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.GT - 计算机科学与博弈论
    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.SI - 社交网络与信息网络
    eess.AS - 语音处理
    eess.IV - 图像与视频处理
    eess.SP - 信号处理
    math.OC - 优化与控制
    math.PR - 概率
    math.ST - 统计理论
    physics.soc-ph - 物理学与社会
    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]Classical Planning as QBF without Grounding (extended version)
    • [cs.CL]A Neural Edge-Editing Approach for Document-Level Relation Graph Extraction
    • [cs.CL]An Information Retrieval Approach to Building Datasets for Hate Speech Detection
    • [cs.CL]Bad Characters: Imperceptible NLP Attacks
    • [cs.CL]Challenges and Limitations with the Metrics Measuring the Complexity of Code-Mixed Text
    • [cs.CL]Continuity of Topic, Interaction, and Query: Learning to Quote in Online Conversations
    • [cs.CL]Enhancing user creativity: Semantic measures for idea generation
    • [cs.CL]GEM: A General Evaluation Benchmark for Multimodal Tasks
    • [cs.CL]Graph-based Joint Pandemic Concern and Relation Extraction on Twitter
    • [cs.CL]LNN-EL: A Neuro-Symbolic Approach to Short-text Entity Linking
    • [cs.CL]Label Mask for Multi-Label Text Classification
    • [cs.CL]Multi-Task Learning and Adapted Knowledge Models for Emotion-Cause Extraction
    • [cs.CL]PRGC: Potential Relation and Global Correspondence Based Joint Relational Triple Extraction
    • [cs.CL]Recurrent Stacking of Layers in Neural Networks: An Application to Neural Machine Translation
    • [cs.CL]SPBERT: Pre-training BERT on SPARQL Queries for End-to-end Question Answering over Knowledge Graphs
    • [cs.CL]Subjective Bias in Abstractive Summarization
    • [cs.CL]Towards Financial Sentiment Analysis in a South African Landscape
    • [cs.CL]Weakly Supervised Pre-Training for Multi-Hop Retriever
    • [cs.CR]Evaluating the Robustness of Trigger Set-Based Watermarks Embedded in Deep Neural Networks
    • [cs.CV]A Coarse-to-Fine Instance Segmentation Network with Learning Boundary Representation
    • [cs.CV]A Dynamic Spatial-temporal Attention Network for Early Anticipation of Traffic Accidents
    • [cs.CV]A Framework for Real-time Traffic Trajectory Tracking, Speed Estimation, and Driver Behavior Calibration at Urban Intersections Using Virtual Traffic Lanes
    • [cs.CV]Advanced Hough-based method for on-device document localization
    • [cs.CV]All You Can Embed: Natural Language based Vehicle Retrieval with Spatio-Temporal Transformers
    • [cs.CV]Bridging the Gap Between Object Detection and User Intent via Query-Modulation
    • [cs.CV]Combined Person Classification with Airborne Optical Sectioning
    • [cs.CV]Contrastive Learning of Generalized Game Representations
    • [cs.CV]Discerning Generic Event Boundaries in Long-Form Wild Videos
    • [cs.CV]Dual-Teacher Class-Incremental Learning With Data-Free Generative Replay
    • [cs.CV]EPIC-KITCHENS-100 Unsupervised Domain Adaptation Challenge for Action Recognition 2021: Team M3EM Technical Report
    • [cs.CV]Effective Model Sparsification by Scheduled Grow-and-Prune Methods
    • [cs.CV]End-to-end Temporal Action Detection with Transformer
    • [cs.CV]Equivariance-bridged SO(2)-Invariant Representation Learning using Graph Convolutional Network
    • [cs.CV]HifiFace: 3D Shape and Semantic Prior Guided High Fidelity Face Swapping
    • [cs.CV]How to train your ViT? Data, Augmentation, and Regularization in Vision Transformers
    • [cs.CV]Learning and Meshing from Deep Implicit Surface Networks Using an Efficient Implementation of Analytic Marching
    • [cs.CV]Light Lies: Optical Adversarial Attack
    • [cs.CV]Light Pollution Reduction in Nighttime Photography
    • [cs.CV]Medical Matting: A New Perspective on Medical Segmentation with Uncertainty
    • [cs.CV]Multi-Granularity Network with Modal Attention for Dense Affective Understanding
    • [cs.CV]Novelty Detection via Contrastive Learning with Negative Data Augmentation
    • [cs.CV]Quantized Neural Networks via {-1, +1} Encoding Decomposition and Acceleration
    • [cs.CV]Residual Contrastive Learning for Joint Demosaicking and Denoising
    • [cs.CV]Self-supervised Video Representation Learning with Cross-Stream Prototypical Contrasting
    • [cs.CV]Shape Prior Non-Uniform Sampling Guided Real-time Stereo 3D Object Detection
    • [cs.CV]Smoothed Multi-View Subspace Clustering
    • [cs.CV]Toward Fault Detection in Industrial Welding Processes with Deep Learning and Data Augmentation
    • [cs.CV]Towards Clustering-friendly Representations: Subspace Clustering via Graph Filtering
    • [cs.CV]Towards Distraction-Robust Active Visual Tracking
    • [cs.CV]Towards interpreting computer vision based on transformation invariant optimization
    • [cs.CV]Training or Architecture? How to Incorporate Invariance in Neural Networks
    • [cs.CV]VSAC: Efficient and Accurate Estimator for H and F
    • [cs.CV]Virtual Temporal Samples for Recurrent Neural Networks: applied to semantic segmentation in agriculture
    • [cs.CV]hSMAL: Detailed Horse Shape and Pose Reconstruction for Motion Pattern Recognition
    • [cs.CY]A Fait Accompli? An Empirical Study into the Absence of Consent to Third-Party Tracking in Android Apps
    • [cs.CY]Data Enforced: An Exploratory Impact Analysis of Automated Speed Enforcement in the District of Columbia
    • [cs.CY]Detox Browser — Towards Filtering Sensitive Content On the Web
    • [cs.CY]How COVID-19 Have Changed Crowdfunding: Evidence From GoFundMe
    • [cs.GT]Equilibrium Design for Concurrent Games
    • [cs.IR]Heuristic Stopping Rules For Technology-Assisted Review
    • [cs.IR]On Minimizing Cost in Legal Document Review Workflows
    • [cs.IR]Point-of-Interest Recommender Systems: A Survey from an Experimental Perspective
    • [cs.IT]Degree Tables for Secure Distributed Matrix Multiplication
    • [cs.IT]Determining when a truncated generalised Reed-Solomon code is Hermitian self-orthogonal
    • [cs.IT]Is Channel Estimation Necessary to Select Phase-Shifts for RIS-Assisted Massive MIMO?
    • [cs.IT]Performance Analysis of Synergetic UAV-RIS Communication Networks
    • [cs.LG]A Note on Optimizing Distributions using Kernel Mean Embeddings
    • [cs.LG]A Probabilistic Representation of DNNs: Bridging Mutual Information and Generalization
    • [cs.LG]A Unified Generative Adversarial Network Training via Self-Labeling and Self-Attention
    • [cs.LG]A Vertical Federated Learning Framework for Horizontally Partitioned Labels
    • [cs.LG]Accumulative Poisoning Attacks on Real-time Data
    • [cs.LG]Active Offline Policy Selection
    • [cs.LG]Adversarial Training Helps Transfer Learning via Better Representations
    • [cs.LG]An Empirical Investigation into Deep and Shallow Rule Learning
    • [cs.LG]An Investigation into Mini-Batch Rule Learning
    • [cs.LG]Batch Multi-Fidelity Bayesian Optimization with Deep Auto-Regressive Networks
    • [cs.LG]Being Properly Improper
    • [cs.LG]Being a Bit Frequentist Improves Bayesian Neural Networks
    • [cs.LG]BinarizedAttack: Structural Poisoning Attacks to Graph-based Anomaly Detection
    • [cs.LG]BitFit: Simple Parameter-efficient Fine-tuning for Transformer-based Masked Language-models
    • [cs.LG]Boolean Matrix Factorization with SAT and MaxSAT
    • [cs.LG]Combining Pseudo-Point and State Space Approximations for Sum-Separable Gaussian Processes
    • [cs.LG]Deep Reinforcement Learning Models Predict Visual Responses in the Brain: A Preliminary Result
    • [cs.LG]Distributed Deep Learning in Open Collaborations
