbalance for Long-tail Object Detection with Balanced Group Softmax
    • [cs.CV]Overcoming Statistical Shortcuts for Open-ended Visual Counting
    • [cs.CV]Progressively Unfreezing Perceptual GAN
    • [cs.CV]SatImNet: Structured and Harmonised Training Data for Enhanced Satellite Imagery Classification
    • [cs.CV]SceneAdapt: Scene-based domain adaptation for semantic segmentation using adversarial learning
    • [cs.CV]Self-Supervised Joint Learning Framework of Depth Estimation via Implicit Cues
    • [cs.CV]Semi-Supervised Recognition under a Noisy and Fine-grained Dataset
    • [cs.CV]Sequential Graph Convolutional Network for Active Learning
    • [cs.CV]Spin-Weighted Spherical CNNs
    • [cs.CV]TearingNet: Point Cloud Autoencoder to Learn Topology-Friendly Representations
    • [cs.CV]UV-Net: Learning from Curve-Networks and Solids
    • [cs.CV]Unsupervised Learning of Visual Features by Contrasting Cluster Assignments
    • [cs.CV]Unsupervised out-of-distribution detection using kernel density estimation
    • [cs.CV]Use of in-the-wild images for anomaly detection in face anti-spoofing
    • [cs.CV]Video Moment Localization using Object Evidence and Reverse Captioning
    • [cs.CV]Video Semantic Segmentation with Distortion-Aware Feature Correction
    • [cs.CV]Vision-Aided Dynamic Blockage Prediction for 6G Wireless Communication Networks
    • [cs.CY]”EHLO WORLD” — Checking If Your Conversational AI Knows Right from Wrong
    • [cs.CY]Enterprise System Lifecycle-wide Innovation
    • [cs.CY]Wide-Area Data Analytics
    • [cs.DC]Efficient Execution of Quantized Deep Learning Models: A Compiler Approach
    • [cs.DC]Faster Secure Data Mining via Distributed Homomorphic Encryption
    • [cs.DC]Is Network the Bottleneck of Distributed Training?
    • [cs.DC]Resource Pools and the CAP Theorem
    • [cs.DC]The Only Undoable CRDTs are Counters
    • [cs.DL]Mapping the “long tail” of research funding: A topic analysis of NSF grant proposals in the Division of Astronomical Sciences
    • [cs.DS]Fair Hierarchical Clustering
    • [cs.GR]Structure and Design of HoloGen
    • [cs.IR]A Knowledge-Enhanced Recommendation Model with Attribute-Level Co-Attention
    • [cs.IR]Interactive Recommender System via Knowledge Graph-enhanced Reinforcement Learning
    • [cs.IR]MIMICS: A Large-Scale Data Collection for Search Clarification
    • [cs.IR]MIMICS: A Large-Scale Data Collection for Search Clarification
    • [cs.IT]A Fast Binary Splitting Approach to Non-Adaptive Group Testing
    • [cs.IT]A decoding algorithm for 2D convolutional codes over the erasure channel
    • [cs.IT]Federated Learning With Quantized Global Model Updates
    • [cs.IT]Information Extraction from a Strategic Sender: The Zero Error Case
    • [cs.IT]Low-Rank Parity-Check Codes over Galois Rings
    • [cs.IT]No projective 16-divisible binary linear code of length 131 exists
    • [cs.IT]Reconfigurable Intelligent Surfaces for Energy Efficiency in D2D Communication Network
    • [cs.IT]Tight Bounds for Connectivity of Random K-out Graphs
    • [cs.LG]A Practical Online Method for Distributionally Deep Robust Optimization
    • [cs.LG]A Tutorial on VAEs: From Bayes’ Rule to Lossless Compression
    • [cs.LG]A block coordinate descent optimizer for classification problems exploiting convexity
    • [cs.LG]Advances in Black-Box VI: Normalizing Flows, Importance Weighting, and Optimization
    • [cs.LG]Algorithmic Decision Making with Conditional Fairness
    • [cs.LG]An Investigation of the Weight Space for Version Control of Neural Networks
    • [cs.LG]Calibrated Reliable Regression using Maximum Mean Discrepancy
    • [cs.LG]Category-Specific CNN for Visual-aware CTR Prediction at JD.com
    • [cs.LG]Class-Attentive Diffusion Network for Semi-Supervised Classification
    • [cs.LG]Competitive Policy Optimization
    • [cs.LG]Confident Off-Policy Evaluation and Selection through Self-Normalized Importance Weighting
    • [cs.LG]Constraining Variational Inference with Geometric Jensen-Shannon Divergence
    • [cs.LG]Constraint-Based Regularization of Neural Networks
    • [cs.LG]DREAM: Deep Regret minimization with Advantage baselines and Model-free learning
    • [cs.LG]Deep Reinforcement Learning amidst Lifelong Non-Stationarity
    • [cs.LG]DisARM: An Antithetic Gradient Estimator for Binary Latent Variables
    • [cs.LG]Distributed Value Function Approximation for Collaborative Multi-Agent Reinforcement Learning
    • [cs.LG]DrNAS: Dirichlet Neural Architecture Search
    • [cs.LG]Erdos Goes Neural: an Unsupervised Learning Framework for Combinatorial Optimization on Graphs
    • [cs.LG]Fair k-Means Clustering
    • [cs.LG]Flows Succeed Where GANs Fail: Lessons from Low-Dimensional Data
    • [cs.LG]Forward Prediction for Physical Reasoning
    • [cs.LG]Fully Test-time Adaptation by Entropy Minimization
    • [cs.LG]GAT-GMM: Generative Adversarial Training for Gaussian Mixture Models
    • [cs.LG]GCC: Graph Contrastive Coding for Graph Neural Network Pre-Training
    • [cs.LG]Gradient Amplification: An efficient way to train deep neural networks
    • [cs.LG]Housing Market Prediction Problem using Different Machine Learning Algorithms: A Case Study
    • [cs.LG]I-BERT: Inductive Generalization of Transformer to Arbitrary Context Lengths
    • [cs.LG]IReEn: Iterative Reverse-Engineering of Black-Box Functions via Neural Program Synthesis
    • [cs.LG]Improvements in Computation and Usage of Joint CDFs for the N-Dimensional Order Statistic
    • [cs.LG]Kernel methods through the roof: handling billions of points efficiently
    • [cs.LG]Learning Invariant Representations for Reinforcement Learning without Reconstruction
    • [cs.LG]Learning by Repetition: Stochastic Multi-armed Bandits under Priming Effect
    • [cs.LG]Learning to Track Dynamic Targets in Partially Known Environments
    • [cs.LG]Leveraging Model Inherent Variable Importance for Stable Online Feature Selection
    • [cs.LG]Likelihood-Free Inference with Deep Gaussian Processes
    • [cs.LG]List-Decodable Mean Estimation via Iterative Multi-Fitering
    • [cs.LG]Local Competition and Uncertainty for Adversarial Robustness in Deep Learning
    • [cs.LG]MMCGAN: Generative Adversarial Network with Explicit Manifold Prior
    • [cs.LG]Markovian RNN: An Adaptive Time Series Prediction Network with HMM-based Switching for Nonstationary Environments
    • [cs.LG]Minimax Lower Bounds for Transfer Learning with Linear and One-hidden Layer Neural Networks
    • [cs.LG]Multi-Source Unsupervised Hyperparameter Optimization
    • [cs.LG]Nearly Optimal Robust Method for Convex Compositional Problems with Heavy-Tailed Noise
    • [cs.LG]Neural Architecture Optimization with Graph VAE
    • [cs.LG]OMBA: User-Guided Product Representations for Online Market Basket Analysis
    • [cs.LG]Offline detection of change-points in the mean for stationary graph signals
    • [cs.LG]On Sparsity in Overparametrised Shallow ReLU Networks
    • [cs.LG]On the Predictability of Pruning Across Scales
    • [cs.LG]On the Role of Sparsity and DAG Constraints for Learning Linear DAGs
    • [cs.LG]Open Ad Hoc Teamwork using Graph-based Policy Learning
