今日学术视野(2021.6.9)

astro-ph.IM - 仪器仪表和天体物理学方法
cond-mat.dis-nn - 无序系统与神经网络
cond-mat.mtrl-sci - 材料科学
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.FL - 形式语言与自动机理论
cs.GR - 计算机图形学
cs.GT - 计算机科学与博弈论
cs.HC - 人机接口
cs.IR - 信息检索
cs.IT - 信息论
cs.LG - 自动学习
cs.NE - 神经与进化计算
cs.NI - 网络和互联网体系结构
cs.RO - 机器人学
cs.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
eess.SY - 系统和控制
math.AG - 代数几何
math.AC - 交换代数
math.OC - 优化与控制
math.ST - 统计理论
q-bio.NC - 神经元与认知
q-bio.PE - 人口与发展
q-fin.TR - 贸易与市场微观结构
quant-ph - 量子物理
stat.AP - 应用统计
stat.ME - 统计方法论
stat.ML - (统计)机器学习

• [astro-ph.IM]Application of neural networks to classification of data of the TUS orbital telescope
• [astro-ph.IM]The effect of phased recurrent units in the classification of multiple catalogs of astronomical lightcurves
• [astro-ph.IM]Training Strategies for Deep Learning Gravitational-Wave Searches
• [cond-mat.dis-nn]The topological Dirac equation of networks and simplicial complexes
• [cond-mat.mtrl-sci]Inverse design of two-dimensional materials with invertible neural networks
• [cs.AI]A generative model for molecule generation based on chemical reaction trees
• [cs.AI]Believe What You See: Implicit Constraint Approach for Offline Multi-Agent Reinforcement Learning
• [cs.AI]Causal Abstractions of Neural Networks
• [cs.AI]Empirically Evaluating Creative Arc Negotiation for Improvisational Decision-making
• [cs.AI]End-to-End Neuro-Symbolic Architecture for Image-to-Image Reasoning Tasks
• [cs.AI]Explainable Artificial Intelligence (XAI) for Increasing User Trust in Deep Reinforcement Learning Driven Autonomous Systems
• [cs.AI]Extending counterfactual accounts of intent to include oblique intent
• [cs.AI]Hierarchical Task Learning from Language Instructions with Unified Transformers and Self-Monitoring
• [cs.AI]Multi-modal Entity Alignment in Hyperbolic Space
• [cs.AI]Path-specific Effects Based on Information Accounts of Causality
• [cs.AI]Reinforcement Learning for Assignment Problem with Time Constraints
• [cs.AI]Uncertain Process Data with Probabilistic Knowledge: Problem Characterization and Challenges
• [cs.AI]What if we Increase the Number of Objectives? Theoretical and Empirical Implications for Many-objective Optimization
• [cs.AI]Zero-shot Task Adaptation using Natural Language
• [cs.CL]A Comprehensive Assessment of Dialog Evaluation Metrics
• [cs.CL]A Globally Normalized Neural Model for Semantic Parsing
• [cs.CL]A Joint Model for Dropped Pronoun Recovery and Conversational Discourse Parsing in Chinese Conversational Speech
• [cs.CL]A Simple Recipe for Multilingual Grammatical Error Correction
• [cs.CL]A Targeted Assessment of Incremental Processing in Neural LanguageModels and Humans
• [cs.CL]Apurinã Universal Dependencies Treebank
• [cs.CL]Attend and Select: A Segment Attention based Selection Mechanism for Microblog Hashtag Generation
• [cs.CL]Attention Temperature Matters in Abstractive Summarization Distillation
• [cs.CL]BERTGEN: Multi-task Generation through BERT
• [cs.CL]BERTnesia: Investigating the capture and forgetting of knowledge in BERT
• [cs.CL]BiToD: A Bilingual Multi-Domain Dataset For Task-Oriented Dialogue Modeling
• [cs.CL]CAiRE in DialDoc21: Data Augmentation for Information-Seeking Dialogue System
• [cs.CL]COVID-Fact: Fact Extraction and Verification of Real-World Claims on COVID-19 Pandemic
• [cs.CL]Combining Static Word Embeddings and Contextual Representations for Bilingual Lexicon Induction
• [cs.CL]Deep Context- and Relation-Aware Learning for Aspect-based Sentiment Analysis
• [cs.CL]Denoising Word Embeddings by Averaging in a Shared Space
• [cs.CL]Diverse Pretrained Context Encodings Improve Document Translation
• [cs.CL]Diversity driven Query Rewriting in Search Advertising
• [cs.CL]Do Grammatical Error Correction Models Realize Grammatical Generalization?
• [cs.CL]Document-level Relation Extraction as Semantic Segmentation
• [cs.CL]Embracing Ambiguity: Shifting the Training Target of NLI Models
• [cs.CL]Emergent Communication of Generalizations
• [cs.CL]Emotion-aware Chat Machine: Automatic Emotional Response Generation for Human-like Emotional Interaction
• [cs.CL]Empowering Language Understanding with Counterfactual Reasoning
• [cs.CL]Encouraging Neural Machine Translation to Satisfy Terminology Constraints
• [cs.CL]Enhancing Label Correlation Feedback in Multi-Label Text Classification via Multi-Task Learning
• [cs.CL]Enhancing Taxonomy Completion with Concept Generation via Fusing Relational Representations
• [cs.CL]Extractive Research Slide Generation Using Windowed Labeling Ranking
• [cs.CL]GTM: A Generative Triple-Wise Model for Conversational Question Generation
• [cs.CL]Generating Relevant and Coherent Dialogue Responses using Self-separated Conditional Variational AutoEncoders
• [cs.CL]How Did This Get Funded?! Automatically Identifying Quirky Scientific Achievements
• [cs.CL]Identifying Populist Paragraphs in Text: A machine-learning approach
• [cs.CL]Improving Automated Evaluation of Open Domain Dialog via Diverse Reference Augmentation
• [cs.CL]Itihasa: A large-scale corpus for Sanskrit to English translation
• [cs.CL]LAWDR: Language-Agnostic Weighted Document Representations from Pre-trained Models
• [cs.CL]Let’s be explicit about that: Distant supervision for implicit discourse relation classification via connective prediction
• [cs.CL]Lexical Semantic Change Discovery
• [cs.CL]Lifelong Learning of Hate Speech Classification on Social Media
• [cs.CL]MergeDistill: Merging Pre-trained Language Models using Distillation
• [cs.CL]Meta-Learning with Variational Semantic Memory for Word Sense Disambiguation
• [cs.CL]Meta-learning for downstream aware and agnostic pretraining
• [cs.CL]MultiOpEd: A Corpus of Multi-Perspective News Editorials
• [cs.CL]Multilingual Neural Semantic Parsing for Low-Resourced Languages
• [cs.CL]Narrative Question Answering with Cutting-Edge Open-Domain QA Techniques: A Comprehensive Study
• [cs.CL]Never guess what I heard… Rumor Detection in Finnish News: a Dataset and a Baseline
• [cs.CL]On the Effectiveness of Adapter-based Tuning for Pretrained Language Model Adaptation
• [cs.CL]On the Language Coverage Bias for Neural Machine Translation
• [cs.CL]PROST: Physical Reasoning of Objects through Space and Time
• [cs.CL]Position Bias Mitigation: A Knowledge-Aware Graph Model for EmotionCause Extraction
• [cs.CL]RedditBias: A Real-World Resource for Bias Evaluation and Debiasing of Conversational Language Models
• [cs.CL]Relative Importance in Sentence Processing
• [cs.CL]RoSearch: Search for Robust Student Architectures When Distilling Pre-trained Language Models
• [cs.CL]Semantic and Syntactic Enhanced Aspect Sentiment Triplet Extraction
• [cs.CL]Semantic-Enhanced Explainable Finetuning for Open-Domain Dialogues
• [cs.CL]Structured Reordering for Modeling Latent Alignments in Sequence Transduction
• [cs.CL]Summary Grounded Conversation Generation
• [cs.CL]The FLORES-101 Evaluation Benchmark for Low-Resource and Multilingual Machine Translation
• [cs.CL]The R-U-A-Robot Dataset: Helping Avoid Chatbot Deception by Detecting User Questions About Human or Non-Human Identity
• [cs.CL]Transient Chaos in BERT
• [cs.CL]Unsupervised Representation Disentanglement of Text: An Evaluation on Synthetic Datasets
• [cs.CL]W-RST: Towards a Weighted RST-style Discourse Framework
• [cs.CL]Weakly-Supervised Methods for Suicide Risk Assessment: Role of Related Domains
• [cs.CL]X2Parser: Cross-Lingual and Cross-Domain Framework for Task-Oriented Compositional Semantic Parsing
• [cs.CR]Sensor Fusion-based GNSS Spoofing Attack Detection Framework for Autonomous Vehicles
• [cs.CR]Tetrad: Actively Secure 4PC for Secure Training and Inference
• [cs.CV]3D Convolution Neural Network based Person Identification using Gait cycles
• [cs.CV]3DB: A Framework for Debugging Computer Vision Models
• [cs.CV]A Comprehensive Survey on Image Dehazing Based on Deep Learning
• [cs.CV]Adversarial Attack and Defense in Deep Ranking
• [cs.CV]Alpha Matte Generation from Single Input for Portrait Matting
• [cs.CV]An Adaptive Framework for Learning Unsupervised Depth Completion
• [cs.CV]An End-to-End Breast Tumour Classification Model Using Context-Based Patch Modelling- A BiLSTM Approach for Image Classification
• [cs.CV]Bias Mitigation of Face Recognition Models Through Calibration
• [cs.CV]CDN-MEDAL: Two-stage Density and Difference Approximation Framework for Motion Analysis
• [cs.CV]Category Contrast for Unsupervised Domain Adaptation in Visual Tasks
• [cs.CV]Channel DropBlock: An Improved Regularization Method for Fine-Grained Visual Classification
• [cs.CV]Combinatorial Optimization for Panoptic Segmentation: An End-to-End Trainable Approach
• [cs.CV]Contextual Guided Segmentation Framework for Semi-supervised Video Instance Segmentation
• [cs.CV]ContourRender: Detecting Arbitrary Contour Shape For Instance Segmentation In One Pass
• [cs.CV]Convolutional Neural Networks with Gated Recurrent Connections
• [cs.CV]DINs: Deep Interactive Networks for Neurofibroma Segmentation in Neurofibromatosis Type 1 on Whole-Body MRI
• [cs.CV]Deep Learning 3D Dose Prediction for Conventional Lung IMRT Using Consistent/Unbiased Automated Plans
• [cs.CV]Deep Matching Prior: Test-Time Optimization for Dense Correspondence
• [cs.CV]Digital Taxonomist: Identifying Plant Species in Citizen Scientists’ Photographs
• [cs.CV]DoubleField: Bridging the Neural Surface and Radiance Fields for High-fidelity Human Rendering
• [cs.CV]Dynamic Resolution Network
• [cs.CV]Efficient Iterative Amortized Inference for Learning Symmetric and Disentangled Multi-Object Representations
• [cs.CV]Efficient Training of Visual Transformers with Small-Size Datasets
• [cs.CV]Efficient training for future video generation based on hierarchical disentangled representation of latent variables
• [cs.CV]End-to-end reconstruction meets data-driven regularization for inverse problems
• [cs.CV]Exploiting Emotional Dependencies with Graph Convolutional Networks for Facial Expression Recognition
• [cs.CV]Exploring to establish an appropriate model for mage aesthetic assessment via CNN-based RSRL: An empirical study
• [cs.CV]FINet: Dual Branches Feature Interaction for Partial-to-Partial Point Cloud Registration
• [cs.CV]Feature Flow Regularization: Improving Structured Sparsity in Deep Neural Networks
• [cs.CV]Feature-based Style Randomization for Domain Generalization
• [cs.CV]Few-Shot Unsupervised Image-to-Image Translation on complex scenes
• [cs.CV]Few-shot segmentation of medical images based on meta-learning with implicit gradients
• [cs.CV]Go with the Flows: Mixtures of Normalizing Flows for Point Cloud Generation and Reconstruction
• [cs.CV]HERS Superpixels: Deep Affinity Learning for Hierarchical Entropy Rate Segmentation
• [cs.CV]High Resolution Solar Image Generation using Generative Adversarial Networks
• [cs.CV]Highlighting the Importance of Reducing Research Bias and Carbon Emissions in CNNs
• [cs.CV]IPS300+: a Challenging Multimodal Dataset for Intersection Perception System
• [cs.CV]Incremental False Negative Detection for Contrastive Learning
• [cs.CV]Large-scale Unsupervised Semantic Segmentation
• [cs.CV]Learning Dynamics via Graph Neural Networks for Human Pose Estimation and Tracking
• [cs.CV]Learning Topology from Synthetic Data for Unsupervised Depth Completion
• [cs.CV]Learning Video Models from Text: Zero-Shot Anticipation for Procedural Actions
• [cs.CV]MOC-GAN: Mixing Objects and Captions to Generate Realistic Images
• [cs.CV]Making CNNs Interpretable by Building Dynamic Sequential Decision Forests with Top-down Hierarchy Learning
• [cs.CV]Mean-Shifted Contrastive Loss for Anomaly Detection
• [cs.CV]Multi-Camera Vehicle Counting Using Edge-AI
• [cs.CV]Multi-Exit Semantic Segmentation Networks
• [cs.CV]Multi-Level Graph Encoding with Structural-Collaborative Relation Learning for Skeleton-Based Person Re-Identification
• [cs.CV]Multi-Target Domain Adaptation with Collaborative Consistency Learning
• [cs.CV]NTIRE 2021 Challenge on Burst Super-Resolution: Methods and Results
• [cs.CV]Neural Implicit 3D Shapes from Single Images with Spatial Patterns
• [cs.CV]Occlusion-aware Unsupervised Learning of Depth from 4-D Light Fields
• [cs.CV]Open source disease analysis system of cactus by artificial intelligence and image processing
• [cs.CV]Oriented Object Detection with Transformer
• [cs.CV]Patch Slimming for Efficient Vision Transformers
• [cs.CV]Person Re-Identification with a Locally Aware Transformer
• [cs.CV]Points2Polygons: Context-Based Segmentation from Weak Labels Using Adversarial Networks
• [cs.CV]RDA: Robust Domain Adaptation via Fourier Adversarial Attacking
• [cs.CV]Radar-Camera Pixel Depth Association for Depth Completion
• [cs.CV]Recovery Analysis for Plug-and-Play Priors using the Restricted Eigenvalue Condition
• [cs.CV]Reducing the feature divergence of RGB and near-infrared images using Switchable Normalization
• [cs.CV]Referring Transformer: A One-step Approach to Multi-task Visual Grounding
• [cs.CV]Refiner: Refining Self-attention for Vision Transformers
• [cs.CV]Region-aware Adaptive Instance Normalization for Image Harmonization
• [cs.CV]Resolution learning in deep convolutional networks using scale-space theory
• [cs.CV]Rethinking Training from Scratch for Object Detection
• [cs.CV]Reveal of Vision Transformers Robustness against Adversarial Attacks
• [cs.CV]SADRNet: Self-Aligned Dual Face Regression Networks for Robust 3D Dense Face Alignment and Reconstruction
• [cs.CV]SIMONe: View-Invariant, Temporally-Abstracted Object Representations via Unsupervised Video Decomposition
• [cs.CV]Self-Damaging Contrastive Learning
• [cs.CV]Self-Supervision & Meta-Learning for One-Shot Unsupervised Cross-Domain Detection
• [cs.CV]Self-supervised Depth Estimation Leveraging Global Perception and Geometric Smoothness Using On-board Videos
• [cs.CV]SelfDoc: Self-Supervised Document Representation Learning
• [cs.CV]Semi-Supervised Domain Adaptation via Adaptive and Progressive Feature Alignment
• [cs.CV]Shape As Points: A Differentiable Poisson Solver
• [cs.CV]Shuffle Transformer: Rethinking Spatial Shuffle for Vision Transformer
• [cs.CV]Source-Free Open Compound Domain Adaptation in Semantic Segmentation
• [cs.CV]Spectral Temporal Graph Neural Network for Trajectory Prediction
• [cs.CV]T-Net: Deep Stacked Scale-Iteration Network for Image Dehazing
• [cs.CV]Technical Report: Temporal Aggregate Representations
• [cs.CV]The Distance Transform and its Computation