    • [cs.LG]Evolving GANs: When Contradictions Turn into Compliance
    • [cs.LG]Federated Robustness Propagation: Sharing Adversarial Robustness in Federated Learning
    • [cs.LG]FinGAT: Financial Graph Attention Networks for Recommending Top-K Profitable Stocks
    • [cs.LG]Fusion of Embeddings Networks for Robust Combination of Text Dependent and Independent Speaker Recognition
    • [cs.LG]Goal-Directed Planning by Reinforcement Learning and Active Inference
    • [cs.LG]Graph Context Encoder: Graph Feature Inpainting for Graph Generation and Self-supervised Pretraining
    • [cs.LG]Indicators of Attack Failure: Debugging and Improving Optimization of Adversarial Examples
    • [cs.LG]Investigating the Role of Negatives in Contrastive Representation Learning
    • [cs.LG]It’s FLAN time! Summing feature-wise latent representations for interpretability
    • [cs.LG]Iterative Feature Matching: Toward Provable Domain Generalization with Logarithmic Environments
    • [cs.LG]Labelling Drifts in a Fault Detection System for Wind Turbine Maintenance
    • [cs.LG]Learning to Generate Code Sketches
    • [cs.LG]Learning to Plan via a Multi-Step Policy Regression Method
    • [cs.LG]Less is More: Feature Selection for Adversarial Robustness with Compressive Counter-Adversarial Attacks
    • [cs.LG]Local AdaGrad-Type Algorithm for Stochastic Convex-Concave Minimax Problems
    • [cs.LG]Locally Differentially Private Federated Learning: Efficient Algorithms with Tight Risk Bounds
    • [cs.LG]MADE: Exploration via Maximizing Deviation from Explored Regions
    • [cs.LG]Machining Cycle Time Prediction: Data-driven Modelling of Machine Tool Feedrate Behavior with Neural Networks
    • [cs.LG]Message Passing in Graph Convolution Networks via Adaptive Filter Banks
    • [cs.LG]NoiseGrad: enhancing explanations by introducing stochasticity to model weights
    • [cs.LG]Nonparametric Hamiltonian Monte Carlo
    • [cs.LG]On Invariance Penalties for Risk Minimization
    • [cs.LG]On the Connections between Counterfactual Explanations and Adversarial Examples
    • [cs.LG]On the Sample Complexity of Batch Reinforcement Learning with Policy-Induced Data
    • [cs.LG]PAC Prediction Sets Under Covariate Shift
    • [cs.LG]Predicting gender of Brazilian names using deep learning
    • [cs.LG]PyKale: Knowledge-Aware Machine Learning from Multiple Sources in Python
    • [cs.LG]QuantumFed: A Federated Learning Framework for Collaborative Quantum Training
    • [cs.LG]Rational Shapley Values
    • [cs.LG]Residual Error: a New Performance Measure for Adversarial Robustness
    • [cs.LG]Riemannian Convex Potential Maps
    • [cs.LG]ScoreGrad: Multivariate Probabilistic Time Series Forecasting with Continuous Energy-based Generative Models
    • [cs.LG]Self-supervised Incremental Deep Graph Learning for Ethereum Phishing Scam Detection
    • [cs.LG]Steerable Partial Differential Operators for Equivariant Neural Networks
    • [cs.LG]The Dimpled Manifold Model of Adversarial Examples in Machine Learning
    • [cs.LG]The Principles of Deep Learning Theory
    • [cs.LG]World-GAN: a Generative Model for Minecraft Worlds
    • [cs.LG]Zero-Shot Federated Learning with New Classes for Audio Classification
    • [cs.LG]pyWATTS: Python Workflow Automation Tool for Time Series
    • [cs.NE]A Fresh Approach to Evaluate Performance in Distributed Parallel Genetic Algorithms
    • [cs.RO]Development of a conversing and body temperature scanning autonomously navigating robot to help screen for COVID-19
    • [cs.RO]Human-Aware Navigation Planner for Diverse Human-Robot Contexts
    • [cs.RO]Improved Radar Localization on Lidar Maps Using Shared Embedding
    • [cs.RO]Optimizing robotic swarm based construction tasks
    • [cs.RO]Position-based Dynamics Simulator of Brain Deformations for Path Planning and Intra-Operative Control in Keyhole Neurosurgery
    • [cs.RO]Semantic navigation with domain knowledge
    • [cs.RO]Towards Robotic Laboratory Automation Plug & Play: The “LAPP” Framework
    • [cs.RO]Under the Sand: Navigation and Localization of a Small Unmanned Aerial Vehicle for Landmine Detection with Ground Penetrating Synthetic Aperture Radar
    • [cs.RO]Variable-Grasping-Mode Gripper With Different Finger Structures For Grasping Small-Sized Items
    • [cs.SD]Synchronising speech segments with musical beats in Mandarin and English singing
    • [cs.SI]Centrality Measures in Interval-Weighted Networks
    • [cs.SI]Community Detection in Interval-Weighted Networks
    • [cs.SI]Embedding Heterogeneous Networks into Hyperbolic Space Without Meta-path
    • [cs.SI]Meta-control of social learning strategies
    • [eess.AS]Low Resource German ASR with Untranscribed Data Spoken by Non-native Children — INTERSPEECH 2021 Shared Task SPAPL System
    • [eess.AS]Multi-mode Transformer Transducer with Stochastic Future Context
    • [eess.AS]On-Device Personalization of Automatic Speech Recognition Models for Disordered Speech
    • [eess.IV]Debiased Subjective Assessment of Real-World Image Enhancement
    • [eess.IV]Non-Iterative Phase Retrieval With Cascaded Neural Networks
    • [eess.SP]ICINet: ICI-Aware Neural Network Based Channel Estimation for Rapidly Time-Varying OFDM Systems
    • [math.OC]Distributed optimal power flow
    • [math.OC]Escaping strict saddle points of the Moreau envelope in nonsmooth optimization
    • [math.PR]Sharp Lower and Upper Bounds for the Covariance of Bounded Random Variables
    • [math.ST]CLT for LSS of sample covariance matrices with unbounded dispersions
    • [math.ST]Entrywise limit theorems of eigenvectors and their one-step refinement for sparse random graphs
    • [math.ST]Generalized regression operator estimation for continuous time functional data processes with missing at random response
    • [math.ST]Local asymptotics of cross-validation in least-squares density estimation
    • [physics.soc-ph]Systematic comparison of graph embedding methods in practical tasks
    • [stat.AP]SAGE: Stealthy Attack GEneration for Cyber-Physical Systems
    • [stat.AP]Sparse Linear Spectral Unmixing of Hyperspectral images using Expectation-Propagation
    • [stat.AP]Trend estimation and short-term forecasting of COVID-19 cases and deaths worldwide
    • [stat.CO]Deterministic Gibbs Sampling via Ordinary Differential Equations
    • [stat.ME]Assessing an Alternative for `Negative Variance Components’: A Gentle Introduction to Bayesian Covariance Structure Modelling for Negative Associations Among Patients with Personalized Treatments
    • [stat.ME]Bayesian Cox Regression for Population-scale Inference in Electronic Health Records
    • [stat.ME]Causal Bias Quantification for Continuous Treatment
    • [stat.ME]Distributionally Weighted Least Squares in Structural Equation Modeling
    • [stat.ME]Generalized Linear Randomized Response Modeling using GLMMRR
    • [stat.ME]LNIRT: An R Package for Joint Modeling of Response Accuracy and Times
    • [stat.ME]Robust nonparametric hypothesis tests for differences in the covariance structure of functional data
    • [stat.ML]An Analysis of the Deployment of Models Trained on Private Tabular Synthetic Data: Unexpected Surprises
    • [stat.ML]Fitting summary statistics of neural data with a differentiable spiking network simulator
    • [stat.ML]On Contrastive Representations of Stochastic Processes
    • [stat.ML]Problem Dependent View on Structured Thresholding Bandit Problems
    • [stat.ML]Wide stochastic networks: Gaussian limit and PAC-Bayesian training