    • [cs.LG]Precise expressions for random proj
    7e3
    ections: Low-rank approximation and randomized Newton
    • [cs.LG]Quantifying Challenges in the Application of Graph Representation Learning
    • [cs.LG]Record fusion: A learning approach
    • [cs.LG]Rethinking Semi-Supervised Learning in VAEs
    • [cs.LG]Revisiting complexity and the bias-variance tradeoff
    • [cs.LG]Robust Group Subspace Recovery: A New Approach for Multi-Modality Data Fusion
    • [cs.LG]Robust Unsupervised Learning of Temporal Dynamic Interactions
    • [cs.LG]SE(3)-Transformers: 3D Roto-Translation Equivariant Attention Networks
    • [cs.LG]STEER : Simple Temporal Regularization For Neural ODEs
    • [cs.LG]SXL: Spatially explicit learning of geographic processes with auxiliary tasks
    • [cs.LG]Self-supervised Learning on Graphs: Deep Insights and New Direction
    • [cs.LG]Set Distribution Networks: a Generative Model for Sets of Images
    • [cs.LG]Shapeshifter Networks: Cross-layer Parameter Sharing for Scalable and Effective Deep Learning
    • [cs.LG]Simple and Principled Uncertainty Estimation with Deterministic Deep Learning via Distance Awareness
    • [cs.LG]Smoothed Analysis of Online and Differentially Private Learning
    • [cs.LG]Sparse Bottleneck Networks for Exploratory Analysis and Visualization of Neural Patch-seq Data
    • [cs.LG]Stochastic Bandits with Linear Constraints
    • [cs.LG]Subgraph Neural Networks
    • [cs.LG]Temporal Graph Networks for Deep Learning on Dynamic Graphs
    • [cs.LG]Tensor Decompositions in Recursive NeuralNetworks for Tree-Structured Data
    • [cs.LG]The Clever Hans Effect in Anomaly Detection
    • [cs.LG]The Recurrent Neural Tangent Kernel
    • [cs.LG]Towards Recurrent Autoregressive Flow Models
    • [cs.LG]Towards Threshold Invariant Fair Classification
    • [cs.LG]Uncertainty in Gradient Boosting via Ensembles
    • [cs.LG]Unsupervised Meta-Learning through Latent-Space Interpolation in Generative Models
    • [cs.LG]Zero-Shot Learning with Common Sense Knowledge Graphs
    • [cs.NE]A Shooting Formulation of Deep Learning
    • [cs.RO]A Mechanical Screwing Tool for 2-Finger Parallel Grippers — Design, Optimization, and Manipulation Policies
    • [cs.RO]PIDA: Smooth and Stable Flight Using Stochastic Dual Simplex Algorithm and Genetic Filter
    • [cs.SD]Artificial Musical Intelligence: A Survey
    • [cs.SE]Robotics Software Engineering: A Perspective from the Service Robotics Domain
    • [cs.SI]A Multi-View Approach Based on Naming Behavioral Modeling for Aligning Chinese User Accounts across Multiple Networks
    • [cs.SI]A unified framework for equivalences in social networks
    • [cs.SI]Catching them red-handed: Real-time Aggression Detection on Social Media
    • [cs.SI]Market Graph Clustering Via QUBO and Digital Annealing
    • [cs.SI]Using Sentiment Information for Preemptive Detection of Toxic Comments in Online Conversations
    • [econ.EM]Approximate Maximum Likelihood for Complex Structural Models
    • [econ.EM]Sparse HP Filter: Finding Kinks in the COVID-19 Contact Rate
    • [eess.AS]Are you wearing a mask? Improving mask detection from speech using augmentation by cycle-consistent GANs
    • [eess.IV]ChestX-det10: Chest X-ray Dataset on Detection of Thoracic Abnormalities
    • [eess.IV]Generating Fundus Fluorescence Angiography Images from Structure Fundus Images Using Generative Adversarial Networks
    • [eess.IV]XRayGAN: Consistency-preserving Generation of X-ray Images from Radiology Reports
    • [eess.SP]A Review of 1D Convolutional Neural Networks toward Unknown Substance Identification in Portable Raman Spectrometer
    • [eess.SP]Cell-Free Massive MIMO with Nonorthogonal Pilots for Internet of Things
    • [eess.SP]Hybrid Beamforming Structure for Massive MIMO System: Full-connection v.s. Partial-connection
    • [eess.SP]Structured Massive Access for Scalable Cell-Free Massive MIMO Systems
    • [math.OC]Apollonius Allocation Algorithm for Heterogeneous Pursuers to Capture Multiple Evaders
    • [math.OC]SGD for Structured Nonconvex Functions: Learning Rates, Minibatching and Interpolation
    • [math.PR]Free Energy Wells and Overlap Gap Property in Sparse PCA
    • [math.ST]Inference for local parameters in convexity constrained models
    • [math.ST]Right-truncated Archimedean and related copulas
    • [physics.acc-ph]Computing techniques
    • [physics.app-ph]A flexible spiraling-metasurface as a versatile haptic interface
    • [q-bio.NC]A Representational Model of Grid Cells Based on Matrix Lie Algebras
    • [q-bio.PE]Analysis of Virus Propagation: A Transition Model Representation of Stochastic Epidemiological Models
    • [stat.AP]CytOpT: Optimal Transport with Domain Adaptation for Interpreting Flow Cytometry data
    • [stat.AP]Homogeneity Test for Functional Data basedon Data-Depth Plots
    • [stat.AP]Small Area Estimation of Health Outcomes
    • [stat.AP]The Essential Role of Empirical Validation in Legislative Redistricting Simulation
    • [stat.AP]Uncertainty quantification for epidemiological forecasts of COVID-19 through combinations of model predictions
    • [stat.ME]Asymptotic distribution-free change-point detection for data with repeated observations
    • [stat.ME]Bayesian Changepoint Analysis
    • [stat.ME]Bayesian Elastic Net based on Empirical Likelihood
    • [stat.ME]Fast Tail Index Estimation for Power Law Distributions in R
    • [stat.ME]Functional Group Bridge for Simultaneous Regression and Support Estimation
    • [stat.ME]Transfer Learning for High-dimensional Linear Regression: Prediction, Estimation, and Minimax Optimality
    • [stat.ME]Using Weighted P-Values in Fisher’s Method
    • [stat.ML]A Framework for Sample Efficient Interval Estimation with Control Variates
    • [stat.ML]Active Learning for Nonlinear System Identification with Guarantees
    • [stat.ML]Distribution-free binary classification: prediction sets, confidence intervals and calibration
    • [stat.ML]Exact posterior distributions of wide Bayesian neural networks
    • [stat.ML]Guarantees for Hierarchical Clustering by the Sublevel Set method
    • [stat.ML]Individual Calibration with Randomized Forecasting
    • [stat.ML]Infinite attention: NNGP and NTK for deep attention networks
    • [stat.ML]Low-Rank Autoregressive Tensor Completion for Multivariate Time Series Forecasting
    • [stat.ML]Matern Gaussian processes on Riemannian manifolds
    • [stat.ML]Median Matrix Completion: from Embarrassment to Optimality
    • [stat.ML]MoFlow: An Invertible Flow Model for Generating Molecular Graphs
    • [stat.ML]Neural Manifold Ordinary Differential Equations
    • [stat.ML]Riemannian Continuous Normalizing Flows
    • [stat.ML]Robust compressed sensing of generative models
    • [stat.ML]Stochastic bandits with arm-dependent delays
    • [stat.ML]Variational Autoencoder with Learned Latent Structure
    • [stat.ML]Variational State-Space Models for Localisation and Dense 3D Mapping in 6 DoF
    • [stat.ML]What Do Neural Networks Learn When Trained With Random Labels?
    • [stat.ML]When Does Preconditioning Help or Hurt Generalization?