• [cs.CV]Transformed ROIs for Capturing Visual Transformations in Videos
• [cs.CV]Transformer in Convolutional Neural Networks
• [cs.CV]Uformer: A General U-Shaped Transformer for Image Restoration
• [cs.CV]Unsupervised Action Segmentation for Instructional Videos
• [cs.CV]Unsupervised Learning for Cuboid Shape Abstraction via Joint Segmentation from Point Clouds
• [cs.CV]Using GANs to Augment Data for Cloud Image Segmentation Task
• [cs.CV]ViTAE: Vision Transformer Advanced by Exploring Intrinsic Inductive Bias
• [cs.CV]Video Imprint
• [cs.CV]Video Instance Segmentation using Inter-Frame Communication Transformers
• [cs.CV]Visual Search Asymmetry: Deep Nets and Humans Share Similar Inherent Biases
• [cs.CV]Visual Transformer for Task-aware Active Learning
• [cs.CV]Web based disease prediction and recommender system
• [cs.CV]Wide-Baseline Relative Camera Pose Estimation with Directional Learning
• [cs.CV]supervised adptive threshold network for instance segmentation
• [cs.CY]Algorithms and Decision-Making in the Public Sector
• [cs.CY]Corona Health — A Study- and Sensor-based Mobile App Platform Exploring Aspects of the COVID-19 Pandemic
• [cs.CY]Smart Village: An IoT Based Digital Transformation
• [cs.DC]Analyzing Open-Source Serverless Platforms: Characteristics and Performance
• [cs.DC]Energy-Efficient Naming in Beeping Networks
• [cs.DC]Experience Report: Writing A Portable GPU Runtime with OpenMP 5.1
• [cs.DC]KupenStack: Kubernetes based Cloud Native OpenStack
• [cs.DC]ModelCI-e: Enabling Continual Learning in Deep Learning Serving Systems
• [cs.DC]PAIO: A Software-Defined Storage Data Plane Framework
• [cs.DC]Tight Lower Bounds for the RMR Complexity of Recoverable Mutual Exclusion
• [cs.DL]Meta-research on COVID-19: An overview of the early trends
• [cs.DS]How to Decompose a Tensor with Group Structure
• [cs.DS]Local Algorithms for Estimating Effective Resistance
• [cs.DS]Numerical Composition of Differential Privacy
• [cs.DS]Parallel Batch-Dynamic -Core Decomposition
• [cs.DS]SILVAN: Estimating Betweenness Centralities with Progressive Sampling and Non-uniform Rademacher Bounds
• [cs.DS]Sparsification for Sums of Exponentials and its Algorithmic Applications
• [cs.DS]Time-Optimal Sublinear Algorithms for Matching and Vertex Cover
• [cs.FL]Free-Choice Nets With Home Clusters Are Lucent
• [cs.GR]Deep Medial Fields
• [cs.GT]Forward Looking Best-Response Multiplicative Weights Update Methods
• [cs.GT]Neural Auction: End-to-End Learning of Auction Mechanisms for E-Commerce Advertising
• [cs.GT]On the Design of Strategic Task Recommendations for Sustainable Crowdsourcing-Based Content Moderation
• [cs.GT]PreferenceNet: Encoding Human Preferences in Auction Design with Deep Learning
• [cs.HC]Real-Time Cognitive Evaluation of Online Learners through Automatically Generated Questions
• [cs.IR]A Semi-Personalized System for User Cold Start Recommendation on Music Streaming Apps
• [cs.IR]A novel method for recommendation systems using invasive weed optimization
• [cs.IR]Auditing Source Diversity Bias in Video Search Results Using Virtual Agents
• [cs.IR]Bidirectional Distillation for Top-K Recommender System
• [cs.IR]Big-Five, MPTI, Eysenck or HEXACO: The Ideal Personality Model for Personality-aware Recommendation Systems
• [cs.IR]DMBGN: Deep Multi-Behavior Graph Networks for Voucher Redemption Rate Prediction
• [cs.IR]Dual Attentive Sequential Learning for Cross-Domain Click-Through Rate Prediction
• [cs.IR]Leveraging Tripartite Interaction Information from Live Stream E-Commerce for Improving Product Recommendation
• [cs.IR]PURS: Personalized Unexpected Recommender System for Improving User Satisfaction
• [cs.IR]Pre-trained Language Model for Web-scale Retrieval in Baidu Search
• [cs.IR]Scientific Dataset Discovery via Topic-level Recommendation
• [cs.IR]Socially-Aware Self-Supervised Tri-Training for Recommendation
• [cs.IT]A Stochastic Model for Block Segmentation of Images Based on the Quadtree and the Bayes Code for It
• [cs.IT]Antenna Array Diagnosis for Millimeter-Wave MIMO Systems
• [cs.IT]Beamforming and Transmit Power Design for Intelligent Reconfigurable Surface-aided Secure Spatial Modulation
• [cs.IT]Contact Tracing Information Improves the Performance of Group Testing Algorithms
• [cs.IT]Dynamic Resource Configuration for Low-Power IoT Networks: A Multi-Objective Reinforcement Learning Method
• [cs.IT]Joint Design for Simultaneously Transmitting And Reflecting (STAR) RIS Assisted NOMA Systems
• [cs.IT]Low-complexity Voronoi shaping for the Gaussian channel
• [cs.IT]Neural Distributed Source Coding
• [cs.IT]On Perceptual Lossy Compression: The Cost of Perceptual Reconstruction and An Optimal Training Framework
• [cs.IT]On the Dual of Generalized Bent Functions
• [cs.IT]On the Skew-Symmetric Binary Sequences and the Merit Factor Problem
• [cs.IT]Optimal Transmit Power and Antenna Selection to Achieve Energy Efficient and Low Complexity in fifth generation Massive MIMO Systems
• [cs.IT]Principle Bit Analysis: Autoencoding with Schur-Concave Loss
• [cs.IT]Rack-Aware Regenerating Codes with Multiple Erasure Tolerance
• [cs.IT]Relay Selection and Resource Allocation for Ultra-Reliable Uplink Transmission in Smart Factory Scenarios
• [cs.IT]Robust Resource Allocation for Multi-Antenna URLLC-OFDMA Systems in a Smart Factory
• [cs.IT]Study of Multi-Branch Tomlinson-Harashima Precoding with Multiple-Antenna Systems and Rate Splitting
• [cs.IT]The Computational and Latency Advantage of Quantum Communication Networks
• [cs.IT]The Convexity and Concavity of Envelopes of the Minimum-Relative-Entropy Region for the DSBS
• [cs.LG]A Physics-Informed Deep Learning Paradigm for Traffic State Estimation and Fundamental Diagram Discovery
• [cs.LG]A Primer on Multi-Neuron Relaxation-based Adversarial Robustness Certification
• [cs.LG]A Variational Perspective on Diffusion-Based Generative Models and Score Matching
• [cs.LG]A call for better unit testing for invariant risk minimisation
• [cs.LG]A novel Deep Neural Network architecture for non-linear system identification
• [cs.LG]Accelerating Stochastic Simulation with Interactive Neural Processes
• [cs.LG]Adversarial Classification of the Attacks on Smart Grids Using Game Theory and Deep Learning
• [cs.LG]Adversarially Regularized Graph Attention Networks for Inductive Learning on Partially Labeled Graphs
• [cs.LG]An Information-theoretic Approach to Distribution Shifts
• [cs.LG]Antipodes of Label Differential Privacy: PATE and ALIBI
• [cs.LG]Asymmetric Loss Functions for Learning with Noisy Labels
• [cs.LG]Automation for Interpretable Machine Learning Through a Comparison of Loss Functions to Regularisers
• [cs.LG]Average-Reward Reinforcement Learning with Trust Region Methods
• [cs.LG]Bandwidth-based Step-Sizes for Non-Convex Stochastic Optimization
• [cs.LG]Beyond Bandit Feedback in Online Multiclass Classification
• [cs.LG]Boosting a Model Zoo for Multi-Task and Continual Learning
• [cs.LG]CAPE: Encoding Relative Positions with Continuous Augmented Positional Embeddings
• [cs.LG]Can Subnetwork Structure be the Key to Out-of-Distribution Generalization?
• [cs.LG]Causal Influence Detection for Improving Efficiency in Reinforcement Learning
• [cs.LG]Churn Reduction via Distillation
• [cs.LG]Collaborative Causal Discovery with Atomic Interventions
• [cs.LG]Commutative Lie Group VAE for Disentanglement Learning
• [cs.LG]Complexity Analysis of Stein Variational Gradient Descent Under Talagrand’s Inequality T1
• [cs.LG]Concave Utility Reinforcement Learning: the Mean-field Game viewpoint
• [cs.LG]Conditional Contrastive Learning: Removing Undesirable Information in Self-Supervised Representations
• [cs.LG]Constrained Generalized Additive 2 Model with Consideration of High-Order Interactions
• [cs.LG]Context-Aware Sparse Deep Coordination Graphs
• [cs.LG]Continual Active Learning for Efficient Adaptation of Machine Learning Models to Changing Image Acquisition
• [cs.LG]Control-Oriented Model-Based Reinforcement Learning with Implicit Differentiation
• [cs.LG]Counterfactual Maximum Likelihood Estimation for Training Deep Networks
• [cs.LG]DAMSL: Domain Agnostic Meta Score-based Learning
• [cs.LG]DL-DDA — Deep Learning based Dynamic Difficulty Adjustment with UX and Gameplay constraints
• [cs.LG]DSelect-k: Differentiable Selection in the Mixture of Experts with Applications to Multi-Task Learning
• [cs.LG]Deep Canonical Correlation Alignment for Sensor Signals
• [cs.LG]Deep Neural Network Modeling of Unknown Partial Differential Equations in Nodal Space
• [cs.LG]Differentially Private Multi-Armed Bandits in the Shuffle Model
• [cs.LG]Distributed Learning and its Application for Time-Series Prediction
• [cs.LG]Distributional Reinforcement Learning with Unconstrained Monotonic Neural Networks
• [cs.LG]Efficient Continuous Control with Double Actors and Regularized Critics
• [cs.LG]Efficient Lottery Ticket Finding: Less Data is More
• [cs.LG]Enabling On-Device Self-Supervised Contrastive Learning With Selective Data Contrast
• [cs.LG]Energy Aligning for Biased Models
• [cs.LG]Energy-Based Learning for Cooperative Games, with Applications to Feature/Data/Model Valuations
• [cs.LG]Ensemble Defense with Data Diversity: Weak Correlation Implies Strong Robustness
• [cs.LG]Equivariant Graph Neural Networks for 3D Macromolecular Structure
• [cs.LG]Error Loss Networks
• [cs.LG]Escaping Saddle Points Faster with Stochastic Momentum
• [cs.LG]Exploring the Limits of Out-of-Distribution Detection
• [cs.LG]Exposing the Implicit Energy Networks behind Masked Language Models via Metropolis—Hastings
• [cs.LG]Extracting Weighted Automata for Approximate Minimization in Language Modelling
• [cs.LG]FedNL: Making Newton-Type Methods Applicable to Federated Learning
• [cs.LG]FlexParser — the adaptive log file parser for continuous results in a changing world
• [cs.LG]Forced Variational Integrator Networks for Prediction and Control of Mechanical Systems
• [cs.LG]Formalizing Distribution Inference Risks
• [cs.LG]GAN Cocktail: mixing GANs without dataset access
• [cs.LG]Generative Adversarial Networks: A Survey Towards Private and Secure Applications
• [cs.LG]Graph Belief Propagation Networks
• [cs.LG]Graph Infomax Adversarial Learning for Treatment Effect Estimation with Networked Observational Data
• [cs.LG]Graph Neural Networks in Network Neuroscience
• [cs.LG]Graph2Graph Learning with Conditional Autoregressive Models
• [cs.LG]GraphMI: Extracting Private Graph Data from Graph Neural Networks
• [cs.LG]Heuristic-Guided Reinforcement Learning
• [cs.LG]High-Dimensional Bayesian Optimisation with Variational Autoencoders and Deep Metric Learning
• [cs.LG]HoroPCA: Hyperbolic Dimensionality Reduction via Horospherical Projections
• [cs.LG]Identifiability in inverse reinforcement learning
• [cs.LG]ImGAGN:Imbalanced Network Embedding via Generative Adversarial Graph Networks
• [cs.LG]Increase and Conquer: Training Graph Neural Networks on Growing Graphs
• [cs.LG]Instrument Space Selection for Kernel Maximum Moment Restriction
• [cs.LG]Integrating Auxiliary Information in Self-supervised Learning
• [cs.LG]Integrating Expert ODEs into Neural ODEs: Pharmacology and Disease Progression
• [cs.LG]Layered gradient accumulation and modular pipeline parallelism: fast and efficient training of large language models
• [cs.LG]Learnable Fourier Features for Multi-DimensionalSpatial Positional Encoding
• [cs.LG]Learning Combinatorial Node Labeling Algorithms
• [cs.LG]Learning MDPs from Features: Predict-Then-Optimize for Sequential Decision Problems by Reinforcement Learning
• [cs.LG]Learning Routines for Effective Off-Policy Reinforcement Learning
• [cs.LG]Learning Stochastic Optimal Policies via Gradient Descent
• [cs.LG]Learning proofs for the classification of nilpotent semigroups
• [cs.LG]Learning stable reduced-order models for hybrid twins
• [cs.LG]Learning to Efficiently Sample from Diffusion Probabilistic Models
• [cs.LG]Learning without Knowing: Unobserved Context in Continuous Transfer Reinforcement Learning
• [cs.LG]Local Disentanglement in Variational Auto-Encoders Using Jacobian Regularization
• [cs.LG]Low Budget Active Learning via Wasserstein Distance: An Integer Programming Approach
• [cs.LG]Making EfficientNet More Efficient: Exploring Batch-Independent Normalization, Group Convolutions and Reduced Resolution Training
• [cs.LG]Measuring Generalization with Optimal Transport
• [cs.LG]MemStream: Memory-Based Anomaly Detection in Multi-Aspect Streams with Concept Drift
• [cs.LG]Meta-Learning Reliable Priors in the Function Space
• [cs.LG]Mitigating Covariate Shift in Imitation Learning via Offline Data Without Great Coverage
• [cs.LG]MixRL: Data Mixing Augmentation for Regression using Reinforcement Learning
• [cs.LG]Multi-armed Bandit Requiring Monotone Arm Sequences
• [cs.LG]Multi-chart flows
• [cs.LG]Multi-facet Contextual Bandits: A Neural Network Perspective
• [cs.LG]Neural Active Learning with Performance Guarantees
• [cs.LG]On Learning to Rank Long Sequences with Contextual Bandits
• [cs.LG]On Local Aggregation in Heterophilic Graphs
• [cs.LG]On Memorization in Probabilistic Deep Generative Models
• [cs.LG]On Training Sample Memorization: Lessons from Benchmarking Generative Modeling with a Large-scale Competition
• [cs.LG]On the Expressive Power of Self-Attention Matrices
• [cs.LG]On the Power of Shallow Learning
• [cs.LG]On the Role of Entropy-based Loss for Learning Causal Structures with Continuous Optimization
• [cs.LG]OoD-Bench: Benchmarking and Understanding Out-of-Distribution Generalization Datasets and Algorithms
• [cs.LG]PAC Best Arm Identification Under a Deadline
• [cs.LG]PID-GAN: A GAN Framework based on a Physics-informed Discriminator for Uncertainty Quantification with Physics
• [cs.LG]Parameter-free Statistically Consistent Interpolation: Dimension-independent Convergence Rates for Hilbert kernel regression
• [cs.LG]PcDGAN: A Continuous Conditional Diverse Generative Adversarial Network For Inverse Design
• [cs.LG]Photonic Differential Privacy with Direct Feedback Alignment
• [cs.LG]Preservation of the Global Knowledge by Not-True Self Knowledge Distillation in Federated Learning
• [cs.LG]Proxy-Normalizing Activations to Match Batch Normalization while Removing Batch Dependence
• [cs.LG]Quantifying and Improving Transferability in Domain Generalization
• [cs.LG]Robust Implicit Networks via Non-Euclidean Contractions
• [cs.LG]SNR optimization of multi-span fiber optic communication systems employing EDFAs with non-flat gain and noise figure