    ·····································

    • [cs.AI]Classical Planning as QBF without Grounding (extended version)
    Irfansha Shaik, Jaco van de Pol
    http://arxiv.org/abs/2106.10138v1

    • [cs.CL]A Neural Edge-Editing Approach for Document-Level Relation Graph Extraction
    Kohei Makino, Makoto Miwa, Yutaka Sasaki
    http://arxiv.org/abs/2106.09900v1

    • [cs.CL]An Information Retrieval Approach to Building Datasets for Hate Speech Detection
    Md Mustafizur Rahman, Dinesh Balakrishnan, Dhiraj Murthy, Mucahid Kutlu, Matthew Lease
    http://arxiv.org/abs/2106.09775v1

    • [cs.CL]Bad Characters: Imperceptible NLP Attacks
    Nicholas Boucher, Ilia Shumailov, Ross Anderson, Nicolas Papernot
    http://arxiv.org/abs/2106.09898v1

    • [cs.CL]Challenges and Limitations with the Metrics Measuring the Complexity of Code-Mixed Text
    Vivek Srivastava, Mayank Singh
    http://arxiv.org/abs/2106.10123v1

    • [cs.CL]Continuity of Topic, Interaction, and Query: Learning to Quote in Online Conversations
    Lingzhi Wang, Jing Li, Xingshan Zeng, Haisong Zhang, Kam-Fai Wong
    http://arxiv.org/abs/2106.09896v1

    • [cs.CL]Enhancing user creativity: Semantic measures for idea generation
    Georgi V. Georgiev, Danko D. Georgiev
    http://arxiv.org/abs/2106.10131v1

    • [cs.CL]GEM: A General Evaluation Benchmark for Multimodal Tasks
    Lin Su, Nan Duan, Edward Cui, Lei Ji, Chenfei Wu, Huaishao Luo, Yongfei Liu, Ming Zhong, Taroon Bharti, Arun Sacheti
    http://arxiv.org/abs/2106.09889v1

    • [cs.CL]Graph-based Joint Pandemic Concern and Relation Extraction on Twitter
    Jingli Shi, Weihua Li, Sira Yongchareon, Yi Yang, Quan Bai
    http://arxiv.org/abs/2106.09929v1

    • [cs.CL]LNN-EL: A Neuro-Symbolic Approach to Short-text Entity Linking
    Hang Jiang, Sairam Gurajada, Qiuhao Lu, Sumit Neelam, Lucian Popa, Prithviraj Sen, Yunyao Li, Alexander Gray
    http://arxiv.org/abs/2106.09795v1

    • [cs.CL]Label Mask for Multi-Label Text Classification
    Rui Song, Xingbing Chen, Zelong Liu, Haining An, Zhiqi Zhang, Xiaoguang Wang, Hao Xu
    http://arxiv.org/abs/2106.10076v1

    • [cs.CL]Multi-Task Learning and Adapted Knowledge Models for Emotion-Cause Extraction
    Elsbeth Turcan, Shuai Wang, Rishita Anubhai, Kasturi Bhattacharjee, Yaser Al-Onaizan, Smaranda Muresan
    http://arxiv.org/abs/2106.09790v1

    • [cs.CL]PRGC: Potential Relation and Global Correspondence Based Joint Relational Triple Extraction
    Hengyi Zheng, Rui Wen, Xi Chen, Yifan Yang, Yunyan Zhang, Ziheng Zhang, Ningyu Zhang, Bin Qin, Ming Xu, Yefeng Zheng
    http://arxiv.org/abs/2106.09895v1

    • [cs.CL]Recurrent Stacking of Layers in Neural Networks: An Application to Neural Machine Translation
    Raj Dabre, Atsushi Fujita
    http://arxiv.org/abs/2106.10002v1

    • [cs.CL]SPBERT: Pre-training BERT on SPARQL Queries for End-to-end Question Answering over Knowledge Graphs
    Hieu Tran, Long Phan, Truong-Son Nguyen
    http://arxiv.org/abs/2106.09997v1

    • [cs.CL]Subjective Bias in Abstractive Summarization
    Lei Li, Wei Liu, Marina Litvak, Natalia Vanetik, Jiacheng Pei, Yinan Liu, Siya Qi
    http://arxiv.org/abs/2106.10084v1

    • [cs.CL]Towards Financial Sentiment Analysis in a South African Landscape
    Michelle Terblanche, Vukosi Marivate
    http://arxiv.org/abs/2106.10004v1

    • [cs.CL]Weakly Supervised Pre-Training for Multi-Hop Retriever
    Yeon Seonwoo, Sang-Woo Lee, Ji-Hoon Kim, Jung-Woo Ha, Alice Oh
    http://arxiv.org/abs/2106.09983v1

    • [cs.CR]Evaluating the Robustness of Trigger Set-Based Watermarks Embedded in Deep Neural Networks
    Suyoung Lee, Wonho Song, Suman Jana, Meeyoung Cha, Sooel Son
    http://arxiv.org/abs/2106.10147v1

    • [cs.CV]A Coarse-to-Fine Instance Segmentation Network with Learning Boundary Representation
    Feng Luo, Bin-Bin Gao, Jiangpeng Yan, Xiu Li
    http://arxiv.org/abs/2106.10213v1

    • [cs.CV]A Dynamic Spatial-temporal Attention Network for Early Anticipation of Traffic Accidents
    Muhammad Monjurul Karim, Yu Li, Ruwen Qin, Zhaozheng Yin
    http://arxiv.org/abs/2106.10197v1