    • [stat.ML]When OT meets MoM: Robust estimation of Wasserstein Distance
    ·····································
    • [astro-ph.IM]Photometric Data-driven Classification of Type Ia Supernovae in the Open Supernova Catalog
    Stanislav Dobryakov, Konstantin Malanchev, Denis Derkach, Mikhail Hushchyn
    http://arxiv.org/abs/2006.10489v1
    • [cond-mat.stat-mech]Restricted Boltzmann Machine Flows and The Critical Temperature of Ising models
    Rodrigo Veiga, Renato Vicente
    http://arxiv.org/abs/2006.10176v1
    • [cs.AI]A Study on AI-FML Robotic Agent for Student Learning Behavior Ontology Construction
    Chang-Shing Lee, Mei-Hui Wang, Wen-Kai Kuan, Zong-Han Ciou, Yi-Lin Tsai, Wei-Shan Chang, Lian-Chao Li, Naoyuki Kubota, Tzong-Xiang Huang, Eri Sato-Shimokawara, Toru Yamaguchi
    http://arxiv.org/abs/2006.10228v1
    • [cs.AI]Compositional Generalization by Learning Analytical Expressions
    Qian Liu, Shengnan An, Jian-Guang Lou, Bei Chen, Zeqi Lin, Yan Gao, Bin Zhou, Nanning Zheng, Dongmei Zhang
    http://arxiv.org/abs/2006.10627v1
    • [cs.AI]Practical Large-Scale Distributed Parallel Monte-Carlo Tree Search Applied to Molecular Design
    Xiufeng Yang, Tanuj Kr Aasawat, Kazuki Yoshizoe
    http://arxiv.org/abs/2006.10504v1
    • [cs.CL]AMALGUM — A Free, Balanced, Multilayer English Web Corpus
    Luke Gessler, Siyao Peng, Yang Liu, Yilun Zhu, Shabnam Behzad, Amir Zeldes
    http://arxiv.org/abs/2006.10677v1
    • [cs.CL]Automatic Speech Recognition Benchmark for Air-Traffic Communications
    Juan Zuluaga-Gomez, Petr Motlicek, Qingran Zhan, Karel Vesely, Rudolf Braun
    http://arxiv.org/abs/2006.10304v1
    • [cs.CL]Deep Encoder, Shallow Decoder: Reevaluating the Speed-Quality Tradeoff in Machine Translation
    Jungo Kasai, Nikolaos Pappas, Hao Peng, James Cross, Noah A. Smith
    http://arxiv.org/abs/2006.10369v1
    • [cs.CL]Explainable and Discourse Topic-aware Neural Language Understanding
    Yatin Chaudhary, Hinrich Schütze, Pankaj Gupta
    http://arxiv.org/abs/2006.10632v1
    • [cs.CL]Extensively Matching for Few-shot Learning Event Detection
    Viet Dac Lai, Franck Dernoncourt, Thien Huu Nguyen
    http://arxiv.org/abs/2006.10093v1
    • [cs.CL]Extraction and Evaluation of Formulaic Expressions Used in Scholarly Papers
    Kenichi Iwatsuki, Florian Boudin, Akiko Aizawa
    http://arxiv.org/abs/2006.10334v1
    • [cs.CL]Is this Dialogue Coherent? Learning from Dialogue Acts and Entities
    Alessandra Cervone, Giuseppe Riccardi
    http://arxiv.org/abs/2006.10157v1
    • [cs.CL]Multi-branch Attentive Transformer
    Yang Fan, Shufang Xie, Yingce Xia, Lijun Wu, Tao Qin, Xiang-Yang Li, Tie-Yan Liu
    http://arxiv.org/abs/2006.10270v1
    • [cs.CL]Octet: Online Catalog Taxonomy Enrichment with Self-Supervision
    Yuning Mao, Tong Zhao, Andrey Kan, Chenwei Zhang, Xin Luna Dong, Christos Faloutsos, Jiawei Han
    http://arxiv.org/abs/2006.10276v1
    • [cs.CL]Political Advertising Dataset: the use case of the Polish 2020 Presidential Elections
    Łukasz Augustyniak, Krzysztof Rajda, Tomasz Kajdanowicz, Michał Bernaczyk
    http://arxiv.org/abs/2006.10207v1
    • [cs.CL]Pre-trained Language Models as Symbolic Reasoners over Knowledge?
    Nora Kassner, Benno Kroje, Hinrich Schütze
    http://arxiv.org/abs/2006.10413v1
    • [cs.CL]SEAL: Segment-wise Extractive-Abstractive Long-form Text Summarization
    Yao Zhao, Mohammad Saleh, Peter J. Liu
    http://arxiv.org/abs/2006.10213v1
    • [cs.CL]STEAM: Self-Supervised Taxonomy Expansion with Mini-Paths
    Yue Yu, Yinghao Li, Jiaming Shen, Hao Feng, Jimeng Sun, Chao Zhang
    http://arxiv.org/abs/2006.10217v1
    • [cs.CR]Drift with Devil: Security of Multi-Sensor Fusion based Localization in High-Level Autonomous Driving under GPS Spoofing (Extended Version)
    Junjie Shen, Jun Yeon Won, Zeyuan Chen, Qi Alfred Chen
    http://arxiv.org/abs/2006.10318v1
    • [cs.CR]Lightweight Collaborative Anomaly Detection for the IoT using Blockchain
    Yisroel Mirsky, Tomer Golomb, Yuval Elovici
    http://arxiv.org/abs/2006.10587v1
    • [cs.CR]SwissCovid: a critical analysis of risk assessment by Swiss authorities
    Paul-Olivier Dehaye, Joel Reardon
    http://arxiv.org/abs/2006.10719v1
    • [cs.CV]3D Pipe Network Reconstruction Based on Structure from Motion with Incremental Conic Shape Detection and Cylindrical Constraint
    Sho kagami, Hajime Taira, Naoyuki Miyashita, Akihiko Torii, Masatoshi Okutomi
    http://arxiv.org/abs/2006.10383v1
    • [cs.CV]Automated Radiological Report Generation For Chest X-Rays With Weakly-Supervised End-to-End Deep Learning
    Shuai Zhang, Xiaoyan Xin, Yang Wang, Yachong Guo, Qiuqiao Hao, Xianfeng Yang, Xianfeng Yang, Jian Zhang, Bing Zhang, Wei Wang
    http://arxiv.org/abs/2006.10347v1
    • [cs.CV]BlazePose: On-device Real-time Body Pose tracking
    Valentin Bazarevsky, Ivan Grishchenko, Karthik Raveendran, Tyler Zhu, Fan Zhang, Matthias Grundmann
    http://arxiv.org/abs/2006.10204v1
    • [cs.CV]Cascaded Regression Tracking: Towards Online Hard Distractor Discrimination
    Ning Wang, Wengang Zhou, Qi Tian, Houqiang Li
    http://arxiv.org/abs/2006.10336v1
    • [cs.CV]Contrastive learning of global and local features for medical image segmentation with limited annotations
    Krishna Chaitanya, Ertunc Erdil, Neerav Karani, Ender Konukoglu
    http://arxiv.org/abs/2006.10511v1
    • [cs.CV]Cyclic Differentiable Architecture Search
    Hongyuan Yu, Houwen Peng
    http://arxiv.org/abs/2006.10724v1
    • [cs.CV]Deep Multitask Learning for Pervasive BMI Estimation and Identity Recognition in Smart Beds
    Vandad Davoodnia, Monet Slinowsky, Ali Etemad
    http://arxiv.org/abs/2006.10453v1
    • [cs.CV]Deep Network for Scatterer Distribution Estimation for Ultrasound Image Simulation
    Lin Zhang, Valery Vishnevskiy, Orcun Goksel
    http://arxiv.org/abs/2006.10166v1
    • [cs.CV]Differentiable Augmentation for Data-Efficient GAN Training
    Shengyu Zhao, Zhijian Liu, Ji Lin, Jun-Yan Zhu, Song Han
    http://arxiv.org/abs/2006.10738v1
    • [cs.CV]Dissecting Deep Networks into an Ensemble of Generative Classifiers for Robust Predictions
    Lokender Tiwari, Anish Madan, Saket Anand, Subhashis Banerjee
    http://arxiv.org/abs/2006.10679v1
    • [cs.CV]Diverse Image Generation via Self-Conditioned GANs
    Steven Liu, Tongzhou Wang, David Bau, Jun-Yan Zhu, Antonio Torralba
    http://arxiv.org/abs/2006.10728v1
    • [cs.CV]Fourier Features Let Networks Learn High Frequency Functions in Low Dimensional Domains
    Matthew Tancik, Pratul P. Srinivasan, Ben Mildenhall, Sara Fridovich-Keil, Nithin Raghavan, Utkarsh Singhal, Ravi Ramamoorthi, Jonathan T. Barron, Ren Ng
    http://arxiv.org/abs/2006.10739v1
    • [cs.CV]Fourth-Order Anisotropic Diffusion for Inpainting and Image Compression