• [cs.LG]Same State, Different Task: Continual Reinforcement Learning without Interference
• [cs.LG]Scalable Computation of Monge Maps with General Costs
• [cs.LG]ScheduleNet: Learn to solve multi-agent scheduling problems with reinforcement learning
• [cs.LG]Securing Secure Aggregation: Mitigating Multi-Round Privacy Leakage in Federated Learning
• [cs.LG]Self-Supervised Graph Learning with Proximity-based Views and Channel Contrast
• [cs.LG]Self-supervised Rubik’s Cube Solver
• [cs.LG]Semi-Riemannian Graph Convolutional Networks
• [cs.LG]SizeFlags: Reducing Size and Fit Related Returns in Fashion E-Commerce
• [cs.LG]Smoothness-Aware Quantization Techniques
• [cs.LG]SoftDICE for Imitation Learning: Rethinking Off-policy Distribution Matching
• [cs.LG]Solving hybrid machine learning tasks by traversing weight space geodesics
• [cs.LG]Stability of Manifold Neural Networks to Deformations
• [cs.LG]Stateful Strategic Regression
• [cs.LG]Sum of Ranked Range Loss for Supervised Learning
• [cs.LG]Tabular Data: Deep Learning is Not All You Need
• [cs.LG]TabularNet: A Neural Network Architecture for Understanding Semantic Structures of Tabular Data
• [cs.LG]Tensor Normal Training for Deep Learning Models
• [cs.LG]The Fine-Grained Hardness of Sparse Linear Regression
• [cs.LG]The Power of Exploiter: Provable Multi-Agent RL in Large State Spaces
• [cs.LG]Top-KAST: Top-K Always Sparse Training
• [cs.LG]Topological Measurement of Deep Neural Networks Using Persistent Homology
• [cs.LG]Towards robust and domain agnostic reinforcement learning competitions
• [cs.LG]Training Robust Graph Neural Networks with Topology Adaptive Edge Dropping
• [cs.LG]Understand and Improve Contrastive Learning Methods for Visual Representation: A Review
• [cs.LG]Vanishing Curvature and the Power of Adaptive Methods in Randomly Initialized Deep Networks
• [cs.LG]Variational Leakage: The Role of Information Complexity in Privacy Leakage
• [cs.LG]Zero-Shot Knowledge Distillation from a Decision-Based Black-Box Model
• [cs.LG]k-Mixup Regularization for Deep Learning via Optimal Transport
• [cs.NE]MoleHD: Automated Drug Discovery using Brain-Inspired Hyperdimensional Computing
• [cs.NE]One-shot learning of paired associations by a reservoir computing model with Hebbian plasticity
• [cs.NE]SpikePropamine: Differentiable Plasticity in Spiking Neural Networks
• [cs.NE]Subject Independent Emotion Recognition using EEG Signals Employing Attention Driven Neural Networks
• [cs.NI]Immediate Proximity Detection Using Wi-Fi-Enabled Smartphones
• [cs.NI]The Four Levels of Fixed-Points in Mean-Field Models
• [cs.RO]A Split-face Study of Novel Robotic Prototype vs Human Operator in Skin Rejuvenation Using Q-switched Nd:Yag Laser: Accuracy, Efficacy and Safety
• [cs.RO]Brno Urban Dataset: Winter Extention
• [cs.RO]Collective transport via sequential caging
• [cs.RO]Cost-effective Mapping of Mobile Robot Based on the Fusion of UWB and Short-range 2D LiDAR
• [cs.RO]Design of hazard based model and collision avoidance system
• [cs.RO]Distributed Task Allocation in Homogeneous Swarms Using Language Measure Theory
• [cs.RO]FACT: A Full-body Ad-hoc Collaboration Test
1bea
bed for Modeling Complex Teamwork
• [cs.RO]Hierarchical Robot Navigation in Novel Environments using Rough 2-D Maps
• [cs.RO]Inferring Objectives in Continuous Dynamic Games from Noise-Corrupted Partial State Observations
• [cs.RO]Motion Planning Transformers: One Model to Plan Them All
• [cs.RO]Multi-goal path planning using multiple random trees
• [cs.RO]Negotiation-Aware Reachability-Based Safety Verification for AutonomousDriving in Interactive Scenarios
• [cs.RO]On Healthcare Robots: Concepts, definitions, and considerations for healthcare robot governance
• [cs.RO]PYROBOCOP : Python-based Robotic Control & Optimization Package for Manipulation and Collision Avoidance
• [cs.RO]Planning Multimodal Exploratory Actions for Online Robot Attribute Learning
• [cs.RO]Real-time Identification and Tuning of Multirotors Based on Deep Neural Networks for Accurate Trajectory Tracking Under Wind Disturbances
• [cs.RO]Robotic Electrospinning Actuated by Non-Circular Joint Continuum Manipulator for Endoluminal Therapy
• [cs.RO]Stein ICP for Uncertainty Estimation in Point Cloud Matching
• [cs.RO]Terrain Adaptive Gait Transitioning for a Quadruped Robot using Model Predictive Control
• [cs.RO]Towards a Multi-purpose Robotic Nursing Assistant
• [cs.RO]Trajectory Optimization of Chance-Constrained Nonlinear Stochastic Systems for Motion Planning and Control
• [cs.RO]Tunable Trajectory Planner Using G3 Curves
• [cs.SD]Active Speaker Detection as a Multi-Objective Optimization with Uncertainty-based Multimodal Fusion
• [cs.SE]Clone-Seeker: Effective Code Clone Search Using Annotations
• [cs.SE]Deterministic Iteratively Built KD-Tree with KNN Search for Exact Applications
• [cs.SE]Discovery of Layered Software Architecture from Source Code Using Ego Networks
• [cs.SE]Redefining measures of Layered Architecture
• [cs.SE]Understanding Neural Code Intelligence Through Program Simplification
• [cs.SI]A Generative Node-attribute Network Model for Detecting Generalized Structure
• [cs.SI]A Pre-training Oracle for Predicting Distances in Social Networks
• [cs.SI]Assessing Attendance by Peer Information
• [cs.SI]DyDiff-VAE: A Dynamic Variational Framework for Information Diffusion Prediction
• [cs.SI]Faster and Generalized Temporal Triangle Counting, via Degeneracy Ordering
• [cs.SI]IM-META: Influence Maximization Using Node Metadata in Networks With Unknown Topology
• [cs.SI]Network Inference and Influence Maximization from Samples
• [cs.SI]Popularity is linked to neural coordination: Neural evidence for an Anna Karenina principle in social networks
• [cs.SI]The spreading potential problem
• [econ.EM]On the “mementum” of Meme Stocks
• [eess.AS]An Attribute-Aligned Strategy for Learning Speech Representation
• [eess.AS]Meta-StyleSpeech : Multi-Speaker Adaptive Text-to-Speech Generation
• [eess.AS]Unsupervised Clustered Federated Learning in Complex Multi-source Acoustic Environments
• [eess.AS]Weakly-supervised word-level pronunciation error detection in non-native English speech
• [eess.IV]A Deep Variational Bayesian Framework for Blind Image Deblurring
• [eess.IV]AOSLO-net: A deep learning-based method for automatic segmentation of retinal microaneurysms from adaptive optics scanning laser ophthalmoscope images
• [eess.IV]Brain Age Estimation From MRI Using Cascade Networks with Ranking Loss
• [eess.IV]Deep Learning-based Type Identification of Volumetric MRI Sequences
• [eess.IV]Deep Neural Network-based Enhancement for Image and Video Streaming Systems: A Survey and Future Directions
• [eess.IV]Hierarchical Temperature Imaging Using Pseudo-Inversed Convolutional Neural Network Aided TDLAS Tomography
• [eess.IV]Knowledge-aware Deep Framework for Collaborative Skin Lesion Segmentation and Melanoma Recognition
• [eess.IV]Pointwise visual field estimation from optical coherence tomography in glaucoma: a structure-function analysis using deep learning
• [eess.SP]3D UAV Trajectory and Data Collection Optimisation via Deep Reinforcement Learning
• [eess.SP]Closed-Loop Wireless Power Transfer with Adaptive Waveform and Beamforming: Design, Prototype, and Experiment
• [eess.SP]Deep Unfolding of Iteratively Reweighted ADMM for Wireless RF Sensing
• [eess.SP]Machine Learning Based Anxiety Detection in Older Adults using Wristband Sensors and Context Feature
• [eess.SY]Controller Synthesis for Omega-Regular and Steady-State Specifications
• [eess.SY]Effect of Adaptive and Fixed Shared Steering Control on Distracted Driver Behavior
• [eess.SY]Multi-armed Bandit Algorithms on System-on-Chip: Go Frequentist or Bayesian?
• [eess.SY]Singular Dynamic Mode Decompositions
• [math.
93e
AG]Neurons on Amoebae
• [math.AC]Learning a performance metric of Buchberger’s algorithm
• [math.OC]Decentralized Optimization with Heterogeneous Delays: a Continuous-Time Approach
• [math.OC]Dynamics of Stochastic Momentum Methods on Large-scale, Quadratic Models
• [math.OC]MURANA: A Generic Framework for Stochastic Variance-Reduced Optimization
• [math.OC]Minibatch and Momentum Model-based Methods for Stochastic Non-smooth Non-convex Optimization
• [math.OC]On the Optimality of Backward Regression: Sparse Recovery and Subset Selection
• [math.OC]Random features for adaptive nonlinear control and prediction
• [math.OC]Signatured Deep Fictitious Play for Mean Field Games with Common Noise
• [math.ST]A sparse model with covariates for directed networks
• [math.ST]Confidence bands for exponential distribution functions under progressive type-II censoring
• [math.ST]Semiparametric inference on Gini indices of two semicontinuous populations under density ratio models
• [math.ST]Superconsistency of tests in high dimensions
• [math.ST]Tempered Stable Autoregressive Models
• [math.ST]The basic distributional theory for the product of zero mean correlated normal random variables
• [math.ST]Towards Practical Mean Bounds for Small Samples
• [q-bio.NC]A Computational Model of Representation Learning in the Brain Cortex, Integrating Unsupervised and Reinforcement Learning
• [q-bio.NC]Neural dSCA: demixing multimodal interaction among brain areas during naturalistic experiments
• [q-bio.PE]The evolving usefulness of the Test-Negative Design in studying risk factors for COVID-19 due to changes in testing policy
• [q-fin.TR]Online Trading Models in the Forex Market Considering Transaction Costs
• [quant-ph]A Review of Machine Learning Classification Using Quantum Annealing for Real-world Applications
• [quant-ph]Predicting Quantum Potentials by Deep Neural Network and Metropolis Sampling
• [quant-ph]The Inductive Bias of Quantum Kernels
• [stat.AP]Bayesian Time Varying Coefficient Model with Applications to Marketing Mix Modeling
• [stat.AP]Connection between forest fire emission and COVID-19 incidents in West Coast regions of the United States
• [stat.AP]Estimating the number of entities with vacancies using administrative and online data
• [stat.AP]Latent class growth analysis for ordinal response data in the Distress Assessment and Response Tool: an evaluation of state-of-the-art implementations
• [stat.AP]Odds Ratios are far from “portable”: A call to use realistic models for effect variation in meta-analysis
• [stat.AP]Proper Scoring Rules for Missing Value Imputation
• [stat.ME]A consistent nonparametric test of the effect of dementia duration on mortality
• [stat.ME]A multivariate Gaussian random field prior against spatial confounding
• [stat.ME]Bayesian graphical modelling for heterogeneous causal effects
• [stat.ME]Causal aggregation: estimation and inference of causal effects by constraint-based data fusion
• [stat.ME]Cluster Analysis via Random Partition Distributions
• [stat.ME]Estimating the size of a closed population by modeling latent and observed heterogeneity
• [stat.ME]Fisher-Pitman permutation tests based on nonparametric Poisson mixtures with application to single cell genomics
• [stat.ME]Hierarchical Bayesian Mixture Models for Time Series Using Context Trees as State Space Partitions
• [stat.ME]High-dimensional Bayesian model selection by proximal nested sampling
• [stat.ME]Hypothesis Testing for Hierarchical Structures in Cognitive Diagnosis Models
• [stat.ME]Joint Learning of Multiple Differential Networks with fMRI data for Brain Connectivity Alteration Detection
• [stat.ME]Modeling Nonstationary Time Series using Locally Stationary Basis Processes
• [stat.ME]Parameter Estimation for Grouped Data Using EM and MCEM Algorithms
• [stat.ME]Safe Tests and Always-Valid Confidence Intervals for contingency tables and beyond
• [stat.ME]Seemingly Unrelated Multi-State processes: a Bayesian semiparametric approach
• [stat.ME]Semi-Supervised Statistical Inference for High-Dimensional Linear Regression with Blockwise Missing Data
• [stat.ME]Simultaneous Confidence Corridors for Mean Functions in Functional Data Analysis of Imaging Data
• [stat.ME]Statistical Inference for Cox Proportional Hazards Models with a Diverging Number of Covariates
• [stat.ME]Statistical summaries of unlabelled evolutionary trees and ranked hierarchical clustering trees
• [stat.ML]A Distance Covariance-based Kernel for Nonlinear Causal Clustering in Heterogeneous Populations
• [stat.ML]Accurate and robust Shapley Values for explaining predictions and focusing on local important variables
• [stat.ML]BayesIMP: Uncertainty Quantification for Causal Data Fusion
• [stat.ML]Calibrating multi-dimensional complex ODE from noisy data via deep neural networks
• [stat.ML]Can a single neuron learn quantiles?
• [stat.ML]Causal Bandits with Unknown Graph Structure
• [stat.ML]Data-driven discovery of interacting particle systems using Gaussian processes
• [stat.ML]Deep Particulate Matter Forecasting Model Using Correntropy-Induced Loss
• [stat.ML]Evaluating State-of-the-Art Classification Models Against Bayes Optimality
• [stat.ML]Fast and Robust Online Inference with Stochastic Gradient Descent via Random Scaling
• [stat.ML]Generalized Linear Bandits with Local Differential Privacy
• [stat.ML]Heavy Tails in SGD and Compressibility of Overparametrized Neural Networks
• [stat.ML]How Tight Can PAC-Bayes be in the Small Data Regime?
• [stat.ML]How to Evaluate Uncertainty Estimates in Machine Learning for Regression?
• [stat.ML]Improved Predictive Uncertainty using Corruption-based Calibration
• [stat.ML]Learning Curves for SGD on Structured Features
• [stat.ML]Learning Gaussian Mixtures with Generalised Linear Models: Precise Asymptotics in High-dimensions
• [stat.ML]Learning Treatment Effects in Panels with General Intervention Patterns
• [stat.ML]Multivariate Probabilistic Regression with Natural Gradient Boosting
• [stat.ML]Navigating to the Best Policy in Markov Decision Processes
• [stat.ML]Network Estimation by Mixing: Adaptivity and More
• [stat.ML]Neural Tangent Kernel Maximum Mean Discrepancy
• [stat.ML]On Inductive Biases for Heterogeneous Treatment Effect Estimation
• [stat.ML]Regularization in ResNet with Stochastic Depth
• [stat.ML]Representation mitosis in wide neural networks
• [stat.ML]Robust Stochastic Linear Contextual Bandits Under Adversarial Attacks
• [stat.ML]Syndicated Bandits: A Framework for Auto Tuning Hyper-parameters in Contextual Bandit Algorithms
• [stat.ML]Towards an Understanding of Benign Overfitting in Neural Networks
• [stat.ML]Unbiased Self-Play