    • [cs.CV]A Framework for Real-time Traffic Trajectory Tracking, Speed Estimation, and Driver Behavior Calibration at Urban Intersections Using Virtual Traffic Lanes
    Awad Abdelhalim, Montasir Abbas, Bhavi Bharat Kotha, Alfred Wicks
    http://arxiv.org/abs/2106.09932v1

    • [cs.CV]Advanced Hough-based method for on-device document localization
    D. V. Tropin, A. M. Ershov, D. P. Nikolaev, V. V
    260
    . Arlazarov
    http://arxiv.org/abs/2106.09987v1

    • [cs.CV]All You Can Embed: Natural Language based Vehicle Retrieval with Spatio-Temporal Transformers
    Carmelo Scribano, Davide Sapienza, Giorgia Franchini, Micaela Verucchi, Marko Bertogna
    http://arxiv.org/abs/2106.10153v1

    • [cs.CV]Bridging the Gap Between Object Detection and User Intent via Query-Modulation
    Marco Fornoni, Chaochao Yan, Liangchen Luo, Kimberly Wilber, Alex Stark, Yin Cui, Boqing Gong, Andrew Howard
    http://arxiv.org/abs/2106.10258v1

    • [cs.CV]Combined Person Classification with Airborne Optical Sectioning
    Indrajit Kurmi, David C. Schedl, Oliver Bimber
    http://arxiv.org/abs/2106.10077v1

    • [cs.CV]Contrastive Learning of Generalized Game Representations
    Chintan Trivedi, Antonios Liapis, Georgios N. Yannakakis
    http://arxiv.org/abs/2106.10060v1

    • [cs.CV]Discerning Generic Event Boundaries in Long-Form Wild Videos
    Ayush K Rai, Tarun Krishna, Julia Dietlmeier, Kevin McGuinness, Alan F Smeaton, Noel E O’Connor
    http://arxiv.org/abs/2106.10090v1

    • [cs.CV]Dual-Teacher Class-Incremental Learning With Data-Free Generative Replay
    Yoojin Choi, Mostafa El-Khamy, Jungwon Lee
    http://arxiv.org/abs/2106.09835v1

    • [cs.CV]EPIC-KITCHENS-100 Unsupervised Domain Adaptation Challenge for Action Recognition 2021: Team M3EM Technical Report
    Lijin Yang, Yifei Huang, Yusuke Sugano, Yoichi Sato
    http://arxiv.org/abs/2106.10026v1

    • [cs.CV]Effective Model Sparsification by Scheduled Grow-and-Prune Methods
    Xiaolong Ma, Minghai Qin, Fei Sun, Zejiang Hou, Kun Yuan, Yi Xu, Yanzhi Wang, Yen-Kuang Chen, Rong Jin, Yuan Xie
    http://arxiv.org/abs/2106.09857v1

    • [cs.CV]End-to-end Temporal Action Detection with Transformer
    Xiaolong Liu, Qimeng Wang, Yao Hu, Xu Tang, Song Bai, Xiang Bai
    http://arxiv.org/abs/2106.10271v1

    • [cs.CV]Equivariance-bridged SO(2)-Invariant Representation Learning using Graph Convolutional Network
    Sungwon Hwang, Hyungtae Lim, Hyun Myung
    http://arxiv.org/abs/2106.09996v1

    • [cs.CV]HifiFace: 3D Shape and Semantic Prior Guided High Fidelity Face Swapping
    Yuhan Wang, Xu Chen, Junwei Zhu, Wenqing Chu, Ying Tai, Chengjie Wang, Jilin Li, Yongjian Wu, Feiyue Huang, Rongrong Ji
    http://arxiv.org/abs/2106.09965v1

    • [cs.CV]How to train your ViT? Data, Augmentation, and Regularization in Vision Transformers
    Andreas Steiner, Alexander Kolesnikov, Xiaohua Zhai, Ross Wightman, Jakob Uszkoreit, Lucas Beyer
    http://arxiv.org/abs/2106.10270v1

    • [cs.CV]Learning and Meshing from Deep Implicit Surface Networks Using an Efficient Implementation of Analytic Marching
    Jiabao Lei, Kui Jia, Yi Ma
    http://arxiv.org/abs/2106.10031v1

    • [cs.CV]Light Lies: Optical Adversarial Attack
    Kyu-Lim Kim, Jeong-Soo Kim, Seung-Ri Song, Jun-Ho Choi, Chul-Min Joo, Jong-Seok Lee
    http://arxiv.org/abs/2106.09908v1

    • [cs.CV]Light Pollution Reduction in Nighttime Photography
    Chang Liu, Xiaolin Wu
    http://arxiv.org/abs/2106.10046v1

    • [cs.CV]Medical Matting: A New Perspective on Medical Segmentation with Uncertainty
    Lin Wang, Lie Ju, Donghao Zhang, Xin Wang, Wanji He, Yelin Huang, Zhiwen Yang, Xuan Yao, Xin Zhao, Xiufen Ye, Zongyuan Ge
    http://arxiv.org/abs/2106.09887v1

    • [cs.CV]Multi-Granularity Network with Modal Attention for Dense Affective Understanding
    Baoming Yan, Lin Wang, Ke Gao, Bo Gao, Xiao Liu, Chao Ban, Jiang Yang, Xiaobo Li
    http://arxiv.org/abs/2106.09964v1

    • [cs.CV]Novelty Detection via Contrastive Learning with Negative Data Augmentation
    Chengwei Chen, Yuan Xie, Shaohui Lin, Ruizhi Qiao, Jian Zhou, Xin Tan, Yi Zhang, Lizhuang Ma
    http://arxiv.org/abs/2106.09958v1

    • [cs.CV]Quantized Neural Networks via {-1, +1} Encoding Decomposition and Acceleration
    Qigong Sun, Xiufang Li, Fanhua Shang, Hongying Liu, Kang Yang, Licheng Jiao, Zhouchen Lin
    http://arxiv.org/abs/2106.09886v1

    • [cs.CV]Residual Contrastive Learning for Joint Demosaicking and Denoising
    Nanqing Dong, Matteo Maggioni, Yongxin Yang, Eduardo Pérez-Pellitero, Ales Leonardis, Steven McDonagh
    http://arxiv.org/abs/2106.10070v1

    • [cs.CV]Self-supervised Video Representation Learning with Cross-Stream Prototypical Contrasting
    Martine Toering, Ioannis Gatopoulos, Maarten Stol, Vincent Tao Hu
    http://arxiv.org/abs/2106.10137v1

    • [cs.CV]Shape Prior Non-Uniform Sampling Guided Real-time Stereo 3D Object Detection
    A. Gao, J. Cao, Y. Pang
    http://arxiv.org/abs/2106.10013v1

    • [cs.CV]Smoothed Multi-View Subspace Clustering
    Peng Chen, Liang Liu, Zhengrui Ma, Zhao Kang
    http://arxiv.org/abs/2106.09875v1

    • [cs.CV]Toward Fault Detection in Industrial Welding Processes with Deep Learning and Data Augmentation
    Jibinraj Antony, Dr. Florian Schlather, Georgij Safronov, Markus Schmitz, Prof. Dr. Kristof Van Laerhoven
    http://arxiv.org/abs/2106.10160v1

    • [cs.CV]Towards Clustering-friendly Representations: Subspace Clustering via Graph Filtering
    Zhengrui Ma, Zhao Kang, Guangchun Luo, Ling Tian
    http://arxiv.org/abs/2106.09874v1