    Ikram Jumakulyyev, Thomas Schultz
    http://arxiv.org/abs/2006.10406v1
    • [cs.CV]Head2Head++: Deep Facial Attributes Re-Targeting
    Michail Christos Doukas, Mohammad Rami Koujan, Viktoriia Sharmanska, Anastasios Roussos
    http://arxiv.org/abs/2006.10199v1
    • [cs.CV]HyNet: Local Descriptor with Hybrid Similarity Measure and Triplet Loss
    Yurun Tian, Axel Barroso-Laguna, Tony Ng, Vassileios Balntas, Krystian Mikolajczyk
    http://arxiv.org/abs/2006.10202v1
    • [cs.CV]Joint Contrastive Learning for Unsupervised Domain Adaptation
    Changhwa Park, Jonghyun Lee, Jaeyoon Yoo, Minhoe Hur, Sungroh Yoon
    http://arxiv.org/abs/2006.10297v1
    • [cs.CV]Language Guided Networks for Cross-modal Moment Retrieval
    Kun Liu, Xun Yang, Tat-seng Chua, Huadong Ma, Chuang Gan
    http://arxiv.org/abs/2006.10457v1
    • [cs.CV]Latent Video Transformer
    Ruslan Rakhimov, Denis Volkhonskiy, Alexey Artemov, Denis Zorin, Evgeny Burnaev
    http://arxiv.org/abs/2006.10704v1
    • [cs.CV]Learning High-Resolution Domain-Specific Representations with a GAN Generator
    Danil Galeev, Konstantin Sofiiuk, Danila Rukhovich, Mikhail Romanov, Olga Barinova, Anton Konushin
    http://arxiv.org/abs/2006.10451v1
    • [cs.CV]MOSQUITO-NET: A deep learning based CADx system for malaria diagnosis along with model interpretation using GradCam and class activation maps
    Aayush Kumar, Sanat B Singh, Suresh Chandra Satapathy, Minakhi Rout
    http://arxiv.org/abs/2006.10547v1
    • [cs.CV]MediaPipe Hands: On-device Real-time Hand Tracking
    Fan Zhang, Valentin Bazarevsky, Andrey Vakunov, Andrei Tkachenka, George Sung, Chuo-Ling Chang, Matthias Grundmann
    http://arxiv.org/abs/2006.10214v1
    • [cs.CV]Multi-Density Sketch-to-Image Translation Network
    Jialu Huang, Jing Liao, Zhifeng Tan, Sam Kwong
    http://arxiv.org/abs/2006.10649v1
    • [cs.CV]Neural Graphics Pipeline for Controllable Image Generation
    Xuelin Chen, Daniel Cohen-Or, Baoquan Chen, Niloy J. Mitra
    http://arxiv.org/abs/2006.10569v1
    • [cs.CV]Ocean: Object-aware Anchor-free Tracking
    Zhipeng Zhang, Houwen Peng
    http://arxiv.org/abs/2006.10721v1
    • [cs.CV]On the Robustness of Active Learning
    Lukas Hahn, Lutz Roese-Koerner, Peet Cremer, Urs Zimmermann, Ori Maoz, Anton Kummert
    http://arxiv.org/abs/2006.10370v1
    • [cs.CV]Online Deep Clustering for Unsupervised Representation Learning
    Xiaohang Zhan, Jiahao Xie, Ziwei Liu, Yew Soon Ong, Chen Change Loy
    http://arxiv.org/abs/2006.10645v1
    • [cs.CV]Overcoming Classifier Imbalance for Long-tail Object Detection with Balanced Group Softmax
    Yu Li, Tao Wang, Bingyi Kang, Sheng Tang, Chunfeng Wang, Jintao Li, Jiashi Feng
    http://arxiv.org/abs/2006.10408v1
    • [cs.CV]Overcoming Statistical Shortcuts for Open-ended Visual Counting
    Corentin Dancette, Remi Cadene, Xinlei Chen, Matthieu Cord
    http://arxiv.org/abs/2006.10079v1
    • [cs.CV]Progressively Unfreezing Perceptual GAN
    Jinxuan Sun, Yang Chen, Junyu Dong, Guoqiang Zhong
    http://arxiv.org/abs/2006.10250v1
    • [cs.CV]SatImNet: Structured and Harmonised Training Data for Enhanced Satellite Imagery Classification
    Vasileios Syrris, Ondrej Pesek, Pierre Soille
    http://arxiv.org/abs/2006.10623v1
    • [cs.CV]SceneAdapt: Scene-based domain adaptation for semantic segmentation using adversarial learning
    Daniele Di Mauro, Antonino Furnari, Giuseppe Patanè, Sebastiano Battiato, Giovanni Maria Farinella
    http://arxiv.org/abs/2006.10386v1
    • [cs.CV]Self-Supervised Joint Learning Framework of Depth Estimation via Implicit Cues
    Jianrong Wang, Ge Zhang, Zhenyu Wu, XueWei Li, Li Liu
    http://arxiv.org/abs/2006.09876v2
    • [cs.CV]Semi-Supervised Recognition under a Noisy and Fine-grained Dataset
    Cheng Cui, Zhi Ye, Yangxi Li, Xinjian Li, Min Yang, Kai Wei, Bing Dai, Yanmei Zhao, Zhongji Liu, Rong Pang
    http://arxiv.org/abs/2006.10702v1
    • [cs.CV]Sequential Graph Convolutional Network for Active Learning
    Razvan Caramalau, Binod Bhattarai, Tae-Kyun Kim
    http://arxiv.org/abs/2006.10219v1
    • [cs.CV]Spin-Weighted Spherical CNNs
    Carlos Esteves, Ameesh Makadia, Kostas Daniilidis
    http://arxiv.org/abs/2006.10731v1
    • [cs.CV]TearingNet: Point Cloud Autoencoder to Learn Topology-Friendly Representations
    Jiahao Pang, Duanshun Li, Dong Tian
    http://arxiv.org/abs/2006.10187v1
    • [cs.CV]UV-Net: Learning from Curve-Networks and Solids
    Pradeep Kumar Jayaraman, Aditya Sanghi, Joseph Lambourne, Thomas Davies, Hooman Shayani, Nigel Morris
    http://arxiv.org/abs/2006.10211v1
    • [cs.CV]Unsupervised Learning of Visual Features by Contrasting Cluster Assignments
    Mathilde Caron, Ishan Misra, Julien Mairal, Priya Goyal, Piotr Bojanowski, Armand Joulin
    http://arxiv.org/abs/2006.09882v2
    • [cs.CV]Unsupervised out-of-distribution detection using kernel density estimation
    Ertunc Erdil, Krishna Chaitanya, Ender Konukoglu
    http://arxiv.org/abs/2006.10712v1
    • [cs.CV]Use of in-the-wild images for anomaly detection in face anti-spoofing
    Latifah Abduh, Ioannis Ivrissimtzis
    http://arxiv.org/abs/2006.10626v1
    • [cs.CV]Video Moment Localization using Object Evidence and Reverse Captioning
    Madhawa Vidanapathirana, Supriya Pandhre, Sonia Raychaudhuri, Anjali Khurana
    http://arxiv.org/abs/2006.10260v1
    • [cs.CV]Video Semantic Segmentation with Distortion-Aware Feature Correction
    Jiafan Zhuang, Zilei Wang, Bingke Wang
    http://arxiv.org/abs/2006.10380v1
    • [cs.CV]Vision-Aided Dynamic Blockage Prediction for 6G Wireless Communication Networks
    Gouranga Charan, Muhammad Alrabeiah, Ahmed Alkhateeb
    http://arxiv.org/abs/2006.09902v2
    • [cs.CY]“EHLO WORLD” — Checking If Your Conversational AI Knows Right from Wrong
    Elayne Ruane, Vivek Nallur
    http://arxiv.org/abs/2006.10437v1
    • [cs.CY]Enterprise System Lifecycle-wide Innovation
    Sachithra Lokuge, Darshana Sedera
    http://arxiv.org/abs/2006.10237v1
    • [cs.CY]Wide-Area Data Analytics
    Rachit Agarwal, Jen Rexford, with contributions from numerous workshop attendees
    http://arxiv.org/abs/2006.10188v1
    • [cs.DC]Efficient Execution of Quantized Deep Learning Models: A Compiler Approach
    Animesh Jain, Shoubhik Bhattacharya, Masahiro Masuda, Vin Sharma, Yida Wang
    http://arxiv.org/abs/2006.10226v1
    • [cs.DC]Faster Secure Data Mining via Distributed Homomorphic Encryption
    Junyi Li, Heng Huang
    http://arxiv.org/abs/2006.10091v1
    • [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/2006.10103v1
    • [cs.DC]Resource Pools and the CAP Theorem
    Andrew Lewis-Pye, Tim Roughgarden
    http://arxiv.org/abs/2006.10698v1
    • [cs.DC]The Only Undoable CRDTs are Counters
    Stephen Dolan
    http://arxiv.org/abs/2006.10494v1