·····································

• [astro-ph.IM]Application of neural networks to classification of data of the TUS orbital telescope
Mikhail Zotov
http://arxiv.org/abs/2106.03361v1

• [astro-ph.IM]The effect of phased recurrent units in the classification of multiple catalogs of astronomical lightcurves
C. Donoso-Oliva, G. Cabrera-Vives, P. Protopapas, R. Carrasco-Davis, P. A. Estevez
http://arxiv.org/abs/2106.03736v1

• [astro-ph.IM]Training Strategies for Deep Learning Gravitational-Wave Searches
Marlin B. Schäfer, Ondřej Zelenka, Alexander H. Nitz, Frank Ohme, Bernd Brügmann
http://arxiv.org/abs/2106.03741v1

• [cond-mat.dis-nn]The topological Dirac equation of networks and simplicial complexes
Ginestra Bianconi
http://arxiv.org/abs/2106.02929v1

• [cond-mat.mtrl-sci]Inverse design of two-dimensional materials with invertible neural networks
Victor Fung, Jiaxin Zhang, Guoxiang Hu, P. Ganesh, Bobby G. Sumpter
http://arxiv.org/abs/2106.03013v1

• [cs.AI]A generative model for molecule generation based on chemical reaction trees
Dai Hai Nguyen, Koji Tsuda
http://arxiv.org/abs/2106.03394v1

• [cs.AI]Believe What You See: Implicit Constraint Approach for Offline Multi-Agent Reinforcement Learning
Yiqin Yang, Xiaoteng Ma, Chenghao Li, Zewu Zheng, Qiyuan Zhang, Gao Huang, Jun Yang, Qianchuan Zhao
http://arxiv.org/abs/2106.03400v1

• [cs.AI]Causal Abstractions of Neural Networks
Atticus Geiger, Hanson Lu, Thomas Icard, Christopher Potts
http://arxiv.org/abs/2106.02997v1

• [cs.AI]Empirically Evaluating Creative Arc Negotiation for Improvisational Decision-making
Mikhail Jacob, Brian Magerko
http://arxiv.org/abs/2106.02921v1

• [cs.AI]End-to-End Neuro-Symbolic Architecture for Image-to-Image Reasoning Tasks
Ananye Agarwal, Pradeep Shenoy, Mausam
http://arxiv.org/abs/2106.03121v1

• [cs.AI]Explainable Artificial Intelligence (XAI) for Increasing User Trust in Deep Reinforcement Learning Driven Autonomous Systems
Jeff Druce, Michael Harradon, James Tittle
http://arxiv.org/abs/2106.03775v1

• [cs.AI]Extending counterfactual accounts of intent to include oblique intent
Hal Ashton
http://arxiv.org/abs/2106.03684v1

• [cs.AI]Hierarchical Task Learning from Language Instructions with Unified Transformers and Self-Monitoring
Yichi Zhang, Joyce Chai
http://arxiv.org/abs/2106.03427v1

• [cs.AI]Multi-modal Entity Alignment in Hyperbolic Space
Hao Guo, Jiuyang Tang, Weixin Zeng, Xiang Zhao, Li Liu
http://arxiv.org/abs/2106.03619v1

• [cs.AI]Path-specific Effects Based on Information Accounts of Causality
Heyang Gong, Ke Zhu
http://arxiv.org/abs/2106.03178v1

• [cs.AI]Reinforcement Learning for Assignment Problem with Time Constraints
Sharmin Pathan, Vyom Shrivastava
http://arxiv.org/abs/2106.02856v1

• [cs.AI]Uncertain Process Data with Probabilistic Knowledge: Problem Characterization and Challenges
Izack Cohen, Avigdor Gal
http://arxiv.org/abs/2106.03324v1

• [cs.AI]What if we Increase the Number of Objectives? Theoretical and Empirical Implications for Many-objective Optimization
Richard Allmendinger, Andrzej Jaszkiewicz, Arnaud Liefooghe, Christiane Tammer
http://arxiv.org/abs/2106.03275v1

• [cs.AI]Zero-shot Task Adaptation using Natural Language
Prasoon Goyal, Raymond J. Mooney, Scott Niekum
http://arxiv.org/abs/2106.02972v1

• [cs.CL]A Comprehensive Assessment of Dialog Evaluation Metrics
Yi-Ting Yeh, Maxine Eskenazi, Shikib Mehri
http://arxiv.org/abs/2106.03706v1

• [cs.CL]A Globally Normalized Neural Model for Semantic Parsing
Chenyang Huang, Wei Yang, Yanshuai Cao, Osmar Zaïane, Lili Mou
http://arxiv.org/abs/2106.03376v1

• [cs.CL]A Joint Model for Dropped Pronoun Recovery and Conversational Discourse Parsing in Chinese Conversational Speech
Jingxuan Yang, Kerui Xu, Jun Xu, Si Li, Sheng Gao, Jun Guo, Nianwen Xue, Ji-Rong Wen
http://arxiv.org/abs/2106.03345v1

• [cs.CL]A Simple Recipe for Multilingual Grammatical Error Correction
Sascha Rothe, Jonathan Mallinson, Eric Malmi, Sebastian Krause, Aliaksei Severyn
http://arxiv.org/abs/2106.03830v1

• [cs.CL]A Targeted Assessment of Incremental Processing in Neural LanguageModels and Humans
Ethan Gotlieb Wilcox, Pranali Vani, Roger P. Levy
http://arxiv.org/abs/2106.03232v1

• [cs.CL]Apurinã Universal Dependencies Treebank
Jack Rueter, Marília Fernanda Pereira de Freitas, Sidney da Silva Facundes, Mika Hämäläinen, Niko Partanen
http://arxiv.org/abs/2106.03391v1

• [cs.CL]Attend and Select: A Segment Attention based Selection Mechanism for Microblog Hashtag Generation
Qianren Mao, Xi Li, Hao Peng, Bang Liu, Shu Guo, Jianxin Li, Lihong Wang, Philip S. Yu
http://arxiv.org/abs/2106.03151v1

• [cs.CL]Attention Temperature Matters in Abstractive Summarization Distillation
Shengqiang Zhang, Xingxing Zhang, Hangbo Bao, Furu Wei
http://arxiv.org/abs/2106.03441v1

• [cs.CL]BERTGEN: Multi-task Generation through BERT
Faidon Mitzalis, Ozan Caglayan, Pranava Madhyastha, Lucia Specia
http://arxiv.org/abs/2106.03484v1

• [cs.CL]BERTnesia: Investigating the capture and forgetting of knowledge in BERT
Jonas Wallat, Jaspreet Singh, Avishek Anand
http://arxiv.org/abs/2106.02902v1

• [cs.CL]BiToD: A Bilingual Multi-Domain Dataset For Task-Oriented Dialogue Modeling
Zhaojiang Lin, Andrea Madotto, Genta Indra Winata, Peng Xu, Feijun Jiang, Yuxiang Hu, Chen Shi, Pascale Fung
http://arxiv.org/abs/2106.02787v1

• [cs.CL]CAiRE in DialDoc21: Data Augmentation for Information-Seeking Dialogue System
Etsuko Ishii, Yan Xu, Genta Indra Winata, Zhaojiang Lin, Andrea Madotto, Zihan Liu, Peng Xu, Pascale Fung
http://arxiv.org/abs/2106.03530v1

• [cs.CL]COVID-Fact: Fact Extraction and Verification of Real-World Claims on COVID-19 Pandemic
Arkadiy Saakyan, Tuhin Chakrabarty, Smaranda Muresan
http://arxiv.org/abs/2106.03794v1

• [cs.CL]Combining Static Word Embeddings and Contextual Representations for Bilingual Lexicon Induction
Jinpeng Zhang, Baijun Ji, Nini Xiao, Xiangyu Duan, Min Zhang, Yangbin Shi, Weihua Luo
http://arxiv.org/abs/2106.03084v1

• [cs.CL]Deep Context- and Relation-Aware Learning for Aspect-based Sentiment Analysis
Shinhyeok Oh, Dongyub Lee, Taesun Whang, IlNam Park, Gaeun Seo, EungGyun Kim, Harksoo Kim
http://arxiv.org/abs/2106.03806v1

• [cs.CL]Denoising Word Embeddings by Averaging in a Shared Space
Avi Caciularu, Ido Dagan, Jacob Goldberger
http://arxiv.org/abs/2106.02954v1

• [cs.CL]Diverse Pretrained Context Encodings Improve Document Translation
Domenic Donato, Lei Yu, Chris Dyer
http://arxiv.org/abs/2106.03717v1

• [cs.CL]Diversity driven Query Rewriting in Search Advertising
Akash Kumar Mohankumar, Nikit Begwani, Amit Singh
http://arxiv.org/abs/2106.03816v1

• [cs.CL]Do Grammatical Error Correction Models Realize Grammatical Generalization?
Masato Mita, Hitomi Yanaka
http://arxiv.org/abs/2106.03031v1

• [cs.CL]Document-level Relation Extraction as Semantic Segmentation
Ningyu Zhang, Xiang Chen, Xin Xie, Shumin Deng, Shumin Deng, Mosha Chen, Fei Huang, Luo Si, Huajun Chen
http://arxiv.org/abs/2106.03618v1

• [cs.CL]Embracing Ambiguity: Shifting the Training Target of NLI Models
Johannes Mario Meissner, Napat Thumwanit, Saku Sugawara, Akiko Aizawa
http://arxiv.org/abs/2106.03020v1

• [cs.CL]Emergent Communication of Generalizations
Jesse Mu, Noah Goodman
http://arxiv.org/abs/2106.02668v1

• [cs.CL]Emotion-aware Chat Machine: Automatic Emotional Response Generation for Human-like Emotional Interaction
Wei Wei, Jiayi Liu, Xianling Mao, Guibing Guo, Feida Zhu, Pan Zhou, Yuchong Hu
http://arxiv.org/abs/2106.03044v1

• [cs.CL]Empowering Language Understanding with Counterfactual Reasoning
Fuli Feng, Jizhi Zhang, Xiangnan He, Hanwang Zhang, Tat-Seng Chua
http://arxiv.org/abs/2106.03046v1

• [cs.CL]Encouraging Neural Machine Translation to Satisfy Terminology Constraints
Melissa Ailem, Jinghsu Liu, Raheel Qader
http://arxiv.org/abs/2106.03730v1

• [cs.CL]Enhancing Label Correlation Feedback in Multi-Label Text Classification via Multi-Task Learning
Ximing Zhang, Qian-Wen Zhang, Zhao Yan, Ruifang Liu, Yunbo Cao
http://arxiv.org/abs/2106.03103v1

• [cs.CL]Enhancing Taxonomy Completion with Concept Generation via Fusing Relational Representations
Qingkai Zeng, Jinfeng Lin, Wenhao Yu, Jane Cleland-Huang, Meng Jiang
http://arxiv.org/abs/2106.02974v1

• [cs.CL]Extractive Research Slide Generation Using Windowed Labeling Ranking
Athar Sefid, Jian Wu, Prasenjit Mitra, Lee Giles
http://arxiv.org/abs/2106.03246v1

• [cs.CL]GTM: A Generative Triple-Wise Model for Conversational Question Generation
Lei Shen, Fandong Meng, Jinchao Zhang, Yang Feng, Jie Zhou
http://arxiv.org/abs/2106.03635v1

• [cs.CL]Generating Relevant and Coherent Dialogue Responses using Self-separated Conditional Variational AutoEncoders
Bin Sun, Shaoxiong Feng, Yiwei Li, Jiamou Liu, Kan Li
http://arxiv.org/abs/2106.03410v1

• [cs.CL]How Did This Get Funded?! Automatically Identifying Quirky Scientific Achievements
Chen Shani, Nadav Borenstein, Dafna Shahaf
http://arxiv.org/abs/2106.03048v1

• [cs.CL]Identifying Populist Paragraphs in Text: A machine-learning approach
Jogilė Ulinkskaitė, Lukas Pukelis
http://arxiv.org/abs/2106.03161v1

• [cs.CL]Improving Automated Evaluation of Open Domain Dialog via Diverse Reference Augmentation
Varun Gangal, Harsh Jhamtani, Eduard Hovy, Taylor Berg-Kirkpatrick
http://arxiv.org/abs/2106.02833v1

• [cs.CL]Itihasa: A large-scale corpus for Sanskrit to English translation
Rahul Aralikatte, Miryam de Lhoneux, Anoop Kunchukuttan, Anders Søgaard
http://arxiv.org/abs/2106.03269v1

• [cs.CL]LAWDR: Language-Agnostic Weighted Document Representations from Pre-trained Models
Hongyu Gong, Vishrav Chaudhary, Yuqing Tang, Francisco Guzmán
http://arxiv.org/abs/2106.03379v1

• [cs.CL]Let’s be explicit about that: Distant supervision for implicit discourse relation classification via connective prediction
Murathan Kurfalı, Robert Östling
http://arxiv.org/abs/2106.03192v1

• [cs.CL]Lexical Semantic Change Discovery
Sinan Kurtyigit, Maike Park, Dominik Schlechtweg, Jonas Kuhn, Sabine Schulte im Walde
http://arxiv.org/abs/2106.03111v1

• [cs.CL]Lifelong Learning of Hate Speech Classification on Social Media
Jing Qian, Hong Wang, Mai ElSherief, Xifeng Yan
http://arxiv.org/abs/2106.02821v1

• [cs.CL]MergeDistill: Merging Pre-trained Language Models using Distillation
Simran Khanuja, Melvin Johnson, Partha Talukdar
http://arxiv.org/abs/2106.02834v1

• [cs.CL]Meta-Learning with Variational Semantic Memory for Word Sense Disambiguation
Yingjun Du, Nithin Holla, Xiantong Zhen, Cees G. M. Snoek, Ekaterina Shutova
http://arxiv.org/abs/2106.02960v1

• [cs.CL]Meta-learning for downstream aware and agnostic pretraining
Hongyin Luo, Shuyan Dong, Yung-Sung Chuang, Shang-Wen Li
http://arxiv.org/abs/2106.03270v1

• [cs.CL]MultiOpEd: A Corpus of Multi-Perspective News Editorials
Siyi Liu, Sihao Chen, Xander Uyttendaele, Dan Roth
http://arxiv.org/abs/2106.02725v1

• [cs.CL]Multilingual Neural Semantic Parsing for Low-Resourced Languages
Menglin Xia, Emilio Monti
http://arxiv.org/abs/2106.03469v1

• [cs.CL]Narrative Question Answering with Cutting-Edge Open-Domain QA Techniques: A Comprehensive Study
Xiangyang Mou, Chenghao Yang, Mo Yu, Bingsheng Yao, Xiaoxiao Guo, Saloni Potdar, Hui Su
http://arxiv.org/abs/2106.03826v1

• [cs.CL]Never guess what I heard… Rumor Detection in Finnish News: a Dataset and a Baseline
Mika Hämäläinen, Khalid Alnajjar, Niko Partanen, Jack Rueter
http://arxiv.org/abs/2106.03389v1

• [cs.CL]On the Effectiveness of Adapter-based Tuning for Pretrained Language Model Adaptation
Ruidan He, Linlin Liu, Hai Ye, Qingyu Tan, Bosheng Ding, Liying Cheng, Jia-Wei Low, Lidong Bing, Luo Si
http://arxiv.org/abs/2106.03164v1

• [cs.CL]On the Language Coverage Bias for Neural Machine Translation
Shuo Wang, Zhaopeng Tu, Zhixing Tan, Shuming Shi, Maosong Sun, Yang Liu
http://arxiv.org/abs/2106.03297v1

• [cs.CL]PROST: Physical Reasoning of Objects through Space and Time
Stéphane Aroca-Ouellette, Cory Paik, Alessandro Roncone, Katharina Kann
http://arxiv.org/abs/2106.03634v1

• [cs.CL]Position Bias Mitigation: A Knowledge-Aware Graph Model for EmotionCause Extraction
Hanqi Yan, Lin Gui, Gabriele Pergola, Yulan He
http://arxiv.org/abs/2106.03518v1