    • [cs.CV]Towards Distraction-Robust Active Visual Tracking
    Fangwei Zhong, Peng Sun, Wenhan Luo, Tingyun Yan, Yizhou Wang
    http://arxiv.org/abs/2106.10110v1

    • [cs.CV]Towards interpreting computer vision based on transformation invariant optimization
    Chen Li, Jinzhe Jiang, Xin Zhang, Tonghuan Zhang, Yaqian Zhao, Dongdong Jiang, RenGang Li
    http://arxiv.org/abs/2106.09982v1

    • [cs.CV]Training or Architecture? How to Incorporate Invariance in Neural Networks
    Kanchana Vaishnavi Gandikota, Jonas Geiping, Zorah Lähner, Adam Czapliński, Michael Moeller
    http://arxiv.org/abs/2106.10044v1

    • [cs.CV]VSAC: Efficient and Accurate Estimator for H and F
    Maksym Ivashechkin, Daniel Barath, Jiri Matas
    http://arxiv.org/abs/2106.10240v1

    • [cs.CV]Virtual Temporal Samples for Recurrent Neural Networks: applied to semantic segmentation in agriculture
    Alireza Ahmadi, Michael Halstead, Chris McCool
    http://arxiv.org/abs/2106.10118v1

    • [cs.CV]hSMAL: Detailed Horse Shape and Pose Reconstruction for Motion Pattern Recognition
    Ci Li, Nima Ghorbani, Sofia Broomé, Maheen Rashid, Michael J. Black, Elin Hernlund, Hedvig Kjellström, Silvia Zuffi
    http://arxiv.org/abs/2106.10102v1

    • [cs.CY]A Fait Accompli? An Empirical Study into the Absence of Consent to Third-Party Tracking in Android Apps
    Konrad Kollnig, Reuben Binns, Pierre Dewitte, Max Van Kleek, Ge Wang, Daniel Omeiza, Helena Webb, Nigel Shadbolt
    http://arxiv.org/abs/2106.09407v2

    • [cs.CY]Data Enforced: An Exploratory Impact Analysis of Automated Speed Enforcement in the District of Columbia
    Awad Abdelhalim, Linda Bailey, Emily Dalphy, Kelli Raboy
    http://arxiv.org/abs/2106.09933v1

    • [cs.CY]Detox Browser — Towards Filtering Sensitive Content On the Web
    Noble Saji Mathews, Sridhar Chimalakonda
    http://arxiv.org/abs/2106.09937v1

    • [cs.CY]How COVID-19 Have Changed Crowdfunding: Evidence From GoFundMe
    Junda Wang, Xupin Zhang, Jiebo Luo
    http://arxiv.org/abs/2106.09981v1

    • [cs.GT]Equilibrium Design for Concurrent Games
    Julian Gutierrez, Muhammad Najib, Giuseppe Perelli, Michael Wooldridge
    http://arxiv.org/abs/2106.10192v1

    • [cs.IR]Heuristic Stopping Rules For Technology-Assisted Review
    Eugene Yang, David D. Lewis, Ophir Frieder
    http://arxiv.org/abs/2106.09871v1

    • [cs.IR]On Minimizing Cost in Legal Document Review Workflows
    Eugene Yang, David D. Lewis, Ophir Frieder
    http://arxiv.org/abs/2106.09866v1

    • [cs.IR]Point-of-Interest Recommender Systems: A Survey from an Experimental Perspective
    Pablo Sánchez, Alejandro Bellogín
    http://arxiv.org/abs/2106.10069v1

    • [cs.IT]Degree Tables for Secure Distributed Matrix Multiplication
    Rafael G. L. D’Oliveira, Salim El Rouayheb, Daniel Heinlein, David Karpuk
    http://arxiv.org/abs/2106.09816v1

    • [cs.IT]Determining when a truncated generalised Reed-Solomon code is Hermitian self-orthogonal
    Simeon Ball, Ricard Vilar
    http://arxiv.org/abs/2106.10180v1

    • [cs.IT]Is Channel Estimation Necessary to Select Phase-Shifts for RIS-Assisted Massive MIMO?
    Özlem Tuğfe Demir, Emil Björnson
    http://arxiv.org/abs/2106.09770v1

    • [cs.IT]Performance Analysis of Synergetic UAV-RIS Communication Networks
    Dimitrios Tyrovolas, Sotiris A. Tegos, Panagiotis D. Diamantoulakis, George K. Karagiannidis
    http://arxiv.org/abs/2106.10034v1

    • [cs.LG]A Note on Optimizing Distributions using Kernel Mean Embeddings
    Boris Muzellec, Francis Bach, Alessandro Rudi
    http://arxiv.org/abs/2106.09994v1

    • [cs.LG]A Probabilistic Representation of DNNs: Bridging Mutual Information and Generalization
    Xinjie Lan, Kenneth Barner
    http://arxiv.org/abs/2106.10262v1

    • [cs.LG]A Unified Generative Adversarial Network Training via Self-Labeling and Self-Attention
    Tomoki Watanabe, Paolo Favaro
    http://arxiv.org/abs/2106.09914v1

    • [cs.LG]A Vertical Federated Learning Framework for Horizontally Partitioned Labels
    Wensheng Xia, Ying Li, Lan Zhang, Zhonghai Wu, Xiaoyong Yuan
    http://arxiv.org/abs/2106.10056v1

    • [cs.LG]Accumulative Poisoning Attacks on Real-time Data
    Tianyu Pang, Xiao Yang, Yinpeng Dong, Hang Su, Jun Zhu
    http://arxiv.org/abs/2106.09993v1

    • [cs.LG]Active Offline Policy Selection
    Ksenia Konyushkova, Yutian Chen, Thomas Paine, Caglar Gulcehre, Cosmin Paduraru, Daniel J Mankowitz, Misha Denil, Nando de Freitas
    http://arxiv.org/abs/2106.10251v1

    • [cs.LG]Adversarial Training Helps Transfer Learning via Better Representations
    Zhun Deng, Linjun Zhang, Kailas Vodrahalli, Kenji Kawaguchi, James Zou
    http://arxiv.org/abs/2106.10189v1

    • [cs.LG]An Empirical Investigation into Deep and Shallow Rule Learning
    Florian Beck, Johannes Fürnkranz
    http://arxiv.org/abs/2106.10254v1

    • [cs.LG]An Investigation into Mini-Batch Rule Learning
    Florian Beck, Johannes Fürnkranz
    http://arxiv.org/abs/2106.10202v1

    • [cs.LG]Batch Multi-Fidelity Bayesian Optimization with Deep Auto-Regressive Networks
    Shibo Li, Robert M. Kirby, Shandian Zhe
    http://arxiv.org/abs/2106.09884v1

    • [cs.LG]Being Properly Improper
    Richard Nock, Tyler Sypherd, Lalitha Sankar
    http://arxiv.org/abs/2106.09920v1

    • [cs.LG]Being a Bit Frequentist Improves Bayesian Neural Networks
    Agustinus Kristiadi, Matthias Hein, Philipp Hennig
    http://arxiv.org/abs/2106.10065v1

    • [cs.LG]BinarizedAttack: Structural Poisoning Attacks to Graph-based Anomaly Detection
    Yulin Zhu, Yuni Lai, Kaifa Zhao, Xiapu Luo, Mingquan Yuan, Jian Ren, Kai Zhou
    http://arxiv.org/abs/2106.09989v1