    • [cs.DL]Mapping the “long tail” of research funding: A topic analysis of NSF grant proposals in the Division of Astronomical Sciences
    Gretchen R. Stahlman, P. Bryan Heidorn
    http://arxiv.org/abs/2006.10673v1
    • [cs.DS]Fair Hierarchical Clustering
    Sara Ahmadian, Alessandro Epasto, Marina Knittel, Ravi Kumar, Mohammad Mahdian, Benjamin Moseley, Philip Pham, Sergei Vassilvtiskii, Yuyan Wang
    http://arxiv.org/abs/2006.10221v1
    • [cs.GR]Structure and Design of HoloGen
    Peter J. Christopher, Timothy D. Wilkinson
    http://arxiv.org/abs/2006.10509v1
    • [cs.IR]A Knowledge-Enhanced Recommendation Model with Attribute-Level Co-Attention
    Deqing Yang, Zengcun Song, Lvxin Xue, Yanghua Xiao
    http://arxiv.org/abs/2006.10233v1
    • [cs.IR]Interactive Recommender System via Knowledge Graph-enhanced Reinforcement Learning
    Sijin Zhou, Xinyi Dai, Haokun Chen, Weinan Zhang, Kan Ren, Ruiming Tang, Xiuqiang He, Yong Yu
    http://arxiv.org/abs/2006.10389v1
    • [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/rg/abs/2006.10174v1
    • [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/2006.10174v1
    • [cs.IT]A Fast Binary Splitting Approach to Non-Adaptive Group Testing
    Eric Price, Jonathan Scarlett
    http://arxiv.org/abs/2006.10268v1
    • [cs.IT]A decoding algorithm for 2D convolutional codes over the erasure channel
    Julia Lieb, Raquel Pinto
    http://arxiv.org/abs/2006.10527v1
    • [cs.IT]Federated Learning With Quantized Global Model Updates
    Mohammad Mohammadi Amiri, Deniz Gunduz, Sanjeev R. Kulkarni, H. Vincent Poor
    http://arxiv.org/abs/2006.10672v1
    • [cs.IT]Information Extraction from a Strategic Sender: The Zero Error Case
    Anuj S. Vora, Ankur A. Kulkarni
    http://arxiv.org/abs/2006.10641v1
    • [cs.IT]Low-Rank Parity-Check Codes over Galois Rings
    Julian Renner, Alessandro Neri, Sven Puchinger
    http://arxiv.org/abs/2006.10588v1
    • [cs.IT]No projective 16-divisible binary linear code of length 131 exists
    Sascha Kurz
    http://arxiv.org/abs/2006.10382v1
    • [cs.IT]Reconfigurable Intelligent Surfaces for Energy Efficiency in D2D Communication Network
    Shuaiqi Jia, Xiaojun Yuan, Ying-Chang Liang
    http://arxiv.org/abs/2006.10320v1
    • [cs.IT]Tight Bounds for Connectivity of Random K-out Graphs
    Mansi Sood, Osman Yagan
    http://arxiv.org/abs/2006.10638v1
    • [cs.LG]A Practical Online Method for Distributionally Deep Robust Optimization
    Qi Qi, Zhishuai Guo, Yi Xu, Rong Jin, Tianbao Yang
    http://arxiv.org/abs/2006.10138v1
    • [cs.LG]A Tutorial on VAEs: From Bayes’ Rule to Lossless Compression
    Ronald Yu
    http://arxiv.org/abs/2006.10273v1
    • [cs.LG]A block coordinate descent optimizer for classification problems exploiting convexity
    Ravi G. Patel, Nathaniel A. Trask, Mamikon A. Gulian, Eric C. Cyr
    http://arxiv.org/abs/2006.10123v1
    • [cs.LG]Advances in Black-Box VI: Normalizing Flows, Importance Weighting, and Optimization
    Abhinav Agrawal, Daniel Sheldon, Justin Domke
    http://arxiv.org/abs/2006.10343v1
    • [cs.LG]Algorithmic Decision Making with Conditional Fairness
    Renzhe Xu, Peng Cui, Kun Kuang, Bo Li, Linjun Zhou, Zheyan Shen, Wei Cui
    http://arxiv.org/abs/2006.10483v1
    • [cs.LG]An Investigation of the Weight Space for Version Control of Neural Networks
    Konstantin Schürholt, Damian Borth
    http://arxiv.org/abs/2006.10424v1
    • [cs.LG]Calibrated Reliable Regression using Maximum Mean Discrepancy
    Peng Cui, Wenbo Hu, Jun Zhu
    http://arxiv.org/abs/2006.10255v1
    • [cs.LG]Category-Specific CNN for Visual-aware CTR Prediction at JD.com
    Hu Liu, Jing Lu, Hao Yang, Xiwei Zhao, Sulong Xu, Hao Peng, Zehua Zhang, Wenjie Niu, Xiaokun Zhu, Yongjun Bao, Weipeng Yan
    http://arxiv.org/abs/2006.10337v1
    • [cs.LG]Class-Attentive Diffusion Network for Semi-Supervised Classification
    Jongin Lim, Daeho Um, Hyung Jin Chang, Dae Ung Jo, Jin Young Choi
    http://arxiv.org/abs/2006.10222v1
    • [cs.LG]Competitive Policy Optimization
    Manish Prajapat, Kamyar Azizzadenesheli, Alexander Liniger, Yisong Yue, Anima Anandkumar
    http://arxiv.org/abs/2006.10611v1
    • [cs.LG]Confident Off-Policy Evaluation and Selection through Self-Normalized Importance Weighting
    Ilja Kuzborskij, Claire Vernade, András György, Csaba Szepesvári
    http://arxiv.org/abs/2006.10460v1
    • [cs.LG]Constraining Variational Inference with Geometric Jensen-Shannon Divergence
    Jacob Deasy, Nikola Simidjievski, Pietro Liò
    http://arxiv.org/abs/2006.10599v1
    • [cs.LG]Constraint-Based Regularization of Neural Networks
    Benedict Leimkuhler, Timothée Pouchon, Tiffany Vlaar, Amos Storkey
    http://arxiv.org/abs/2006.10114v1
    • [cs.LG]DREAM: Deep Regret minimization with Advantage baselines and Model-free learning
    Eric Steinberger, Adam Lerer, Noam Brown
    http://arxiv.org/abs/2006.10410v1
    • [cs.LG]Deep Reinforcement Learning amidst Lifelong Non-Stationarity
    Annie Xie, James Harrison, Chelsea Finn
    http://arxiv.org/abs/2006.10701v1
    • [cs.LG]DisARM: An Antithetic Gradient Estimator for Binary Latent Variables
    Zhe Dong, Andriy Mnih, George Tucker
    http://arxiv.org/abs/2006.10680v1
    • [cs.LG]Distributed Value Function Approximation for Collaborative Multi-Agent Reinforcement Learning
    Milos S. Stankovic, Marko Beko, Srdjan S. Stankovic
    http://arxiv.org/abs/2006.10443v1
    • [cs.LG]DrNAS: Dirichlet Neural Architecture Search
    Xiangning Chen, Ruochen Wang, Minhao Cheng, Xiaocheng Tang, Cho-Jui Hsieh
    http://arxiv.org/abs/2006.10355v1
    • [cs.LG]Erdos Goes Neural: an Unsupervised Learning Framework for Combinatorial Optimization on Graphs
    Nikolaos Karalias, Andreas Loukas
    http://arxiv.org/abs/2006.10643v1
    • [cs.LG]Fair k-Means Clustering
    Mehrdad Ghadiri, Samira Samadi, Santosh Vempala
    http://arxiv.org/abs/2006.10085v1
    • [cs.LG]Flows Succeed Where GANs Fail: Lessons from Low-Dimensional Data
    Tianci Liu, Jeffrey Regier
    http://arxiv.org/abs/2006.10175v1
    • [cs.LG]Forward Prediction for Physical Reasoning
    Rohit Girdhar, Laura Gustafson, Aaron Adcock, Laurens van der Maaten
    http://arxiv.org/abs/2006.10734v1
    • [cs.LG]Fully Test-time Adaptation by Entropy Minimization
    Dequan Wang, Evan Shelhamer, Shaoteng Liu, Bruno Olshausen, Trevor Darrell
    http://arxiv.org/abs/2006.10726v1
    • [cs.LG]GAT-GMM: Generative Adversarial Training for Gaussian Mixture Models
    Farzan Farnia, William Wang, Subhro Das, Ali Jadbabaie
    http://arxiv.org/abs/2006.10293v1
    • [cs.LG]GCC: Graph Contrastive Coding for Graph Neural Network Pre-Training
    Jiezhong Qiu, Qibin Chen, Yuxiao Dong, Jing Zhang, Hongxia Yang, Ming Ding, Kuansan Wang, Jie Tang
    http://arxiv.org/abs/2006.09963v2