• [cs.CL]RedditBias: A Real-World Resource for Bias Evaluation and Debiasing of Conversational Language Models
Soumya Barikeri, Anne Lauscher, Ivan Vulić, Goran Glavaš
http://arxiv.org/abs/2106.03521v1

• [cs.CL]Relative Importance in Sentence Processing
Nora Hollenstein, Lisa Beinborn
http://arxiv.org/abs/2106.03471v1

• [cs.CL]RoSearch: Search for Robust Student Architectures When Distilling Pre-trained Language Models
Xin Guo, Jianlei Yang, Haoyi Zhou, Xucheng Ye, Jianxin Li
http://arxiv.org/abs/2106.03613v1

• [cs.CL]Semantic and Syntactic Enhanced Aspect Sentiment Triplet Extraction
Zhexue Chen, Hong Huang, Bang Liu, Xuanhua Shi, Hai Jin
http://arxiv.org/abs/2106.03315v1

• [cs.CL]Semantic-Enhanced Explainable Finetuning for Open-Domain Dialogues
Chen Henry Wu, Yinhe Zheng, Yida Wang, Zhenyu Yang, Minlie Huang
http://arxiv.org/abs/2106.03065v1

• [cs.CL]Structured Reordering for Modeling Latent Alignments in Sequence Transduction
Bailin Wang, Mirella Lapata, Ivan Titov
http://arxiv.org/abs/2106.03257v1

• [cs.CL]Summary Grounded Conversation Generation
Chulaka Gunasekara, Guy Feigenblat, Benjamin Sznajder, Sachindra Joshi, David Konopnicki
http://arxiv.org/abs/2106.03337v1

• [cs.CL]The FLORES-101 Evaluation Benchmark for Low-Resource and Multilingual Machine Translation
Naman Goyal, Cynthia Gao, Vishrav Chaudhary, Peng-Jen Chen, Guillaume Wenzek, Da Ju, Sanjana Krishnan, Marc’Aurelio Ranzato, Francisco Guzman, Angela Fan
http://arxiv.org/abs/2106.03193v1

• [cs.CL]The R-U-A-Robot Dataset: Helping Avoid Chatbot Deception by Detecting User Questions About Human or Non-Human Identity
David Gros, Yu Li, Zhou Yu
http://arxiv.org/abs/2106.02692v1

• [cs.CL]Transient Chaos in BERT
Katsuma Inoue, Soh Ohara, Yasuo Kuniyoshi, Kohei Nakajima
http://arxiv.org/abs/2106.03181v1

• [cs.CL]Unsupervised Representation Disentanglement of Text: An Evaluation on Synthetic Datasets
Lan Zhang, Victor Prokhorov, Ehsan Shareghi
http://arxiv.org/abs/2106.03631v1

• [cs.CL]W-RST: Towards a Weighted RST-style Discourse Framework
Patrick Huber, Wen Xiao, Giuseppe Carenini
http://arxiv.org/abs/2106.02658v1

• [cs.CL]Weakly-Supervised Methods for Suicide Risk Assessment: Role of Related Domains
Chenghao Yang, Yudong Zhang, Smaranda Muresan
http://arxiv.org/abs/2106.02792v1

• [cs.CL]X2Parser: Cross-Lingual and Cross-Domain Framework for Task-Oriented Compositional Semantic Parsing
Zihan Liu, Genta Indra Winata, Peng Xu, Pascale Fung
http://arxiv.org/abs/2106.03777v1

• [cs.CR]Sensor Fusion-based GNSS Spoofing Attack Detection Framework for Autonomous Vehicles
Sagar Dasgupta, Mizanur Rahman, Mhafuzul Islam, Mashrur Chowdhury
http://arxiv.org/abs/2106.02982v1

• [cs.CR]Tetrad: Actively Secure 4PC for Secure Training and Inference
Nishat Koti, Arpita Patra, Rahul Rachuri, Ajith Suresh
http://arxiv.org/abs/2106.02850v1

• [cs.CV]3D Convolution Neural Network based Person Identification using Gait cycles
Ravi Shekhar Tiwari, Supraja P, Rijo Jackson Tom
http://arxiv.org/abs/2106.03136v1

• [cs.CV]3DB: A Framework for Debugging Computer Vision Models
Guillaume Leclerc, Hadi Salman, Andrew Ilyas, Sai Vemprala, Logan Engstrom, Vibhav Vineet, Kai Xiao, Pengchuan Zhang, Shibani Santurkar, Greg Yang, Ashish Kapoor, Aleksander Madry
http://arxiv.org/abs/2106.03805v1

• [cs.CV]A Comprehensive Survey on Image Dehazing Based on Deep Learning
Jie Gui, Xiaofeng Cong, Yuan Cao, Wenqi Ren, Jun Zhang, Jing Zhang, Dacheng Tao
http://arxiv.org/abs/2106.03323v1

• [cs.CV]Adversarial Attack and Defense in Deep Ranking
Mo Zhou, Le Wang, Zhenxing Niu, Qilin Zhang, Nanning Zheng, Gang Hua
http://arxiv.org/abs/2106.03614v1

• [cs.CV]Alpha Matte Generation from Single Input for Portrait Matting
Dogucan Yaman, Hazım Kemal Ekenel, Alexander Waibel
http://arxiv.org/abs/2106.03210v1

• [cs.CV]An Adaptive Framework for Learning Unsupervised Depth Completion
Alex Wong, Xiaohan Fei, Byung-Woo Hong, Stefano Soatto
http://arxiv.org/abs/2106.03010v1

• [cs.CV]An End-to-End Breast Tumour Classification Model Using Context-Based Patch Modelling- A BiLSTM Approach for Image Classification
Suvidha Tripathi, Satish Kumar Singh, Hwee Kuan Lee
http://arxiv.org/abs/2106.02864v1

• [cs.CV]Bias Mitigation of Face Recognition Models Through Calibration
Tiago Salvador, Stephanie Cairns, Vikram Voleti, Noah Marshall, Adam Oberman
http://arxiv.org/abs/2106.03761v1

• [cs.CV]CDN-MEDAL: Two-stage Density and Difference Approximation Framework for Motion Analysis
Synh Viet-Uyen Ha, Cuong Tien Nguyen, Hung Ngoc Phan, Nhat Minh Chung, Phuong Hoai Ha
http://arxiv.org/abs/2106.03776v1

• [cs.CV]Category Contrast for Unsupervised Domain Adaptation in Visual Tasks
Jiaxing Huang, Dayan Guan, Aoran Xiao, Shijian Lu
http://arxiv.org/abs/2106.02885v1

• [cs.CV]Channel DropBlock: An Improved Regularization Method for Fine-Grained Visual Classification
Yifeng Ding, Shuwei Dong, Yujun Tong, Zhanyu Ma, Bo Xiao, Haibin Ling
http://arxiv.org/abs/2106.03432v1

• [cs.CV]Combinatorial Optimization for Panoptic Segmentation: An End-to-End Trainable Approach
Ahmed Abbas, Paul Swoboda
http://arxiv.org/abs/2106.03188v1

• [cs.CV]Contextual Guided Segmentation Framework for Semi-supervised Video Instance Segmentation
Trung-Nghia Le, Tam V. Nguyen, Minh-Triet Tran
http://arxiv.org/abs/2106.03330v1

• [cs.CV]ContourRender: Detecting Arbitrary Contour Shape For Instance Segmentation In One Pass
Tutian Tang, Wenqiang Xu, Ruolin Ye, Yan-Feng Wang, Cewu Lu
http://arxiv.org/abs/2106.03382v1

• [cs.CV]Convolutional Neural Networks with Gated Recurrent Connections
Jianfeng Wang, Xiaolin Hu
http://arxiv.org/abs/2106.02859v1

• [cs.CV]DINs: Deep Interactive Networks for Neurofibroma Segmentation in Neurofibromatosis Type 1 on Whole-Body MRI
Jian-Wei Zhang, Wei Chen, K. Ina Ly, Xubin Zhang, Fan Yan, Justin Jordan, Gordon Harris, Scott Plotkin, Pengyi Hao, Wenli Cai
http://arxiv.org/abs/2106.03388v1

• [cs.CV]Deep Learning 3D Dose Prediction for Conventional Lung IMRT Using Consistent/Unbiased Automated Plans
Navdeep Dahiya, Gourav Jhanwar, Anthony Yezzi, Masoud Zarepisheh, Saad Nadeem
http://arxiv.org/abs/2106.03705v1

• [cs.CV]Deep Matching Prior: Test-Time Optimization for Dense Correspondence
Sunghwan Hong, Seungryong Kim
http://arxiv.org/abs/2106.03090v1

• [cs.CV]Digital Taxonomist: Identifying Plant Species in Citizen Scientists’ Photographs
Riccardo de Lutio, Yihang She, Stefano D’Aronco, Stefania Russo, Philipp Brun, Jan D. Wegner, Konrad Schindler
http://arxiv.org/abs/2106.03774v1

• [cs.CV]DoubleField: Bridging the Neural Surface and Radiance Fields for High-fidelity Human Rendering
Ruizhi Shao, Hongwen Zhang, He Zhang, Yanpei Cao, Tao Yu, Yebin Liu
http://arxiv.org/abs/2106.03798v1

• [cs.CV]Dynamic Resolution Network
Mingjian Zhu, Kai Han, Enhua Wu, Qiulin Zhang, Ying Nie, Zhenzhong Lan, Yunhe Wang
http://arxiv.org/abs/2106.02898v1

• [cs.CV]Efficient Iterative Amortized Inference for Learning Symmetric and Disentangled Multi-Object Representations
Patrick Emami, Pan He, Sanjay Ranka, Anand Rangarajan
http://arxiv.org/abs/2106.03630v1

• [cs.CV]Efficient Training of Visual Transformers with Small-Size Datasets
Yahui Liu, Enver Sangineto, Wei Bi, Nicu Sebe, Bruno Lepri, Marco De Nadai
http://arxiv.org/abs/2106.03746v1

• [cs.CV]Efficient training for future video generation based on hierarchical disentangled representation of latent variables
Naoya Fushishita, Antonio Tejero-de-Pablos, Yusuke Mukuta, Tatsuya Harada
http://arxiv.org/abs/2106.03502v1

• [cs.CV]End-to-end reconstruction meets data-driven regularization for inverse problems
Subhadip Mukherjee, Marcello Carioni, Ozan Öktem, Carola-Bibiane Schönlieb
http://arxiv.org/abs/2106.03538v1

• [cs.CV]Exploiting Emotional Dependencies with Graph Convolutional Networks for Facial Expression Recognition
Panagiotis Antoniadis, Panagiotis P. Filntisis, Petros Maragos
http://arxiv.org/abs/2106.03487v1

• [cs.CV]Exploring to establish an appropriate model for mage aesthetic assessment via CNN-based RSRL: An empirical study
Ying Dai
http://arxiv.org/abs/2106.03316v1

• [cs.CV]FINet: Dual Branches Feature Interaction for Partial-to-Partial Point Cloud Registration
Hao Xu, Nianjin Ye, Shuaicheng Liu, Guanghui Liu, Bing Zeng
http://arxiv.org/abs/2106.03479v1

• [cs.CV]Feature Flow Regularization: Improving Structured Sparsity in Deep Neural Networks
Yue Wu, Yuan Lan, Luchan Zhang, Yang Xiang
http://arxiv.org/abs/2106.02914v1

• [cs.CV]Feature-based Style Randomization for Domain Generalization
Yue Wang, Lei Qi, Yinghuan Shi, Yang Gao
http://arxiv.org/abs/2106.03171v1

• [cs.CV]Few-Shot Unsupervised Image-to-Image Translation on complex scenes
Luca Barras, Samuel Chassot, Daniel Filipe Nunes Silva
http://arxiv.org/abs/2106.03770v1

• [cs.CV]Few-shot segmentation of medical images based on meta-learning with implicit gradients
Rabindra Khadga, Debesh Jha, Sharib Ali, Steven Hicks, Vajira Thambawita, Michael A. Riegler, Pål Halvorsen
http://arxiv.org/abs/2106.03223v1

• [cs.CV]Go with the Flows: Mixtures of Normalizing Flows for Point Cloud Generation and Reconstruction
Janis Postels, Mengya Liu, Riccardo Spezialetti, Luc Van Gool, Federico Tombari
http://arxiv.org/abs/2106.03135v1

• [cs.CV]HERS Superpixels: Deep Affinity Learning for Hierarchical Entropy Rate Segmentation
Hankui Peng, Angelica I. Aviles-Rivero, Carola-Bibiane Schonlieb
http://arxiv.org/abs/2106.03755v1

• [cs.CV]High Resolution Solar Image Generation using Generative Adversarial Networks
Ankan Dash, Junyi Ye, Guiling Wang
http://arxiv.org/abs/2106.03814v1

• [cs.CV]Highlighting the Importance of Reducing Research Bias and Carbon Emissions in CNNs
Ahmed Badar, Arnav Varma, Adrian Staniec, Mahmoud Gamal, Omar Magdy, Haris Iqbal, Elahe Arani, Bahram Zonooz
http://arxiv.org/abs/2106.03242v1

• [cs.CV]IPS300+: a Challenging Multimodal Dataset for Intersection Perception System
Huana
b61
n Wang, Xinyu Zhang, Jun Li, Zhiwei Li, Lei Yang, Shuyue Pan, Yongqiang Deng
http://arxiv.org/abs/2106.02781v1

• [cs.CV]Incremental False Negative Detection for Contrastive Learning
Tsai-Shien Chen, Wei-Chih Hung, Hung-Yu Tseng, Shao-Yi Chien, Ming-Hsuan Yang
http://arxiv.org/abs/2106.03719v1

• [cs.CV]Large-scale Unsupervised Semantic Segmentation
Shang-Hua Gao, Zhong-Yu Li, Ming-Hsuan Yang, Ming-Ming Cheng, Junwei Han, Philip Torr
http://arxiv.org/abs/2106.03149v1

• [cs.CV]Learning Dynamics via Graph Neural Networks for Human Pose Estimation and Tracking
Yiding Yang, Zhou Ren, Haoxiang Li, Chunluan Zhou, Xinchao Wang, Gang Hua
http://arxiv.org/abs/2106.03772v1

• [cs.CV]Learning Topology from Synthetic Data for Unsupervised Depth Completion
Alex Wong, Safa Cicek, Stefano Soatto
http://arxiv.org/abs/2106.02994v1

• [cs.CV]Learning Video Models from Text: Zero-Shot Anticipation for Procedural Actions
Fadime Sener, Rishabh Saraf, Angela Yao
http://arxiv.org/abs/2106.03158v1

• [cs.CV]MOC-GAN: Mixing Objects and Captions to Generate Realistic Images
Tao Ma, Yikang Li
http://arxiv.org/abs/2106.03128v1

• [cs.CV]Making CNNs Interpretable by Building Dynamic Sequential Decision Forests with Top-down Hierarchy Learning
Yilin Wang, Shaozuo Yu, Xiaokang Yang, Wei Shen
http://arxiv.org/abs/2106.02824v1

• [cs.CV]Mean-Shifted Contrastive Loss for Anomaly Detection
Tal Reiss, Yedid Hoshen
http://arxiv.org/abs/2106.03844v1

• [cs.CV]Multi-Camera Vehicle Counting Using Edge-AI
Luca Ciampi, Claudio Gennaro, Fabio Carrara, Fabrizio Falchi, Claudio Vairo, Giuseppe Amato
http://arxiv.org/abs/2106.02842v1

• [cs.CV]Multi-Exit Semantic Segmentation Networks
Alexandros Kouris, Stylianos I. Venieris, Stefanos Laskaridis, Nicholas D. Lane
http://arxiv.org/abs/2106.03527v1

• [cs.CV]Multi-Level Graph Encoding with Structural-Collaborative Relation Learning for Skeleton-Based Person Re-Identification
Haocong Rao, Shihao Xu, Xiping Hu, Jun Cheng, Bin Hu
http://arxiv.org/abs/2106.03069v1