    • [cs.LG]BitFit: Simple Parameter-efficient Fine-tuning for Transformer-based Masked Language-models
    Elad Ben Zaken, Shauli Ravfogel, Yoav Goldberg
    http://arxiv.org/abs/2106.10199v1

    • [cs.LG]Boolean Matrix Factorization with SAT and MaxSAT
    Florent Avellaneda, Roger Villemaire
    http://arxiv.org/abs/2106.10105v1

    • [cs.LG]Combining Pseudo-Point and State Space Approximations for Sum-Separable Gaussian Processes
    Will Tebbutt, Arno Solin, Richard E. Turner
    http://arxiv.org/abs/2106.10210v1

    • [cs.LG]Deep Reinforcement Learning Models Predict Visual Responses in the Brain: A Preliminary Result
    Maytus Piriyajitakonkij, Sirawaj Itthipuripat, Theerawit Wilaiprasitporn, Nat Dilokthanakul
    http://arxiv.org/abs/2106.10112v1

    • [cs.LG]Distributed Deep Learning in Open Collaborations
    Michael Diskin, Alexey Bukhtiyarov, Max Ryabinin, Lucile Saulnier, Quentin Lhoest, Anton Sinitsin, Dmitry Popov, Dmitry Pyrkin, Maxim Kashirin, Alexander Borzunov, Albert Villanova del Moral, Denis Mazur, Ilia Kobelev, Yacine Jernite, Thomas Wolf, Gennady Pekhimenko
    http://arxiv.org/abs/2106.10207v1

    • [cs.LG]Evolving GANs: When Contradictions Turn into Compliance
    Sauptik Dhar, Javad Heydari, Samarth Tripathi, Unmesh Kurup, Mohak Shah
    http://arxiv.org/abs/2106.09946v1

    • [cs.LG]Federated Robustness Propagation: Sharing Adversarial Robustness in Federated Learning
    Junyuan Hong, Haotao Wang, Zhangyang Wang, Jiayu Zhou
    http://arxiv.org/abs/2106.10196v1

    • [cs.LG]FinGAT: Financial Graph Attention Networks for Recommending Top-K Profitable Stocks
    Yi-Ling Hsu, Yu-Che Tsai, Cheng-Te Li
    http://arxiv.org/abs/2106.10159v1

    • [cs.LG]Fusion of Embeddings Networks for Robust Combination of Text Dependent and Independent Speaker Recognition
    Ruirui Li, Chelsea J. -T. Ju, Zeya Chen, Hongda Mao, Oguz Elibol, Andreas Stolcke
    http://arxiv.org/abs/2106.10169v1

    • [cs.LG]Goal-Directed Planning by Reinforcement Learning and Active Inference
    Dongqi Han, Kenji Doya, Jun Tani
    http://arxiv.org/abs/2106.09938v1

    • [cs.LG]Graph Context Encoder: Graph Feature Inpainting for Graph Generation and Self-supervised Pretraining
    Oriel Frigo, Rémy Brossard, David Dehaene
    http://arxiv.org/abs/2106.10124v1

    • [cs.LG]Indicators of Attack Failure: Debugging and Improving Optimization of Adversarial Examples
    Maura Pintor, Luca Demetrio, Angelo Sotgiu, Giovanni Manca, Ambra Demontis, Nicholas Carlini, Battista Biggio, Fabio Roli
    http://arxiv.org/abs/2106.09947v1

    • [cs.LG]Investigating the Role of Negatives in Contrastive Representation Learning
    Jordan T. Ash, Surbhi Goel, Akshay Krishnamurthy, Dipendra Misra
    http://arxiv.org/abs/2106.09943v1

    • [cs.LG]It’s FLAN time! Summing feature-wise latent representations for interpretability
    An-phi Nguyen, Maria Rodriguez Martinez
    http://arxiv.org/abs/2106.10086v1

    • [cs.LG]Iterative Feature Matching: Toward Provable Domain Generalization with Logarithmic Environments
    Yining Chen, Elan Rosenfeld, Mark Sellke, Tengyu Ma, Andrej Risteski
    http://arxiv.org/abs/2106.09913v1

    • [cs.LG]Labelling Drifts in a Fault Detection System for Wind Turbine Maintenance
    Iñigo Martinez, Elisabeth Viles, Iñaki Cabrejas
    http://arxiv.org/abs/2106.09951v1

    • [cs.LG]Learning to Generate Code Sketches
    Daya Guo, Alexey Svyatkovskiy, Jian Yin, Nan Duan, Marc Brockschmidt, Miltiadis Allamanis
    http://arxiv.org/abs/2106.10158v1

    • [cs.LG]Learning to Plan via a Multi-Step Policy Regression Method
    Stefan Wagner, Michael Janschek, Tobias Uelwer, Stefan Harmeling
    http://arxiv.org/abs/2106.10075v1

    • [cs.LG]Less is More: Feature Selection for Adversarial Robustness with Compressive Counter-Adversarial Attacks
    Emre Ozfatura, Muhammad Zaid Hameed, Kerem Ozfatura, Deniz Gunduz
    http://arxiv.org/abs/2106.10252v1

    • [cs.LG]Local AdaGrad-Type Algorithm for Stochastic Convex-Concave Minimax Problems
    Luofeng Liao, Li Shen, Jia Duan, Mladen Kolar, Dacheng Tao
    http://arxiv.org/abs/2106.10022v1

    • [cs.LG]Locally Differentially Private Federated Learning: Efficient Algorithms with Tight Risk Bounds
    Andrew Lowy, Meisam Razaviyayn
    http://arxiv.org/abs/2106.09779v1

    • [cs.LG]MADE: Exploration via Maximizing Deviation from Explored Regions
    Tianjun Zhang, Paria Rashidinejad, Jiantao Jiao, Yuandong Tian, Joseph Gonzalez, Stuart Russell
    http://arxiv.org/abs/2106.10268v1

    • [cs.LG]Machining Cycle Time Prediction: Data-driven Modelling of Machine Tool Feedrate Behavior with Neural Networks
    Chao Sun, Javier Dominguez-Caballero, Rob Ward, Sabino Ayvar-Soberanis, David Curtis
    http://arxiv.org/abs/2106.09719v1

    • [cs.LG]Message Passing in Graph Convolution Networks via Adaptive Filter Banks
    Xing Gao, Wenrui Dai, Chenglin Li, Junni Zou, Hongkai Xiong, Pascal Frossard
    http://arxiv.org/abs/2106.09910v1

    • [cs.LG]NoiseGrad: enhancing explanations by introducing stochasticity to model weights
    Kirill Bykov, Anna Hedström, Shinichi Nakajima, Marina M. -C. Höhne
    http://arxiv.org/abs/2106.10185v1

    • [cs.LG]Nonparametric Hamiltonian Monte Carlo
    Carol Mak, Fabian Zaiser, Luke Ong
    http://arxiv.org/abs/2106.10238v1

    • [cs.LG]On Invariance Penalties for Risk Minimization
    Kia Khezeli, Arno Blaas, Frank Soboczenski, Nicholas Chia, John Kalantari
    http://arxiv.org/abs/2106.09777v1