    • [cs.LG]Gradient Amplification: An efficient way to train deep neural networks
    Sunitha Basodi, Chunyan Ji, Haiping Zhang, Yi Pan
    http://arxiv.org/abs/2006.10560v1
    • [cs.LG]Housing Market Prediction Problem using Different Machine Learning Algorithms: A Case Study
    Shashi Bhushan Jha, Radu F. Babiceanu, Vijay Pandey, Rajesh Kumar Jha
    http://arxiv.org/abs/2006.10092v1
    • [cs.LG]I-BERT: Inductive Generalization of Transformer to Arbitrary Context Lengths
    Hyoungwook Nam, Seung Byum Seo, Vikram Sharma Malithody, Noor Michael, Lan Li
    http://arxiv.org/abs/2006.10220v1
    • [cs.LG]IReEn: Iterative Reverse-Engineering of Black-Box Functions via Neural Program Synthesis
    Hossein Hajipour, Mateusz Malinowski, Mario Fritz
    http://arxiv.org/abs/2006.10720v1
    • [cs.LG]Improvements in Computation and Usage of Joint CDFs for the N-Dimensional Order Statistic
    Arvind Thiagarajan
    http://arxiv.org/abs/2006.10124v1
    • [cs.LG]Kernel methods through the roof: handling billions of points efficiently
    Giacomo Meanti, Luigi Carratino, Lorenzo Rosasco, Alessandro Rudi
    http://arxiv.org/abs/2006.10350v1
    • [cs.LG]Learning Invariant Representations for Reinforcement Learning without Reconstruction
    Amy Zhang, Rowan McAllister, Roberto Calandra, Yarin Gal, Sergey Levine
    http://arxiv.org/abs/2006.10742v1
    • [cs.LG]Learning by Repetition: Stochastic Multi-armed Bandits under Priming Effect
    Priyank Agrawal, Theja Tulabandhula
    http://arxiv.org/abs/2006.10356v1
    • [cs.LG]Learning to Track Dynamic Targets in Partially Known Environments
    Heejin Jeong, Hamed Hassani, Manfred Morari, Daniel D. Lee, George J. Pappas
    http://arxiv.org/abs/2006.10190v1
    • [cs.LG]Leveraging Model Inherent Variable Importance for Stable Online Feature Selection
    Johannes Haug, Martin Pawelczyk, Klaus Broelemann, Gjergji Kasneci
    http://arxiv.org/abs/2006.10398v1
    • [cs.LG]Likelihood-Free Inference with Deep Gaussian Processes
    Alexander Aushev, Henri Pesonen, Markus Heinonen, Jukka Corander, Samuel Kaski
    http://arxiv.org/abs/2006.10571v1
    • [cs.LG]List-Decodable Mean Estimation via Iterative Multi-Fitering
    Ilias Diakonikolas, Daniel M. Kane, Daniel Kongsgaard
    http://arxiv.org/abs/2006.10715v1
    • [cs.LG]Local Competition and Uncertainty for Adversarial Robustness in Deep Learning
    Antonios Alexos, Konstantinos P. Panousis, Sotirios Chatzis
    http://arxiv.org/abs/2006.10620v1
    • [cs.LG]MMCGAN: Generative Adversarial Network with Explicit Manifold Prior
    Guanhua Zheng, Jitao Sang, Changsheng Xu
    http://arxiv.org/abs/2006.10331v1
    • [cs.LG]Markovian RNN: An Adaptive Time Series Prediction Network with HMM-based Switching for Nonstationary Environments
    Fatih Ilhan, Oguzhan Karaahmetoglu, Ismail Balaban, Suleyman Serdar Kozat
    http://arxiv.org/abs/2006.10119v1
    • [cs.LG]Minimax Lower Bounds for Transfer Learning with Linear and One-hidden Layer Neural Networks
    Seyed Mohammadreza Mousavi Kalan, Zalan Fabian, A. Salman Avestimehr, Mahdi Soltanolkotabi
    http://arxiv.org/abs/2006.10581v1
    • [cs.LG]Multi-Source Unsupervised Hyperparameter Optimization
    Masahiro Nomura, Yuta Saito
    http://arxiv.org/abs/2006.10600v1
    • [cs.LG]Nearly Optimal Robust Method for Convex Compositional Problems with Heavy-Tailed Noise
    Yan Yan, Xin Man, Tianbao Yang
    http://arxiv.org/abs/2006.10095v1
    • [cs.LG]Neural Architecture Optimization with Graph VAE
    Jian Li, Yong Liu, Jiankun Liu, Weiping Wang
    http://arxiv.org/abs/2006.10310v1
    • [cs.LG]OMBA: User-Guided Product Representations for Online Market Basket Analysis
    Amila Silva, Ling Luo, Shanika Karunasekera, Christopher Leckie
    http://arxiv.org/abs/2006.10396v1
    • [cs.LG]Offline detection of change-points in the mean for stationary graph signals
    Alejandro de la Concha, Nicolas Vayatis, Argyris Kalogeratos
    http://arxiv.org/abs/2006.10628v1
    • [cs.LG]On Sparsity in Overparametrised Shallow ReLU Networks
    Jaume de Dios, Joan Bruna
    http://arxiv.org/abs/2006.10225v1
    • [cs.LG]On the Predictability of Pruning Across Scales
    Jonathan S. Rosenfeld, Jonathan Frankle, Michael Carbin, Nir Shavit
    http://arxiv.org/abs/2006.10621v1
    • [cs.LG]On the Role of Sparsity and DAG Constraints for Learning Linear DAGs
    Ignavier Ng, AmirEmad Ghassami, Kun Zhang
    http://arxiv.org/abs/2006.10201v1
    • [cs.LG]Open Ad Hoc Teamwork using Graph-based Policy Learning
    Arrasy Rahman, Niklas Hopner, Filippos Christianos, Stefano V. Albrecht
    http://arxiv.org/abs/2006.10412v1
    • [cs.LG]Precise expressions for random proj
    7e3
    ections: Low-rank approximation and randomized Newton
    Michał Dereziński, Feynman Liang, Zhenyu Liao, Michael W. Mahoney
    http://arxiv.org/abs/2006.10653v1
    • [cs.LG]Quantifying Challenges in the Application of Graph Representation Learning
    Antonia Gogoglou, C. Bayan Bruss, Brian Nguyen, Reza Sarshogh, Keegan E. Hines
    http://arxiv.org/abs/2006.10252v1
    • [cs.LG]Record fusion: A learning approach
    Alireza Heidari, George Michalopoulos, Shrinu Kushagra, Ihab F. Ilyas, Theodoros Rekatsinas
    http://arxiv.org/abs/2006.10208v1
    • [cs.LG]Rethinking Semi-Supervised Learning in VAEs
    Tom Joy, Sebastian M. Schmon, Philip H. S. Torr, N. Siddharth, Tom Rainforth
    http://arxiv.org/abs/2006.10102v1
    • [cs.LG]Revisiting complexity and the bias-variance tradeoff
    Raaz Dwivedi, Chandan Singh, Bin Yu, Martin J. Wainwright
    http://arxiv.org/abs/2006.10189v1
    • [cs.LG]Robust Group Subspace Recovery: A New Approach for Multi-Modality Data Fusion
    Sally Ghanem, Ashkan Panahi, Hamid Krim, Ryan A. Kerekes
    http://arxiv.org/abs/2006.10657v1
    • [cs.LG]Robust Unsupervised Learning of Temporal Dynamic Interactions
    Aritra Guha, Rayleigh Lei, Jiacheng Zhu, XuanLong Nguyen, Ding Zhao
    http://arxiv.org/abs/2006.10241v1
    • [cs.LG]SE(3)-Transformers: 3D Roto-Translation Equivariant Attention Networks
    Fabian B. Fuchs, Daniel E. Worrall, Volker Fischer, Max Welling
    http://arxiv.org/abs/2006.10503v1
    • [cs.LG]STEER : Simple Temporal Regularization For Neural ODEs
    Arnab Ghosh, Harkirat Singh Behl, Emilien Dupont, Philip H. S. Torr, Vinay Namboodiri
    http://arxiv.org/abs/2006.10711v1
    • [cs.LG]SXL: Spatially explicit learning of geographic processes with auxiliary tasks
    Konstantin Klemmer, Daniel B. Neill
    http://arxiv.org/abs/2006.10461v1
    • [cs.LG]Self-supervised Learning on Graphs: Deep Insights and New Direction
    Wei Jin, Tyler Derr, Haochen Liu, Yiqi Wang, Suhang Wang, Zitao Liu, Jiliang Tang
    http://arxiv.org/abs/2006.10141v1
    • [cs.LG]Set Distribution Networks: a Generative Model for Sets of Images