• [cs.CV]Multi-Target Domain Adaptation with Collaborative Consistency Learning
Takashi Isobe, Xu Jia, Shuaijun Chen, Jianzhong He, Yongjie Shi, Jianzhuang Liu, Huchuan Lu, Shengjin Wang
http://arxiv.org/abs/2106.03418v1

• [cs.CV]NTIRE 2021 Challenge on Burst Super-Resolution: Methods and Results
Goutam Bhat, Martin Danelljan, Radu Timofte, Kazutoshi Akita, Wooyeong Cho, Haoqiang Fan, Lanpeng Jia, Daeshik Kim, Bruno Lecouat, Youwei Li, Shuaicheng Liu, Ziluan Liu, Ziwei Luo, Takahiro Maeda, Julien Mairal, Christian Micheloni, Xuan Mo, Takeru Oba, Pavel Ostyakov, Jean Ponce, Sanghyeok Son, Jian Sun, Norimichi Ukita, Rao Muhammad Umer, Youliang Yan, Lei Yu, Magauiya Zhussip, Xueyi Zou
http://arxiv.org/abs/2106.03839v1

• [cs.CV]Neural Implicit 3D Shapes from Single Images with Spatial Patterns
Yixin Zhuang, Yunzhe Liu, Baoquan Chen
http://arxiv.org/abs/2106.03087v1

• [cs.CV]Occlusion-aware Unsupervised Learning of Depth from 4-D Light Fields
Jing Jin, Junhui Hou
http://arxiv.org/abs/2106.03043v1

• [cs.CV]Open source disease analysis system of cactus by artificial intelligence and image processing
Kanlayanee Kaweesinsakul, Siranee Nuchitprasitchai, Joshua M. Pearce
http://arxiv.org/abs/2106.03669v1

• [cs.CV]Oriented Object Detection with Transformer
Teli Ma, Mingyuan Mao, Honghui Zheng, Peng Gao, Xiaodi Wang, Shumin Han, Errui Ding, Baochang Zhang, David Doermann
http://arxiv.org/abs/2106.03146v1

• [cs.CV]Patch Slimming for Efficient Vision Transformers
Yehui Tang, Kai Han, Yunhe Wang, Chang Xu, Jianyuan Guo, Chao Xu, Dacheng Tao
http://arxiv.org/abs/2106.02852v1

• [cs.CV]Person Re-Identification with a Locally Aware Transformer
Charu Sharma, Siddhant R. Kapil, David Chapman
http://arxiv.org/abs/2106.03720v1

• [cs.CV]Points2Polygons: Context-Based Segmentation from Weak Labels Using Adversarial Networks
Kuai Yu, Hakeem Frank, Daniel Wilson
http://arxiv.org/abs/2106.02804v1

• [cs.CV]RDA: Robust Domain Adaptation via Fourier Adversarial Attacking
Jiaxing Huang, Dayan Guan, Aoran Xiao, Shijian Lu
http://arxiv.org/abs/2106.02874v1

• [cs.CV]Radar-Camera Pixel Depth Association for Depth Completion
Yunfei Long, Daniel Morris, Xiaoming Liu, Marcos Castro, Punarjay Chakravarty, Praveen Narayanan
http://arxiv.org/abs/2106.02778v1

• [cs.CV]Recovery Analysis for Plug-and-Play Priors using the Restricted Eigenvalue Condition
Jiaming Liu, M. Salman Asif, Brendt Wohlberg, Ulugbek S. Kamilov
http://arxiv.org/abs/2106.03668v1

• [cs.CV]Reducing the feature divergence of RGB and near-infrared images using Switchable Normalization
Siwei Yang, Shaozuo Yu, Bingchen Zhao, Yin Wang
http://arxiv.org/abs/2106.03088v1

• [cs.CV]Referring Transformer: A One-step Approach to Multi-task Visual Grounding
Muchen Li, Leonid Sigal
http://arxiv.org/abs/2106.03089v1

• [cs.CV]Refiner: Refining Self-attention for Vision Transformers
Daquan Zhou, Yujun Shi, Bingyi Kang, Weihao Yu, Zihang Jiang, Yuan Li, Xiaojie Jin, Qibin Hou, Jiashi Feng
http://arxiv.org/abs/2106.03714v1

• [cs.CV]Region-aware Adaptive Instance Normalization for Image Harmonization
Jun Ling, Han Xue, Li Song, Rong Xie, Xiao Gu
http://arxiv.org/abs/2106.02853v1

• [cs.CV]Resolution learning in deep convolutional networks using scale-space theory
Silvia L. Pintea, Nergis Tomen, Stanley F. Goes, Marco Loog, Jan C. van Gemert
http://arxiv.org/abs/2106.03412v1

• [cs.CV]Rethinking Training from Scratch for Object Detection
Yang Li, Hong Zhang, Yu Zhang
http://arxiv.org/abs/2106.03112v1

• [cs.CV]Reveal of Vision Transformers Robustness against Adversarial Attacks
Ahmed Aldahdooh, Wassim Hamidouche, Olivier Deforges
http://arxiv.org/abs/2106.03734v1

• [cs.CV]SADRNet: Self-Aligned Dual Face Regression Networks for Robust 3D Dense Face Alignment and Reconstruction
Zeyu Ruan, Changqing Zou, Longhai Wu, Gangshan Wu, Limin Wang
http://arxiv.org/abs/2106.03021v1

• [cs.CV]SIMONe: View-Invariant, Temporally-Abstracted Object Representations via Unsupervised Video Decomposition
Rishabh Kabra, Daniel Zoran, Goker Erdogan, Loic Matthey, Antonia Creswell, Matthew Botvinick, Alexander Lerchner, Christopher P. Burgess
http://arxiv.org/abs/2106.03849v1

• [cs.CV]Self-Damaging Contrastive Learning
Ziyu Jiang, Tianlong Chen, Bobak Mortazavi, Zhangyang Wang
http://arxiv.org/abs/2106.02990v1

• [cs.CV]Self-Supervision & Meta-Learning for One-Shot Unsupervised Cross-Domain Detection
F. Cappio Borlino, S. Polizzotto, A. D’Innocente, S. Bucci, B. Caputo, T. Tommasi
http://arxiv.org/abs/2106.03496v1

• [cs.CV]Self-supervised Depth Estimation Leveraging Global Perception and Geometric Smoothness Using On-board Videos
Shaocheng Jia, Xin Pei, Wei Yao, S. C. Wong
http://arxiv.org/abs/2106.03505v1

• [cs.CV]SelfDoc: Self-Supervised Document Representation Learning
Peizhao Li, Jiuxiang Gu, Jason Kuen, Vlad I. Morariu, Handong Zhao, Rajiv Jain, Varun Manjunatha, Hongfu Liu
http://arxiv.org/abs/2106.03331v1

• [cs.CV]Semi-Supervised Domain Adaptation via Adaptive and Progressive Feature Alignment
Jiaxing Huang, Dayan Guan, Aoran Xiao, Shijian Lu
http://arxiv.org/abs/2106.02845v1

• [cs.CV]Shape As Points: A Differentiable Poisson Solver
Songyou Peng, Chiyu “Max” Jiang, Yiyi Liao, Michael Niemeyer, Marc Pollefeys, Andreas Geiger
http://arxiv.org/abs/2106.03452v1

• [cs.CV]Shuffle Transformer: Rethinking Spatial Shuffle for Vision Transformer
Zilong Huang, Youcheng Ben, Guozhong Luo, Pei Cheng, Gang Yu, Bin Fu
http://arxiv.org/abs/2106.03650v1

• [cs.CV]Source-Free Open Compound Domain Adaptation in Semantic Segmentation
Yuyang Zhao, Zhun Zhong, Zhiming Luo, Gim Hee Lee, Nicu Sebe
http://arxiv.org/abs/2106.03422v1

• [cs.CV]Spectral Temporal Graph Neural Network for Trajectory Prediction
Defu Cao, Jiachen Li, Hengbo Ma, Masayoshi Tomizuka
http://arxiv.org/abs/2106.02930v1

• [cs.CV]T-Net: Deep Stacked Scale-Iteration Network for Image Dehazing
Lirong Zheng, Yanshan Li, Kaihao Zhang, Wenhan Luo
http://arxiv.org/abs/2106.02809v1

• [cs.CV]Technical Report: Temporal Aggregate Representations
Fadime Sener, Dibyadip Chatterjee, Angela Yao
http://arxiv.org/abs/2106.03152v1

• [cs.CV]The Distance Transform and its Computation
Tilo Strutz
http://arxiv.org/abs/2106.03503v1

• [cs.CV]Transformed ROIs for Capturing Visual Transformations in Videos
Abhinav Rai, Fadime Sener, Angela Yao
http://arxiv.org/abs/2106.03162v1

• [cs.CV]Transformer in Convolutional Neural Networks
Yun Liu, Guolei Sun, Yu Qiu, Le Zhang, Ajad Chhatkuli, Luc Van Gool
http://arxiv.org/abs/2106.03180v1

• [cs.CV]Uformer: A General U-Shaped Transformer for Image Restoration
Zhendong Wang, Xiaodong Cun, Jianmin Bao, Jianzhuang Liu
http://arxiv.org/abs/2106.03106v1

• [cs.CV]Unsupervised Action Segmentation for Instructional Videos
AJ Piergiovanni, Anelia Angelova, Michael S. Ryoo, Irfan Essa
http://arxiv.org/abs/2106.03738v1

• [cs.CV]Unsupervised Learning for Cuboid Shape Abstraction via Joint Segmentation from Point Clouds
Kaizhi Yang, Xuejin Chen
http://arxiv.org/abs/2106.03437v1

• [cs.CV]Using GANs to Augment Data for Cloud Image Segmentation Task
Mayank Jain, Conor Meegan, Soumyabrata Dev
http://arxiv.org/abs/2106.03064v1

• [cs.CV]ViTAE: Vision Transformer Advanced by Exploring Intrinsic Inductive Bias
Yufei Xu, Qiming Zhang, Jing Zhang, Dacheng Tao
http://arxiv.org/abs/2106.03348v1

• [cs.CV]Video Imprint
Zhanning Gao, Le Wang, Nebojsa Jojic, Zhenxing Niu, Nanning Zheng, Gang Hua
http://arxiv.org/abs/2106.03283v1

• [cs.CV]Video Instance Segmentation using Inter-Frame Communication Transformers
Sukjun Hwang, Miran Heo, Seoung Wug Oh, Seon Joo Kim
http://arxiv.org/abs/2106.03299v1

• [cs.CV]Visual Search Asymmetry: Deep Nets and Humans Share Similar Inherent Biases
Shashi Kant Gupta, Mengmi Zhang, Chia-Chien Wu, Jeremy M. Wolfe, Gabriel Kreiman
http://arxiv.org/abs/2106.02953v1

• [cs.CV]Visual Transformer for Task-aware Active Learning
Razvan Caramalau, Binod Bhattarai, Tae-Kyun Kim
http://arxiv.org/abs/2106.03801v1

• [cs.CV]Web based disease prediction and recommender system
Harish Rajora, Narinder Singh Punn, Sanjay Kumar Sonbhadra, Sonali Agarwal
http://arxiv.org/abs/2106.02813v1

• [cs.CV]Wide-Baseline Relative Camera Pose Estimation with Directional Learning
Kefan Chen, Noah Snavely, Ameesh Makadia
http://arxiv.org/abs/2106.03336v1

• [cs.CV]supervised adptive threshold network for instance segmentation
Kuikun Liu, Jie Yang, Cai Sun, Haoyuan Chi
http://arxiv.org/abs/2106.03450v1

• [cs.CY]Algorithms and Decision-Making in the Public Sector
Karen Levy, Kyla E. Chasalow, Sarah Riley
http://arxiv.org/abs/2106.03673v1

• [cs.CY]Corona Health — A Study- and Sensor-based Mobile App Platform Exploring Aspects of the COVID-19 Pandemic
Felix Beierle, Johannes Schobel, Carsten Vogel, Johannes Allgaier, Lena Mulansky, Fabian Haug, Julian Haug, Winfried Schlee, Marc Holfelder, Michael Stach, Marc Schickler, Harald Baumeister, Caroline Cohrdes, Jürgen Deckert, Lorenz Deserno, Johanna-Sophie Edler, Felizitas A. Eichner, Helmut Greger, Grit Hein, Peter Heuschmann, Dennis John, Hans A. Kestler, Dagmar Krefting, Berthold Langguth, Patrick Meybohm, Thomas Probst, Manfred Reichert, Marcel Romanos, Stefan Störk, Yannik Terhorst, Martin Weiß, Rüdiger Pryss
http://arxiv.org/abs/2106.03386v1

• [cs.CY]Smart Village: An IoT Based Digital Transformation
Amit Degada, Himanshu Thapliyal, Saraju P. Mohanty
http://arxiv.org/abs/2106.03750v1

• [cs.DC]Analyzing Open-Source Serverless Platforms: Characteristics and Performance
Junfeng Li, Sameer G. Kulkarni, K. K. Ramakrishnan, Dan Li
http://arxiv.org/abs/2106.03601v1

• [cs.DC]Energy-Efficient Naming in Beeping Networks
Ny Aina Andriambolamalala, Vlady Ravelomanana
http://arxiv.org/abs/2106.03753v1

• [cs.DC]Experience Report: Writing A Portable GPU Runtime with OpenMP 5.1
Shilei Tian, Jon Chesterfield, Johannes Doerfert, Barbara Chapman
http://arxiv.org/abs/2106.03219v1

• [cs.DC]KupenStack: Kubernetes based Cloud Native OpenStack
Parth Yadav, Vipin Kumar Rathi
http://arxiv.org/abs/2106.02956v1

• [cs.DC]ModelCI-e: Enabling Continual Learning in Deep Learning Serving Systems
Yizheng Huang, Huaizheng Zhang, Yonggang Wen, Peng Sun, Nguyen Binh Duong TA
http://arxiv.org/abs/2106.03122v1

• [cs.DC]PAIO: A Software-Defined Storage Data Plane Framework
Ricardo Macedo, Yusuke Tanimura, Jason Haga, Vijay Chidambaram, José Pereira, João Paulo
http://arxiv.org/abs/2106.03617v1

• [cs.DC]Tight Lower Bounds for the RMR Complexity of Recoverable Mutual Exclusion
David Yu Cheng Chan, Philipp Woelfel
http://arxiv.org/abs/2106.03185v1

• [cs.DL]Meta-research on COVID-19: An overview of the early trends
Giovanni Colavizza
http://arxiv.org/abs/2106.02961v1

• [cs.DS]How to Decompose a Tensor with Group Structure
Allen Liu, Ankur Moitra
http://arxiv.org/abs/2106.02680v1

• [cs.DS]Local Algorithms for Estimating Effective Resistance
Pan Peng, Daniel Lopatta, Yuichi Yoshida, Gramoz Goranci
http://arxiv.org/abs/2106.03476v1

• [cs.DS]Numerical Composition of Differential Privacy
Sivakanth Gopi, Yin Tat Lee, Lukas Wutschitz
http://arxiv.org/abs/2106.02848v1

• [cs.DS]Parallel Batch-Dynamic -Core Decomposition
Quanquan C. Liu, Jessica Shi, Shangdi Yu, Laxman Dhulipala, Julian Shun
http://arxiv.org/abs/2106.03824v1

• [cs.DS]SILVAN: Estimating Betweenness Centralities with Progressive Sampling and Non-uniform Rademacher Bounds
Leonardo Pellegrina, Fabio Vandin
http://arxiv.org/abs/2106.03462v1

• [cs.DS]Sparsification for Sums of Exponentials and its Algorithmic Applications
Jerry Li, Allen Liu, Ankur Moitra
http://arxiv.org/abs/2106.02774v1

• [cs.DS]Time-Optimal Sublinear Algorithms for Matching and Vertex Cover
Soheil Behnezhad
http://arxiv.org/abs/2106.02942v1