    • [cs.LG]On the Connections between Counterfactual Explanations and Adversarial Examples
    Martin Pawelczyk, Shalmali Joshi, Chirag Agarwal, Sohini Upadhyay, Himabindu Lakkaraju
    http://arxiv.org/abs/2106.09992v1

    • [cs.LG]On the Sample Complexity of Batch Reinforcement Learning with Policy-Induced Data
    Chenjun Xiao, Ilbin Lee, Bo Dai, Dale Schuurmans, Csaba Szepesvari
    http://arxiv.org/abs/2106.09973v1

    • [cs.LG]PAC Prediction Sets Under Covariate Shift
    Sangdon Park, Edgar Dobriban, Insup Lee, Osbert Bastani
    http://arxiv.org/abs/2106.09848v1

    • [cs.LG]Predicting gender of Brazilian names using deep learning
    Rosana C. B. Rego, Verônica M. L. Silva
    http://arxiv.org/abs/2106.10156v1

    • [cs.LG]PyKale: Knowledge-Aware Machine Learning from Multiple Sources in Python
    Haiping Lu, Xianyuan Liu, Robert Turner, Peizhen Bai, Raivo E Koot, Shuo Zhou, Mustafa Chasmai, Lawrence Schobs
    http://arxiv.org/abs/2106.09756v1

    • [cs.LG]QuantumFed: A Federated Learning Framework for Collaborative Quantum Training
    Qi Xia, Qun Li
    http://arxiv.org/abs/2106.09109v2

    • [cs.LG]Rational Shapley Values
    David S. Watson
    http://arxiv.org/abs/2106.10191v1

    • [cs.LG]Residual Error: a New Performance Measure for Adversarial Robustness
    Hossein Aboutalebi, Mohammad Javad Shafiee, Michelle Karg, Christian Scharfenberger, Alexander Wong
    http://arxiv.org/abs/2106.10212v1

    • [cs.LG]Riemannian Convex Potential Maps
    Samuel Cohen, Brandon Amos, Yaron Lipman
    http://arxiv.org/abs/2106.10272v1

    • [cs.LG]ScoreGrad: Multivariate Probabilistic Time Series Forecasting with Continuous Energy-based Generative Models
    Tijin Yan, Hongwei Zhang, Tong Zhou, Yufeng Zhan, Yuanqing Xia
    http://arxiv.org/abs/2106.10121v1

    • [cs.LG]Self-supervised Incremental Deep Graph Learning for Ethereum Phishing Scam Detection
    Shucheng Li, Fengyuan Xu, Runchuan Wang, Sheng Zhong
    http://arxiv.org/abs/2106.10176v1

    • [cs.LG]Steerable Partial Differential Operators for Equivariant Neural Networks
    Erik Jenner, Maurice Weiler
    http://arxiv.org/abs/2106.10163v1

    • [cs.LG]The Dimpled Manifold Model of Adversarial Examples in Machine Learning
    Adi Shamir, Odelia Melamed, Oriel BenShmuel
    http://arxiv.org/abs/2106.10151v1

    • [cs.LG]The Principles of Deep Learning Theory
    Daniel A. Roberts, Sho Yaida, Boris Hanin
    http://arxiv.org/abs/2106.10165v1

    • [cs.LG]World-GAN: a Generative Model for Minecraft Worlds
    Maren Awiszus, Frederik Schubert, Bodo Rosenhahn
    http://arxiv.org/abs/2106.10155v1

    • [cs.LG]Zero-Shot Federated Learning with New Classes for Audio Classification
    Gautham Krishna Gudur, Satheesh K. Perepu
    http://arxiv.org/abs/2106.10019v1

    • [cs.LG]pyWATTS: Python Workflow Automation Tool for Time Series
    Benedikt Heidrich, Andreas Bartschat, Marian Turowski, Oliver Neumann, Kaleb Phipps, Stefan Meisenbacher, Kai Schmieder, Nicole Ludwig, Ralf Mikut, Veit Hagenmeyer
    http://arxiv.org/abs/2106.10157v1

    • [cs.NE]A Fresh Approach to Evaluate Performance in Distributed Parallel Genetic Algorithms
    Tomohiro Harada, Enrique Alba, Gabriel Luque
    http://arxiv.org/abs/2106.09922v1

    • [cs.RO]Development of a conversing and body temperature scanning autonomously navigating robot to help screen for COVID-19
    Ryan Kim
    http://arxiv.org/abs/2106.09894v1

    • [cs.RO]Human-Aware Navigation Planner for Diverse Human-Robot Contexts
    Phani Singamaneni, Anthony Favier, Rachid Alami
    http://arxiv.org/abs/2106.09971v1

    • [cs.RO]Improved Radar Localization on Lidar Maps Using Shared Embedding
    Huan Yin, Yue Wang, Rong Xiong
    http://arxiv.org/abs/2106.10000v1

    • [cs.RO]Optimizing robotic swarm based construction tasks
    Teshan Liyanage, Subha Fernando
    http://arxiv.org/abs/2106.09749v1

    • [cs.RO]Position-based Dynamics Simulator of Brain Deformations for Path Planning and Intra-Operative Control in Keyhole Neurosurgery
    Alice Segato, Chiara Di Vece, Sara Zucchelli, Marco Di Marzo, Thomas Wendler, Mohammad Farid Azampour, Stefano Galvan, Riccardo Secoli, Elena De Momi
    http://arxiv.org/abs/2106.10206v1

    • [cs.RO]Semantic navigation with domain knowledge
    Rafael Gomes Braga, Sina Karimi, Ulrich Dah-Achinanon, Ivanka Iordanova, David St-Onge
    http://arxiv.org/abs/2106.10220v1

    • [cs.RO]Towards Robotic Laboratory Automation Plug & Play: The “LAPP” Framework
    Ádám Wolf, David Wolton, Josef Trapl, Julien Janda, Stefan Romeder-Finger, Thomas Gatternig, Jean-Baptiste Farcet, Péter Galambos, Károly Széll
    http://arxiv.org/abs/2106.10129v1

    • [cs.RO]Under the Sand: Navigation and Localization of a Small Unmanned Aerial Vehicle for Landmine Detection with Ground Penetrating Synthetic Aperture Radar
    Rik Bähnemann, Nicholas Lawrance, Lucas Streichenberg, Jen Jen Chung, Michael Pantic, Alexander Grathwohl, Christian Waldschmidt, Roland Siegwart
    http://arxiv.org/abs/2106.10108v1

    • [cs.RO]Variable-Grasping-Mode Gripper With Different Finger Structures For Grasping Small-Sized Items
    Tetsuyou Watanabe, Kota Morino, Yoshitatsu Asama, Seiji Nishitani, Ryo Toshima
    http://arxiv.org/abs/2106.09957v1

    • [cs.SD]Synchronising speech segments with musical beats in Mandarin and English singing
    Cong Zhang, Jian Zhu
    http://arxiv.org/abs/2106.10045v1

    • [cs.SI]Centrality Measures in Interval-Weighted Networks
    Hélder Alves, Paula Brito, Pedro Campos
    http://arxiv.org/abs/2106.10016v1

    • [cs.SI]Community Detection in Interval-Weighted Networks
    Hélder Alves, Paula Brito, Pedro Campos
    http://arxiv.org/abs/2106.10217v1

    • [cs.SI]Embedding Heterogeneous Networks into Hyperbolic Space Without Meta-path
    Lili Wang, Chongyang Gao, Chenghan Huang, Ruibo Liu, Weicheng Ma, Soroush Vosoughi
    http://arxiv.org/abs/2106.09923v1