    Shuangfei Zhai, Walter Talbott, Miguel Angel Bautista, Carlos Guestrin, Josh M. Susskind
    http://arxiv.org/abs/2006.10705v1
    • [cs.LG]Shapeshifter Networks: Cross-layer Parameter Sharing for Scalable and Effective Deep Learning
    Bryan A. Plummer, Nikoli Dryden, Julius Frost, Torsten Hoefler, Kate Saenko
    http://arxiv.org/abs/2006.10598v1
    • [cs.LG]Simple and Principled Uncertainty Estimation with Deterministic Deep Learning via Distance Awareness
    Jeremiah Zhe Liu, Zi Lin, Shreyas Padhy, Dustin Tran, Tania Bedrax-Weiss, Balaji Lakshminarayanan
    http://arxiv.org/abs/2006.10108v1
    • [cs.LG]Smoothed Analysis of Online and Differentially Private Learning
    Nika Haghtalab, Tim Roughgarden, Abhishek Shetty
    http://arxiv.org/abs/2006.10129v1
    • [cs.LG]Sparse Bottleneck Networks for Exploratory Analysis and Visualization of Neural Patch-seq Data
    Yves Bernaerts, Philipp Berens, Dmitry Kobak
    http://arxiv.org/abs/2006.10411v1
    • [cs.LG]Stochastic Bandits with Linear Constraints
    Aldo Pacchiano, Mohammad Ghavamzadeh, Peter Bartlett, Heinrich Jiang
    http://arxiv.org/abs/2006.10185v1
    • [cs.LG]Subgraph Neural Networks
    Emily Alsentzer, Samuel G. Finlayson, Michelle M. Li, Marinka Zitnik
    http://arxiv.org/abs/2006.10538v1
    • [cs.LG]Temporal Graph Networks for Deep Learning on Dynamic Graphs
    Emanuele Rossi, Ben Chamberlain, Fabrizio Frasca, Davide Eynard, Federico Monti, Michael Bronstein
    http://arxiv.org/abs/2006.10637v1
    • [cs.LG]Tensor Decompositions in Recursive NeuralNetworks for Tree-Structured Data
    Daniele Castellana, Davide Bacciu
    http://arxiv.org/abs/2006.10619v1
    • [cs.LG]The Clever Hans Effect in Anomaly Detection
    Jacob Kauffmann, Lukas Ruff, Grégoire Montavon, Klaus-Robert Müller
    http://arxiv.org/abs/2006.10609v1
    • [cs.LG]The Recurrent Neural Tangent Kernel
    Sina Alemohammad, Zichao Wang, Randall Balestriero, Richard Baraniuk
    http://arxiv.org/abs/2006.10246v1
    • [cs.LG]Towards Recurrent Autoregressive Flow Models
    John Mern, Peter Morales, Mykel J. Kochenderfer
    http://arxiv.org/abs/2006.10096v1
    • [cs.LG]Towards Threshold Invariant Fair Classification
    Mingliang Chen, Min Wu
    http://arxiv.org/abs/2006.10667v1
    • [cs.LG]Uncertainty in Gradient Boosting via Ensembles
    Aleksei Ustimenko, Liudmila Prokhorenkova, Andrey Malinin
    http://arxiv.org/abs/2006.10562v1
    • [cs.LG]Unsupervised Meta-Learning through Latent-Space Interpolation in Generative Models
    Siavash Khodadadeh, Sharare Zehtabian, Saeed Vahidian, Weijia Wang, Bill Lin, Ladislau Bölöni
    http://arxiv.org/abs/2006.10236v1
    • [cs.LG]Zero-Shot Learning with Common Sense Knowledge Graphs
    Nihal V. Nayak, Stephen H. Bach
    http://arxiv.org/abs/2006.10713v1
    • [cs.NE]A Shooting Formulation of Deep Learning
    François-Xavier Vialard, Roland Kwitt, Susan Wei, Marc Niethammer
    http://arxiv.org/abs/2006.10330v1
    • [cs.RO]A Mechanical Screwing Tool for 2-Finger Parallel Grippers — Design, Optimization, and Manipulation Policies
    Zhengtao Hu, Weiwei Wan, Keisuke Koyama, Kensuke Harada
    http://arxiv.org/abs/2006.10366v1
    • [cs.RO]PIDA: Smooth and Stable Flight Using Stochastic Dual Simplex Algorithm and Genetic Filter
    Seid Miad Zandavi, Vera Chung, Ali Anaissi
    http://arxiv.org/abs/2006.10522v1
    • [cs.SD]Artificial Musical Intelligence: A Survey
    Elad Liebman, Peter Stone
    http://arxiv.org/abs/2006.10553v1
    • [cs.SE]Robotics Software Engineering: A Perspective from the Service Robotics Domain
    Sergio García, Daniel Strüber, Davide Brugali, Thorsten Berger, Patrizio Pelliccione
    http://arxiv.org/abs/2006.10608v1
    • [cs.SI]A Multi-View Approach Based on Naming Behavioral Modeling for Aligning Chinese User Accounts across Multiple Networks
    Junxing Zhu, Xiang Wang, Qiang Liu, Xiaoyong Li, Chengcheng Shao, Bin Zhou
    http://arxiv.org/abs/2006.10633v1
    • [cs.SI]A unified framework for equivalences in social networks
    Nina Otter, Mason A. Porter
    http://arxiv.org/abs/2006.10733v1
    • [cs.SI]Catching them red-handed: Real-time Aggression Detection on Social Media
    Herodotos Herodotou, Despoina Chatzakou, Nicolas Kourtellis
    http://arxiv.org/abs/2006.10104v1
    • [cs.SI]Market Graph Clustering Via QUBO and Digital Annealing
    Seo Hong, Pierre Miasnikof, Roy Kwon, Yuri Lawryshyn
    http://arxiv.org/abs/2006.10716v1
    • [cs.SI]Using Sentiment Information for Preemptive Detection of Toxic Comments in Online Conversations
    Éloi Brassard-Gourdeau, Richard Khoury
    http://arxiv.org/abs/2006.10145v1
    • [econ.EM]Approximate Maximum Likelihood for Complex Structural Models
    Veronika Czellar, David T. Frazier, Eric Renault
    http://arxiv.org/abs/2006.10245v1
    • [econ.EM]Sparse HP Filter: Finding Kinks in the COVID-19 Contact Rate
    Sokbae Lee, Yuan Liao, Myung Hwan Seo, Youngki Shin
    http://arxiv.org/abs/2006.10555v1
    • [eess.AS]Are you wearing a mask? Improving mask detection from speech using augmentation by cycle-consistent GANs
    Nicolae-Cătălin Ristea, Radu Tudor Ionescu
    http://arxiv.org/abs/2006.10147v1
    • [eess.IV]ChestX-det10: Chest X-ray Dataset on Detection of Thoracic Abnormalities
    Jingyu Liu, Jie Lian, Yizhou Yu
    http://arxiv.org/abs/2006.10550v1
    • [eess.IV]Generating Fundus Fluorescence Angiography Images from Structure Fundus Images Using Generative Adversarial Networks
    Wanyue Li, Wen Kong, Yiwei Chen, Jing Wang, Yi He, Guohua Shi, Guohua Deng
    http://arxiv.org/abs/2006.10216v1
    • [eess.IV]XRayGAN: Consistency-preserving Generation of X-ray Images from Radiology Reports
    Xingyi Yang, Nandiraju Gireesh, Eric Xing, Pengtao Xie
    http://arxiv.org/abs/2006.10552v1
    • [eess.SP]A Review of 1D Convolutional Neural Networks toward Unknown Substance Identification in Portable Raman Spectrometer
    M. Hamed Mozaffari, Li-Lin Tay
    http://arxiv.org/abs/2006.10575v1
    • [eess.SP]Cell-Free Massive MIMO with Nonorthogonal Pilots for Internet of Things
    Shilpa Rao, Alexei Ashikhmin, Hong Yang
    http://arxiv.org/abs/2006.10363v1
    • [eess.SP]Hybrid Beamforming Structure for Massive MIMO System: Full-connection v.s. Partial-connection
    Zheda Li, Shengqian Han, Andreas F. Molisch
    http://arxiv.org/abs/2006.10044v1
    • [eess.SP]Structured Massive Access for Scalable Cell-Free Massive MIMO Systems
    Shuaifei Chen, Jiayi Zhang, Emil Björnson, Jing Zhang, Bo Ai
    http://arxiv.org/abs/2006.10275v1
    • [math.OC]Apollonius Allocation Algorithm for Heterogeneous Pursuers to Capture Multiple Evaders
    Venkata Ramana Makkapati, Panagiotis Tsiotras
    http://arxiv.org/abs/2006.10253v1
    • [math.OC]SGD for Structured Nonconvex Functions: Learning Rates, Minibatching and Interpolation