• [cs.FL]Free-Choice Nets With Home Clusters Are Lucent
Wil M. P. van der Aalst
http://arxiv.org/abs/2106.03554v1

• [cs.GR]Deep Medial Fields
Daniel Rebain, Ke Li, Vincent Sitzmann, Soroosh Yazdani, Kwang Moo Yi, Andrea Tagliasacchi
http://arxiv.org/abs/2106.03804v1

• [cs.GT]Forward Looking Best-Response Multiplicative Weights Update Methods
Michail Fasoulakis, Evangelos Markakis, Yannis Pantazis, Constantinos Varsos
http://arxiv.org/abs/2106.03579v1

• [cs.GT]Neural Auction: End-to-End Learning of Auction Mechanisms for E-Commerce Advertising
Xiangyu Liu, Chuan Yu, Zhilin Zhang, Zhenzhe Zheng, Yu Rong, Hongtao Lv, Da Huo, Yiqing Wang, Dagui Chen, Jian Xu, Fan Wu, Guihai Chen, Xiaoqiang Zhu
http://arxiv.org/abs/2106.03593v1

• [cs.GT]On the Design of Strategic Task Recommendations for Sustainable Crowdsourcing-Based Content Moderation
Sainath Sanga, Venkata Sriram Siddhardh Nadendla
http://arxiv.org/abs/2106.02708v1

• [cs.GT]PreferenceNet: Encoding Human Preferences in Auction Design with Deep Learning
Neehar Peri, Michael J. Curry, Samuel Dooley, John P. Dickerson
http://arxiv.org/abs/2106.03215v1

• [cs.HC]Real-Time Cognitive Evaluation of Online Learners through Automatically Generated Questions
Ritu Gala, Revathi Vijayaraghavan, Valmik Nikam, Arvind Kiwelekar
http://arxiv.org/abs/2106.03036v1

• [cs.IR]A Semi-Personalized System for User Cold Start Recommendation on Music Streaming Apps
Léa Briand, Guillaume Salha-Galvan, Walid Bendada, Mathieu Morlon, Viet-Anh Tran
http://arxiv.org/abs/2106.03819v1

• [cs.IR]A novel method for recommendation systems using invasive weed optimization
Fahimeh Soltaninejad, Amir Jalaly Bidgoly
http://arxiv.org/abs/2106.02831v1

• [cs.IR]Auditing Source Diversity Bias in Video Search Results Using Virtual Agents
Aleksandra Urman, Mykola Makhortykh, Roberto Ulloa
http://arxiv.org/abs/2106.02715v1

• [cs.IR]Bidirectional Distillation for Top-K Recommender System
Wonbin Kweon, SeongKu Kang, Hwanjo Yu
http://arxiv.org/abs/2106.02870v1

• [cs.IR]Big-Five, MPTI, Eysenck or HEXACO: The Ideal Personality Model for Personality-aware Recommendation Systems
Sahraoui Dhelim, Liming Luke Chen, Nyothiri Aung, Wenyin Zhang, Huansheng Ning
http://arxiv.org/abs/2106.03060v1

• [cs.IR]DMBGN: Deep Multi-Behavior Graph Networks for Voucher Redemption Rate Prediction
Fengtong Xiao, Lin Li, Weinan Xu, Jingyu Zhao, Xiaofeng Yang, Jun Lang, Hao Wang
http://arxiv.org/abs/2106.03356v1

• [cs.IR]Dual Attentive Sequential Learning for Cross-Domain Click-Through Rate Prediction
Pan Li, Zhichao Jiang, Maofei Que, Yao Hu, Alexander Tuzhilin
http://arxiv.org/abs/2106.02768v1

• [cs.IR]Leveraging Tripartite Interaction Information from Live Stream E-Commerce for Improving Product Recommendation
Sanshi Yu, Zhuoxuan Jiang, Dong-Dong Chen, Shanshan Feng, Dongsheng Li, Qi Liu, Jinfeng Yi
http://arxiv.org/abs/2106.03415v1

• [cs.IR]PURS: Personalized Unexpected Recommender System for Improving User Satisfaction
Pan Li, Maofei Que, Zhichao Jiang, Yao Hu, Alexander Tuzhilin
http://arxiv.org/abs/2106.02771v1

• [cs.IR]Pre-trained Language Model for Web-scale Retrieval in Baidu Search
Yiding Liu, Weixue Lu, Suqi Cheng, Daiting Shi, Shuaiqiang Wang, Zhicong Cheng, Dawei Yin
http://arxiv.org/abs/2106.03373v1

• [cs.IR]Scientific Dataset Discovery via Topic-level Recommendation
Basmah Altaf, Shichao Pei, Xiangliang Zhang
http://arxiv.org/abs/2106.03399v1

• [cs.IR]Socially-Aware Self-Supervised Tri-Training for Recommendation
Junliang Yu, Hongzhi Yin, Min Gao, Xin Xia, Xiangliang Zhang, Nguyen Quoc Viet Hung
http://arxiv.org/abs/2106.03569v1

• [cs.IT]A Stochastic Model for Block Segmentation of Images Based on the Quadtree and the Bayes Code for It
Yuta Nakahara, Toshiyasu Matsushima
http://arxiv.org/abs/2106.03349v1

• [cs.IT]Antenna Array Diagnosis for Millimeter-Wave MIMO Systems
Siqi Ma, Wenqian Shen, Jianping An, Lajos Hanzo
http://arxiv.org/abs/2106.02862v1

• [cs.IT]Beamforming and Transmit Power Design for Intelligent Reconfigurable Surface-aided Secure Spatial Modulation
Feng Shu, Xinyi Jiang, Wenlong Cai, Weiping Shi, Mengxing Huang, Jiangzhou Wang, Xiaohu You
http://arxiv.org/abs/2106.03616v1

• [cs.IT]Contact Tracing Information Improves the Performance of Group Testing Algorithms
Ritesh Goenka, Shu-Jie Cao, Chau-Wai Wong, Ajit Rajwade, Dror Baron
http://arxiv.org/abs/2106.02699v1

• [cs.IT]Dynamic Resource Configuration for Low-Power IoT Networks: A Multi-Objective Reinforcement Learning Method
Yang Huang, Caiyong Hao, Yijie Mao, Fuhui Zhou
http://arxiv.org/abs/2106.02826v1

• [cs.IT]Joint Design for Simultaneously Transmitting And Reflecting (STAR) RIS Assisted NOMA Systems
Jiakuo Zuo, Yuanwei Liu, Zhiguo Ding, Lingyang Song, H. Vincent Poor
http://arxiv.org/abs/2106.03001v1

• [cs.IT]Low-complexity Voronoi shaping for the Gaussian channel
S. Li, A. Mirani, M. Karlsson, E. Agrell
http://arxiv.org/abs/2106.03262v1

• [cs.IT]Neural Distributed Source Coding
Jay Whang, Anish Acharya, Hyeji Kim, Alexandros G. Dimakis
http://arxiv.org/abs/2106.02797v1

• [cs.IT]On Perceptual Lossy Compression: The Cost of Perceptual Reconstruction and An Optimal Training Framework
Zeyu Yan, Fei Wen, Rendong Ying, Chao Ma, Peilin Liu
http://arxiv.org/abs/2106.02782v1

• [cs.IT]On the Dual of Generalized Bent Functions
Jiaxin Wang, Fang-Wei Fu
http://arxiv.org/abs/2106.03102v1

• [cs.IT]On the Skew-Symmetric Binary Sequences and the Merit Factor Problem
Miroslav Dimitrov
http://arxiv.org/abs/2106.03377v1

• [cs.IT]Optimal Transmit Power and Antenna Selection to Achieve Energy Efficient and Low Complexity in fifth generation Massive MIMO Systems
Adeeb Salh, Lukman Audah, Nor Shahida Mohd Shah, Qazwan Abdullah, Noorsaliza Abdullah, Jameel Mukred, Shipun Hamzah
http://arxiv.org/abs/2106.03664v1

• [cs.IT]Principle Bit Analysis: Autoencoding with Schur-Concave Loss
Sourbh Bhadane, Aaron B. Wagner, Jayadev Acharya
http://arxiv.org/abs/2106.02796v1

• [cs.IT]Rack-Aware Regenerating Codes with Multiple Erasure Tolerance
Liyang Zhou, Zhifang Zhang
http://arxiv.org/abs/2106.03302v1

• [cs.IT]Relay Selection and Resource Allocation for Ultra-Reliable Uplink Transmission in Smart Factory Scenarios
Jing Cheng, Chao Shen
http://arxiv.org/abs/2106.02677v1

• [cs.IT]Robust Resource Allocation for Multi-Antenna URLLC-OFDMA Systems in a Smart Factory
Jing Cheng, Chao Shen, Shuqiang Xia
http://arxiv.org/abs/2106.02670v1

• [cs.IT]Study of Multi-Branch Tomlinson-Harashima Precoding with Multiple-Antenna Systems and Rate Splitting
A. Flores, R. C. de Lamare, B. Clerckx
http://arxiv.org/abs/2106.02776v1

• [cs.IT]The Computational and Latency Advantage of Quantum Communication Networks
Roberto Ferrara, Riccardo Bassoli, Christian Deppe, Frank H. P. Fitzek, Holger Boche
http://arxiv.org/abs/2106.03360v1

• [cs.IT]The Convexity and Concavity of Envelopes of the Minimum-Relative-Entropy Region for the DSBS
Lei Yu
http://arxiv.org/abs/2106.03654v1

• [cs.LG]A Physics-Informed Deep Learning Paradigm for Traffic State Estimation and Fundamental Diagram Discovery
Rongye Shi, Zhaobin Mo, Kuang Huang, Xuan Di, Qiang Du
http://arxiv.org/abs/2106.03142v1

• [cs.LG]A Primer on Multi-Neuron Relaxation-based Adversarial Robustness Certification
Kevin Roth
http://arxiv.org/abs/2106.03099v1

• [cs.LG]A Variational Perspective on Diffusion-Based Generative Models and Score Matching
Chin-Wei Huang, Jae Hyun Lim, Aaron Courville
http://arxiv.org/abs/2106.02808v1

• [cs.LG]A call for better unit testing for invariant risk minimisation
Chunyang Xiao, Pranava Madhyastha
http://arxiv.org/abs/2106.03234v1

• [cs.LG]A novel Deep Neural Network architecture for non-linear system identification
Luca Zancato, Alessandro Chiuso
http://arxiv.org/abs/2106.03078v1

• [cs.LG]Accelerating Stochastic Simulation with Interactive Neural Processes
Dongxia Wu, Matteo Chinazzi, Alessandro Vespignani, Yi-An Ma, Rose Yu
http://arxiv.org/abs/2106.02770v1

• [cs.LG]Adversarial Classification of the Attacks on Smart Grids Using Game Theory and Deep Learning
Kian Hamedani, Lingjia Liu, Jithin Jagannath, Yang, Yi
http://arxiv.org/abs/2106.03209v1

• [cs.LG]Adversarially Regularized Graph Attention Networks for Inductive Learning on Partially Labeled Graphs
Jiaren Xiao, Quanyu Dai, Xiaochen Xie, James Lam, Ka-Wai Kwok
http://arxiv.org/abs/2106.03393v1

• [cs.LG]An Information-theoretic Approach to Distribution Shifts
Marco Federici, Ryota Tomioka, Patrick Forré
http://arxiv.org/abs/2106.03783v1

• [cs.LG]Antipodes of Label Differential Privacy: PATE and ALIBI
Mani Malek, Ilya Mironov, Karthik Prasad, Igor Shilov, Florian Tramèr
http://arxiv.org/abs/2106.03408v1

• [cs.LG]Asymmetric Loss Functions for Learning with Noisy Labels
Xiong Zhou, Xianming Liu, Junjun Jiang, Xin Gao, Xiangyang Ji
http://arxiv.org/abs/2106.03110v1

• [cs.LG]Automation for Interpretable Machine Learning Through a Comparison of Loss Functions to Regularisers
A. I. Parkes, J. Camilleri, D. A. Hudson, A. J. Sobey
http://arxiv.org/abs/2106.03428v1

• [cs.LG]Average-Reward Reinforcement Learning with Trust Region Methods
Xiaoteng Ma, Xiaohang Tang, Li Xia, Jun Yang, Qianchuan Zhao
http://arxiv.org/abs/2106.03442v1

• [cs.LG]Bandwidth-based Step-Sizes for Non-Convex Stochastic Optimization
Xiaoyu Wang, Mikael Johansson
http://arxiv.org/abs/2106.02888v1

• [cs.LG]Beyond Bandit Feedback in Online Multiclass Classification
Dirk van der Hoeven, Federico Fusco, Nicolò Cesa-Bianchi
http://arxiv.org/abs/2106.03596v1

• [cs.LG]Boosting a Model Zoo for Multi-Task and Continual Learning
Rahul Ramesh, Pratik Chaudhari
http://arxiv.org/abs/2106.03027v1

• [cs.LG]CAPE: Encoding Relative Positions with Continuous Augmented Positional Embeddings
Tatiana Likhomanenko, Qiantong Xu, Ronan Collobert, Gabriel Synnaeve, Alex Rogozhnikov
http://arxiv.org/abs/2106.03143v1

• [cs.LG]Can Subnetwork Structure be the Key to Out-of-Distribution Generalization?
Dinghuai Zhang, Kartik Ahuja, Yilun Xu, Yisen Wang, Aaron Courville
http://arxiv.org/abs/2106.02890v1

• [cs.LG]Causal Influence Detection for Improving Efficiency in Reinforcement Learning
Maximilian Seitzer, Bernhard Schölkopf, Georg Martius
http://arxiv.org/abs/2106.03443v1

• [cs.LG]Churn Reduction via Distillation
Heinrich Jiang, Harikrishna Narasimhan, Dara Bahri, Andrew Cotter, Afshin Rostamizadeh
http://arxiv.org/abs/2106.02654v1

• [cs.LG]Collaborative Causal Discovery with Atomic Interventions
Raghavendra Addanki, Shiva Prasad Kasiviswanathan
http://arxiv.org/abs/2106.03028v1

• [cs.LG]Commutative Lie Group VAE for Disentanglement Learning
Xinqi Zhu, Chang Xu, Dacheng Tao
http://arxiv.org/abs/2106.03375v1

• [cs.LG]Complexity Analysis of Stein Variational Gradient Descent Under Talagrand’s Inequality T1
Adil Salim, Lukang Sun, Peter Richtárik
http://arxiv.org/abs/2106.03076v1

• [cs.LG]Concave Utility Reinforcement Learning: the Mean-field Game viewpoint
Matthieu Geist, Julien Pérolat, Mathieu Laurière, Romuald Elie, Sarah Perrin, Olivier Bachem, Rémi Munos, Olivier Pietquin
http://arxiv.org/abs/2106.03787v1

• [cs.LG]Conditional Contrastive Learning: Removing Undesirable Information in Self-Supervised Representations
Yao-Hung Hubert Tsai, Martin Q. Ma, Han Zhao, Kun Zhang, Louis-Philippe Morency, Ruslan Salakhutdinov
http://arxiv.org/abs/2106.02866v1

• [cs.LG]Constrained Generalized Additive 2 Model with Consideration of High-Order Interactions
Akihisa Watanabe, Michiya Kuramata, Kaito Majima, Haruka Kiyohara, Kensho Kondo, Kazuhide Nakata
http://arxiv.org/abs/2106.02836v1

• [cs.LG]Context-Aware Sparse Deep Coordination Graphs
Tonghan Wang, Liang Zeng, Weijun Dong, Qianlan Yang, Yang Yu, Chongjie Zhang
http://arxiv.org/abs/2106.02886v1

• [cs.LG]Continual Active Learning for Efficient Adaptation of Machine Learning Models to Changing Image Acquisition
Matthias Perkonigg, Johannes Hofmanninger, Georg Langs
http://arxiv.org/abs/2106.03351v1

• [cs.LG]Control-Oriented Model-Based Reinforcement Learning with Implicit Differentiation
Evgenii Nikishin, Romina Abachi, Rishabh Agarwal, Pierre-Luc Bacon
http://arxiv.org/abs/2106.03273v1