    • [cs.SI]Meta-control of social learning strategies
    Anil Yaman, Nicolas Bredeche, Onur Çaylak, Joel Z. Leibo, Sang Wan Lee
    http://arxiv.org/abs/2106.10015v1

    • [eess.AS]Low Resource German ASR with Untranscribed Data Spoken by Non-native Children — INTERSPEECH 2021 Shared Task SPAPL System
    Jinhan Wang, Yunzheng Zhu, Ruchao Fan, Wei Chu, Abeer Alwan
    http://arxiv.org/abs/2106.09963v1

    • [eess.AS]Multi-mode Transformer Transducer with Stochastic Future Context
    Kwangyoun Kim, Felix Wu, Prashant Sridhar, Kyu J. Han, Shinji Watanabe
    http://arxiv.org/abs/2106.09760v1

    • [eess.AS]On-Device Personalization of Automatic Speech Recognition Models for Disordered Speech
    Katrin Tomanek, Françoise Beaufays, Julie Cattiau, Angad Chandorkar, Khe Chai Sim
    http://arxiv.org/abs/2106.10259v1

    • [eess.IV]Debiased Subjective Assessment of Real-World Image Enhancement
    Cao Peibei. Wang Zhangyang, Ma Kede
    http://arxiv.org/abs/2106.10080v1

    • [eess.IV]Non-Iterative Phase Retrieval With Cascaded Neural Networks
    Tobias Uelwer, Tobias Hoffmann, Stefan Harmeling
    http://arxiv.org/abs/2106.10195v1

    • [eess.SP]ICINet: ICI-Aware Neural Network Based Channel Estimation for Rapidly Time-Varying OFDM Systems
    Yi Sun, Hong Shen, Zhenguo Du, Lan Peng, Chunming Zhao
    http://arxiv.org/abs/2106.09891v1

    • [math.OC]Distributed optimal power flow
    HyungSeon Oh
    http://arxiv.org/abs/2106.10051v1

    • [math.OC]Escaping strict saddle points of the Moreau envelope in nonsmooth optimization
    Damek Davis, Mateo Díaz, Dmitriy Drusvyatskiy
    http://arxiv.org/abs/2106.09815v1

    • [math.PR]Sharp Lower and Upper Bounds for the Covariance of Bounded Random Variables
    Ola Hössjer, Arvid Sjölander
    http://arxiv.org/abs/2106.10037v1

    • [math.ST]CLT for LSS of sample covariance matrices with unbounded dispersions
    Liu Zhijun, Bai Zhidong, Hu Jiang, Song Haiyan
    http://arxiv.org/abs/2106.10135v1

    • [math.ST]Entrywise limit theorems of eigenvectors and their one-step refinement for sparse random graphs
    Fangzheng Xie
    http://arxiv.org/abs/2106.09840v1

    • [math.ST]Generalized regression operator estimation for continuous time functional data processes with missing at random response
    Mohamed Chaouch, Naâmane Laïb
    http://arxiv.org/abs/2106.09769v1

    • [math.ST]Local asymptotics of cross-validation in least-squares density estimation
    Guillaume Maillard
    http://arxiv.org/abs/2106.09962v1

    • [physics.soc-ph]Systematic comparison of graph embedding methods in practical tasks
    Yi-Jiao Zhang, Kai-Cheng Yang, Filippo Radicchi
    http://arxiv.org/abs/2106.10198v1

    • [stat.AP]SAGE: Stealthy Attack GEneration for Cyber-Physical Systems
    Michael Biehler, Zhen Zhong, Jianjun Shi
    http://arxiv.org/abs/2106.09905v1

    • [stat.AP]Sparse Linear Spectral Unmixing of Hyperspectral images using Expectation-Propagation
    Zeng Li, Yoann Altmann, Jie Chen, Stephen Mclaughlin, Susanto Rahardja
    http://arxiv.org/abs/2106.09985v1

    • [stat.AP]Trend estimation and short-term forecasting of COVID-19 cases and deaths worldwide
    Ekaterina Krymova, Benjamín Béjar, Dorina Thanou, Tao Sun, Elisa Manetti, Gavin Lee, Kristen Namigai, Christine Choirat, Antoine Flahault, Guillaume Obozinski
    http://arxiv.org/abs/2106.10203v1

    • [stat.CO]Deterministic Gibbs Sampling via Ordinary Differential Equations
    Kirill Neklyudov, Roberto Bondesan, Max Welling
    http://arxiv.org/abs/2106.10188v1

    • [stat.ME]Assessing an Alternative for `Negative Variance Components’: A Gentle Introduction to Bayesian Covariance Structure Modelling for Negative Associations Among Patients with Personalized Treatments
    Jean-Paul Fox, Wouter Smink
    http://arxiv.org/abs/2106.10107v1

    • [stat.ME]Bayesian Cox Regression for Population-scale Inference in Electronic Health Records
    Alexander W. Jung, Moritz Gerstung
    http://arxiv.org/abs/2106.10057v1

    • [stat.ME]Causal Bias Quantification for Continuous Treatment
    Gianluca Detommaso, Michael Brückner, Philip Schulz, Victor Chernozhukov
    http://arxiv.org/abs/2106.09762v1

    • [stat.ME]Distributionally Weighted Least Squares in Structural Equation Modeling
    Han Du, Peter M. Bentler
    http://arxiv.org/abs/2106.09845v1

    • [stat.ME]Generalized Linear Randomized Response Modeling using GLMMRR
    Jean-Paul Fox, Konrad Klotzke, Duco Veen
    http://arxiv.org/abs/2106.10171v1

    • [stat.ME]LNIRT: An R Package for Joint Modeling of Response Accuracy and Times
    Jean-Paul Fox, Konrad Klotzke, Ahmet Salih Simsek
    http://arxiv.org/abs/2106.10144v1

    • [stat.ME]Robust nonparametric hypothesis tests for differences in the covariance structure of functional data
    Kelly Ramsay, Shojaeddin Chenouri
    http://arxiv.org/abs/2106.10173v1

    • [stat.ML]An Analysis of the Deployment of Models Trained on Private Tabular Synthetic Data: Unexpected Surprises
    Mayana Pereira, Meghana Kshirsagar, Sumit Mukherjee, Rahul Dodhia, Juan Lavista Ferres
    http://arxiv.org/abs/2106.10241v1

    • [stat.ML]Fitting summary statistics of neural data with a differentiable spiking network simulator
    Guillaume Bellec, Shuqi Wang, Alireza Modirshanechi, Johanni Brea, Wulfram Gerstner
    http://arxiv.org/abs/2106.10064v1

    • [stat.ML]On Contrastive Representations of Stochastic Processes
    Emile Mathieu, Adam Foster, Yee Whye Teh
    http://arxiv.org/abs/2106.10052v1

    • [stat.ML]Problem Dependent View on Structured Thresholding Bandit Problems
    James Cheshire, Pierre Ménard, Alexandra Carpentier
    http://arxiv.org/abs/2106.10166v1

    • [stat.ML]Wide stochastic networks: Gaussian limit and PAC-Bayesian training
    Eugenio Clerico, George Deligiannidis, Arnaud Doucet
    http://arxiv.org/abs/2106.09798v1（）