    Robert M. Gower, Othmane Sebbouh, Nicolas Loizou
    http://arxiv.org/abs/2006.10311v1
    • [math.PR]Free Energy Wells and Overlap Gap Property in Sparse PCA
    Gérard Ben Arous, Alexander S. Wein, Ilias Zadik
    http://arxiv.org/abs/2006.10689v1
    • [math.ST]Inference for local parameters in convexity constrained models
    Hang Deng, Qiyang Han, Bodhisattva Sen
    http://arxiv.org/abs/2006.10264v1
    • [math.ST]Right-truncated Archimedean and related copulas
    Marius Hofert
    http://arxiv.org/abs/2006.10107v1
    • [physics.acc-ph]Computing techniques
    X. Buffat
    http://arxiv.org/abs/2006.10664v1
    • [physics.app-ph]A flexible spiraling-metasurface as a versatile haptic interface
    Osama R. Bilal, Vincenzo Costanza, Ali Israr, Antonio Palermo, Paolo Celli, Frances Lau, Chiara Daraio
    http://arxiv.org/abs/2006.10717v1
    • [q-bio.NC]A Representational Model of Grid Cells Based on Matrix Lie Algebras
    Ruiqi Gao, Jianwen Xie, Song-Chun Zhu, Ying Nian Wu
    http://arxiv.org/abs/2006.10259v1
    • [q-bio.PE]Analysis of Virus Propagation: A Transition Model Representation of Stochastic Epidemiological Models
    Christian Gourieroux, Joann Jasiak
    http://arxiv.org/abs/2006.10265v1
    • [stat.AP]CytOpT: Optimal Transport with Domain Adaptation for Interpreting Flow Cytometry data
    Paul Freulon, Jérémie Bigot, Boris P. Hejblum
    http://arxiv.org/abs/2006.09003v3
    • [stat.AP]Homogeneity Test for Functional Data basedon Data-Depth Plots
    Alejandro Calle-Saldarriaga, Henry Laniado, Francisco Zuluaga
    http://arxiv.org/abs/2006.10646v1
    • [stat.AP]Small Area Estimation of Health Outcomes
    Jon Wakefield, Taylor Okonek, Jon Pedersen
    http://arxiv.org/abs/2006.10266v1
    • [stat.AP]The Essential Role of Empirical Validation in Legislative Redistricting Simulation
    Benjamin Fifield, Kosuke Imai, Jun Kawahara, Christopher T. Kenny
    http://arxiv.org/abs/2006.10148v1
    • [stat.AP]Uncertainty quantification for epidemiological forecasts of COVID-19 through combinations of model predictions
    V. E. Bowman, D. S. Silk, U. Dalrymple, D. C. Woods
    http://arxiv.org/abs/2006.10714v1
    • [stat.ME]Asymptotic distribution-free change-point detection for data with repeated observations
    Hoseung Song, Hao Chen
    http://arxiv.org/abs/2006.10305v1
    • [stat.ME]Bayesian Changepoint Analysis
    Tobias Siems
    http://arxiv.org/abs/2006.10428v1
    • [stat.ME]Bayesian Elastic Net based on Empirical Likelihood
    Chul Moon, Adel Bedoui
    http://arxiv.org/abs/2006.10258v1
    • [stat.ME]Fast Tail Index Estimation for Power Law Distributions in R
    Ranjiva Munasinghe, Pathum Kossinna, Dovini Jayasinghe, Dilanka Wijeratne
    http://arxiv.org/abs/2006.10308v1
    • [stat.ME]Functional Group Bridge for Simultaneous Regression and Support Estimation
    Zhengjia Wang, John Magnotti, Michael S. Beauchamp, Meng Li
    http://arxiv.org/abs/2006.10163v1
    • [stat.ME]Transfer Learning for High-dimensional Linear Regression: Prediction, Estimation, and Minimax Optimality
    Sai Li, T. Tony Cai, Hongzhe Li
    http://arxiv.org/abs/2006.10593v1
    • [stat.ME]Using Weighted P-Values in Fisher’s Method
    Arvind Thiagarajan
    http://arxiv.org/abs/2006.10126v1
    • [stat.ML]A Framework for Sample Efficient Interval Estimation with Control Variates
    Shengjia Zhao, Christopher Yeh, Stefano Ermon
    http://arxiv.org/abs/2006.10287v1
    • [stat.ML]Active Learning for Nonlinear System Identification with Guarantees
    Horia Mania, Michael I. Jordan, Benjamin Recht
    http://arxiv.org/abs/2006.10277v1
    • [stat.ML]Distribution-free binary classification: prediction sets, confidence intervals and calibration
    Chirag Gupta, Aleksandr Podkopaev, Aaditya Ramdas
    http://arxiv.org/abs/2006.10564v1
    • [stat.ML]Exact posterior distributions of wide Bayesian neural networks
    Jiri Hron, Yasaman Bahri, Roman Novak, Jeffrey Pennington, Jascha Sohl-Dickstein
    http://arxiv.org/abs/2006.10541v1
    • [stat.ML]Guarantees for Hierarchical Clustering by the Sublevel Set method
    Marina Meila
    http://arxiv.org/abs/2006.10274v1
    • [stat.ML]Individual Calibration with Randomized Forecasting
    Shengjia Zhao, Tengyu Ma, Stefano Ermon
    http://arxiv.org/abs/2006.10288v1
    • [stat.ML]Infinite attention: NNGP and NTK for deep attention networks
    Jiri Hron, Yasaman Bahri, Jascha Sohl-Dickstein, Roman Novak
    http://arxiv.org/abs/2006.10540v1
    • [stat.ML]Low-Rank Autoregressive Tensor Completion for Multivariate Time Series Forecasting
    Xinyu Chen, Lijun Sun
    http://arxiv.org/abs/2006.10436v1
    • [stat.ML]Matern Gaussian processes on Riemannian manifolds
    Viacheslav Borovitskiy, Alexander Terenin, Peter Mostowsky, Marc Peter Deisenroth
    http://arxiv.org/abs/2006.10160v1
    • [stat.ML]Median Matrix Completion: from Embarrassment to Optimality
    Weidong Liu, Xiaojun Mao, Raymond K. W. Wong
    http://arxiv.org/abs/2006.10400v1
    • [stat.ML]MoFlow: An Invertible Flow Model for Generating Molecular Graphs
    Chengxi Zang, Fei Wang
    http://arxiv.org/abs/2006.10137v1
    • [stat.ML]Neural Manifold Ordinary Differential Equations
    Aaron Lou, Derek Lim, Isay Katsman, Leo Huang, Qingxuan Jiang, Ser-Nam Lim, Christopher De Sa
    http://arxiv.org/abs/2006.10254v1
    • [stat.ML]Riemannian Continuous Normalizing Flows
    Emile Mathieu, Maximilian Nickel
    http://arxiv.org/abs/2006.10605v1
    • [stat.ML]Robust compressed sensing of generative models
    Ajil Jalal, Liu Liu, Alexandros G. Dimakis, Constantine Caramanis
    http://arxiv.org/abs/2006.09461v2
    • [stat.ML]Stochastic bandits with arm-dependent delays
    Anne Gael Manegueu, Claire Vernade, Alexandra Carpentier, Michal Valko
    http://arxiv.org/abs/2006.10459v1
    • [stat.ML]Variational Autoencoder with Learned Latent Structure
    Marissa C. Connor, Gregory H. Canal, Christopher J. Rozell
    http://arxiv.org/abs/2006.10597v1
    • [stat.ML]Variational State-Space Models for Localisation and Dense 3D Mapping in 6 DoF
    Atanas Mirchev, Baris Kayalibay, Patrick van der Smagt, Justin Bayer
    http://arxiv.org/abs/2006.10178v1
    • [stat.ML]What Do Neural Networks Learn When Trained With Random Labels?
    Hartmut Maennel, Ibrahim Alabdulmohsin, Ilya Tolstikhin, Robert J. N. Baldock, Olivier Bousquet, Sylvain Gelly, Daniel Keysers
    http://arxiv.org/abs/2006.10455v1
    • [stat.ML]When Does Preconditioning Help or Hurt Generalization?
    Shun-ichi Amari, Jimmy Ba, Roger Grosse, Xuechen Li, Atsushi Nitanda, Taiji Suzuki, Denny Wu, Ji Xu
    http://arxiv.org/abs/2006.10732v1
    • [stat.ML]When OT meets MoM: Robust estimation of Wasserstein Distance
    Guillaume Staerman, Pierre Laforgue, Pavlo Mozharovskyi, Florence d’Alché-Buc
    http://arxiv.org/abs/2006.10325v1