• [cs.LG]Counterfactual Maximum Likelihood Estimation for Training Deep Networks
Xinyi Wang, Wenhu Chen, Michael Saxon, William Yang Wang
http://arxiv.org/abs/2106.03831v1

• [cs.LG]DAMSL: Domain Agnostic Meta Score-based Learning
John Cai, Bill Cai, Shengmei Shen
http://arxiv.org/abs/2106.03041v1

• [cs.LG]DL-DDA — Deep Learning based Dynamic Difficulty Adjustment with UX and Gameplay constraints
Dvir Ben Or, Michael Kolomenkin, Gil Shabat
http://arxiv.org/abs/2106.03075v1

• [cs.LG]DSelect-k: Differentiable Selection in the Mixture of Experts with Applications to Multi-Task Learning
Hussein Hazimeh, Zhe Zhao, Aakanksha Chowdhery, Maheswaran Sathiamoorthy, Yihua Chen, Rahul Mazumder, Lichan Hong, Ed H. Chi
http://arxiv.org/abs/2106.03760v1

• [cs.LG]Deep Canonical Correlation Alignment for Sensor Signals
Narayan Schütz, Angela Botros, Michael Single, Philipp Buluschek, Tobias Nef
http://arxiv.org/abs/2106.03637v1

• [cs.LG]Deep Neural Network Modeling of Unknown Partial Differential Equations in Nodal Space
Zhen Chen, Victor Churchill, Kailiang Wu, Dongbin Xiu
http://arxiv.org/abs/2106.03603v1

• [cs.LG]Differentially Private Multi-Armed Bandits in the Shuffle Model
Jay Tenenbaum, Haim Kaplan, Yishay Mansour, Uri Stemmer
http://arxiv.org/abs/2106.02900v1

• [cs.LG]Distributed Learning and its Application for Time-Series Prediction
Nhuong V. Nguyen, Sybille Legitime
http://arxiv.org/abs/2106.03211v1

• [cs.LG]Distributional Reinforcement Learning with Unconstrained Monotonic Neural Networks
Thibaut Théate, Antoine Wehenkel, Adrien Bolland, Gilles Louppe, Damien Ernst
http://arxiv.org/abs/2106.03228v1

• [cs.LG]Efficient Continuous Control with Double Actors and Regularized Critics
Jiafei Lyu, Xiaoteng Ma, Jiangpeng Yan, Xiu Li
http://arxiv.org/abs/2106.03050v1

• [cs.LG]Efficient Lottery Ticket Finding: Less Data is More
Zhenyu Zhang, Xuxi Chen, Tianlong Chen, Zhangyang Wang
http://arxiv.org/abs/2106.03225v1

• [cs.LG]Enabling On-Device Self-Supervised Contrastive Learning With Selective Data Contrast
Yawen Wu, Zhepeng Wang, Dewen Zeng, Yiyu Shi, Jingtong Hu
http://arxiv.org/abs/2106.03796v1

• [cs.LG]Energy Aligning for Biased Models
Bowen Zhao, Chen Chen, Qi Ju, ShuTao Xia
http://arxiv.org/abs/2106.03343v1

• [cs.LG]Energy-Based Learning for Cooperative Games, with Applications to Feature/Data/Model Valuations
Yatao Bian, Yu Rong, Tingyang Xu, Jiaxiang Wu, Andreas Krause, Junzhou Huang
http://arxiv.org/abs/2106.02938v1

• [cs.LG]Ensemble Defense with Data Diversity: Weak Correlation Implies Strong Robustness
Renjue Li, Hanwei Zhang, Pengfei Yang, Cheng-Chao Huang, Aimin Zhou, Bai Xue, Lijun Zhang
http://arxiv.org/abs/2106.02867v1

• [cs.LG]Equivariant Graph Neural Networks for 3D Macromolecular Structure
Bowen Jing, Stephan Eismann, Pratham N. Soni, Ron O. Dror
http://arxiv.org/abs/2106.03843v1

• [cs.LG]Error Loss Networks
Badong Chen, Yunfei Zheng, Pengju Ren
http://arxiv.org/abs/2106.03722v1

• [cs.LG]Escaping Saddle Points Faster with Stochastic Momentum
Jun-Kun Wang, Chi-Heng Lin, Jacob Abernethy
http://arxiv.org/abs/2106.02985v1

• [cs.LG]Exploring the Limits of Out-of-Distribution Detection
Stanislav Fort, Jie Ren, Balaji Lakshminarayanan
http://arxiv.org/abs/2106.03004v1

• [cs.LG]Exposing the Implicit Energy Networks behind Masked Language Models via Metropolis—Hastings
Kartik Goyal, Chris Dyer, Taylor Berg-Kirkpatrick
http://arxiv.org/abs/2106.02736v1

• [cs.LG]Extracting Weighted Automata for Approximate Minimization in Language Modelling
Clara Lacroce, Prakash Panangaden, Guillaume Rabusseau
http://arxiv.org/abs/2106.02965v1

• [cs.LG]FedNL: Making Newton-Type Methods Applicable to Federated Learning
Mher Safaryan, Rustem Islamov, Xun Qian, Peter Richtárik
http://arxiv.org/abs/2106.02969v1

• [cs.LG]FlexParser — the adaptive log file parser for continuous results in a changing world
Nadine Ruecker, Andreas Maier
http://arxiv.org/abs/2106.03170v1

• [cs.LG]Forced Variational Integrator Networks for Prediction and Control of Mechanical Systems
Aaron Havens, Girish Chowdhary
http://arxiv.org/abs/2106.02973v1

• [cs.LG]Formalizing Distribution Inference Risks
Anshuman Suri, David Evans
http://arxiv.org/abs/2106.03699v1

• [cs.LG]GAN Cocktail: mixing GANs without dataset access
Omri Avrahami, Dani Lischinski, Ohad Fried
http://arxiv.org/abs/2106.03847v1

• [cs.LG]Generative Adversarial Networks: A Survey Towards Private and Secure Applications
Zhipeng Cai, Zuobin Xiong, Honghui Xu, Peng Wang, Wei Li, Yi Pan
http://arxiv.org/abs/2106.03785v1

• [cs.LG]Graph Belief Propagation Networks
Junteng Jia, Cenk Baykal, Vamsi K. Potluru, Austin R. Benson
http://arxiv.org/abs/2106.03033v1

• [cs.LG]Graph Infomax Adversarial Learning for Treatment Effect Estimation with Networked Observational Data
Zhixuan Chu, Stephen L. Rathbun, Sheng Li
http://arxiv.org/abs/2106.02881v1

• [cs.LG]Graph Neural Networks in Network Neuroscience
Alaa Bessadok, Mohamed Ali Mahjoub, Islem Rekik
http://arxiv.org/abs/2106.03535v1

• [cs.LG]Graph2Graph Learning with Conditional Autoregressive Models
Guan Wang, Francois Bernard Lauze, Aasa Feragen
http://arxiv.org/abs/2106.03236v1

• [cs.LG]GraphMI: Extracting Private Graph Data from Graph Neural Networks
Zaixi Zhang, Qi Liu, Zhenya Huang, Hao Wang, Chengqiang Lu, Chuanren Liu, Enhong Chen
http://arxiv.org/abs/2106.02820v1

• [cs.LG]Heuristic-Guided Reinforcement Learning
Ching-An Cheng, Andrey Kolobov, Adith Swaminathan
http://arxiv.org/abs/2106.02757v1

• [cs.LG]High-Dimensional Bayesian Optimisation with Variational Autoencoders and Deep Metric Learning
Antoine Grosnit, Rasul Tutunov, Alexandre Max Maraval, Ryan-Rhys Griffiths, Alexander I. Cowen-Rivers, Lin Yang, Lin Zhu, Wenlong Lyu, Zhitang Chen, Jun Wang, Jan Peters, Haitham Bou-Ammar
http://arxiv.org/abs/2106.03609v1

• [cs.LG]HoroPCA: Hyperbolic Dimensionality Reduction via Horospherical Projections
Ines Chami, Albert Gu, Dat Nguyen, Christopher Ré
http://arxiv.org/abs/2106.03306v1

• [cs.LG]Identifiability in inverse reinforcement learning
Haoyang Cao, Samuel N. Cohen, Lukasz Szpruch
http://arxiv.org/abs/210
1000
6.03498v1
1000
6.03498v1)

• [cs.LG]ImGAGN:Imbalanced Network Embedding via Generative Adversarial Graph Networks
Liang Qu, Huaisheng Zhu, Ruiqi Zheng, Yuhui Shi, Hongzhi Yin
http://arxiv.org/abs/2106.02817v1

• [cs.LG]Increase and Conquer: Training Graph Neural Networks on Growing Graphs
Juan Cervino, Luana Ruiz, Alejandro Ribeiro
http://arxiv.org/abs/2106.03693v1

• [cs.LG]Instrument Space Selection for Kernel Maximum Moment Restriction
Rui Zhang, Krikamol Muandet, Bernhard Schölkopf, Masaaki Imaizumi
http://arxiv.org/abs/2106.03340v1

• [cs.LG]Integrating Auxiliary Information in Self-supervised Learning
Yao-Hung Hubert Tsai, Tianqin Li, Weixin Liu, Peiyuan Liao, Ruslan Salakhutdinov, Louis-Philippe Morency
http://arxiv.org/abs/2106.02869v1

• [cs.LG]Integrating Expert ODEs into Neural ODEs: Pharmacology and Disease Progression
Zhaozhi Qian, William R. Zame, Mihaela van der Schaar, Lucas M. Fleuren, Paul Elbers
http://arxiv.org/abs/2106.02875v1

• [cs.LG]Layered gradient accumulation and modular pipeline parallelism: fast and efficient training of large language models
Joel Lamy-Poirier
http://arxiv.org/abs/2106.02679v1

• [cs.LG]Learnable Fourier Features for Multi-DimensionalSpatial Positional Encoding
Yang Li, Si Si, Gang Li, Cho-Jui Hsieh, Samy Bengio
http://arxiv.org/abs/2106.02795v1

• [cs.LG]Learning Combinatorial Node Labeling Algorithms
Lukas Gianinazzi, Maximilian Fries, Nikoli Dryden, Tal Ben-Nun, Torsten Hoefler
http://arxiv.org/abs/2106.03594v1

• [cs.LG]Learning MDPs from Features: Predict-Then-Optimize for Sequential Decision Problems by Reinforcement Learning
Kai Wang, Sanket Shat, Haipeng Chen, Andrew Perrault, Finale Doshi-Velez, Milind Tambe
http://arxiv.org/abs/2106.03279v1

• [cs.LG]Learning Routines for Effective Off-Policy Reinforcement Learning
Edoardo Cetin, Oya Celiktutan
http://arxiv.org/abs/2106.02943v1

• [cs.LG]Learning Stochastic Optimal Policies via Gradient Descent
Stefano Massaroli, Michael Poli, Stefano Peluchetti, Jinkyoo Park, Atsushi Yamashita, Hajime Asama
http://arxiv.org/abs/2106.03780v1

• [cs.LG]Learning proofs for the classification of nilpotent semigroups
Carlos Simpson
http://arxiv.org/abs/2106.03015v1

• [cs.LG]Learning stable reduced-order models for hybrid twins
Abel Sancarlos, Morgan Cameron, Jean-Marc Le Peuvedic, Juliette Groulier, Jean-Louis Duval, Elias Cueto, Francisco Chinesta
http://arxiv.org/abs/2106.03464v1

• [cs.LG]Learning to Efficiently Sample from Diffusion Probabilistic Models
Daniel Watson, Jonathan Ho, Mohammad Norouzi, William Chan
http://arxiv.org/abs/2106.03802v1

• [cs.LG]Learning without Knowing: Unobserved Context in Continuous Transfer Reinforcement Learning
Chenyu Liu, Yan Zhang, Yi Shen, Michael M. Zavlanos
http://arxiv.org/abs/2106.03833v1

• [cs.LG]Local Disentanglement in Variational Auto-Encoders Using Jacobian Regularization
Travers Rhodes, Daniel D. Lee
http://arxiv.org/abs/2106.02923v1

• [cs.LG]Low Budget Active Learning via Wasserstein Distance: An Integer Programming Approach
Rafid Mahmood, Sanja Fidler, Marc T. Law
http://arxiv.org/abs/2106.02968v1

• [cs.LG]Making EfficientNet More Efficient: Exploring Batch-Independent Normalization, Group Convolutions and Reduced Resolution Training
Dominic Masters, Antoine Labatie, Zach Eaton-Rosen, Carlo Luschi
http://arxiv.org/abs/2106.03640v1

• [cs.LG]Measuring Generalization with Optimal Transport
Ching-Yao Chuang, Youssef Mroueh, Kristjan Greenewald, Antonio Torralba, Stefanie Jegelka
http://arxiv.org/abs/2106.03314v1

• [cs.LG]MemStream: Memory-Based Anomaly Detection in Multi-Aspect Streams with Concept Drift
Siddharth Bhatia, Arjit Jain, Shivin Srivastava, Kenji Kawaguchi, Bryan Hooi
http://arxiv.org/abs/2106.03837v1

• [cs.LG]Meta-Learning Reliable Priors in the Function Space
Jonas Rothfuss, Dominique Heyn, Jinfan Chen, Andreas Krause
http://arxiv.org/abs/2106.03195v1

• [cs.LG]Mitigating Covariate Shift in Imitation Learning via Offline Data Without Great Coverage
Jonathan D. Chang, Masatoshi Uehara, Dhruv Sreenivas, Rahul Kidambi, Wen Sun
http://arxiv.org/abs/2106.03207v1

• [cs.LG]MixRL: Data Mixing Augmentation for Regression using Reinforcement Learning
Seong-Hyeon Hwang, Steven Euijong Whang
http://arxiv.org/abs/2106.03374v1

• [cs.LG]Multi-armed Bandit Requiring Monotone Arm Sequences
Ningyuan Chen
http://arxiv.org/abs/2106.03790v1

• [cs.LG]Multi-chart flows
Dimitris Kalatzis, Johan Ziruo Ye, Jesper Wohlert, Søren Hauberg
http://arxiv.org/abs/2106.03500v1

• [cs.LG]Multi-facet Contextual Bandits: A Neural Network Perspective
Yikun Ban, Jingrui He, Curtiss B. Cook
http://arxiv.org/abs/2106.03039v1

• [cs.LG]Neural Active Learning with Performance Guarantees
Pranjal Awasthi, Christoph Dann, Claudio Gentile, Ayush Sekhari, Zhilei Wang
http://arxiv.org/abs/2106.03243v1

• [cs.LG]On Learning to Rank Long Sequences with Contextual Bandits
Anirban Santara, Claudio Gentile, Gaurav Aggarwal, Shuai Li
http://arxiv.org/abs/2106.03546v1

• [cs.LG]On Local Aggregation in Heterophilic Graphs
Hesham Mostafa, Marcel Nassar, Somdeb Majumdar
http://arxiv.org/abs/2106.03213v1

• [cs.LG]On Memorization in Probabilistic Deep Generative Models
Gerrit J. J. van den Burg, Christopher K. I. Williams
http://arxiv.org/abs/2106.03216v1

• [cs.LG]On Training Sample Memorization: Lessons from Benchmarking Generative Modeling with a Large-scale Competition
Ching-Yuan Bai, Hsuan-Tien Lin, Colin Raffel, Wendy Chih-wen Kan
http://arxiv.org/abs/2106.03062v1

• [cs.LG]On the Expressive Power of Self-Attention Matrices
Valerii Likhosherstov, Krzysztof Choromanski, Adrian Weller
http://arxiv.org/abs/2106.03764v1

• [cs.LG]On the Power of Shallow Learning
James B. Simon, Sajant Anand, Michael R. DeWeese
http://arxiv.org/abs/2106.03186v1

• [cs.LG]On the Role of Entropy-based Loss for Learning Causal Structures with Continuous Optimization
Ruichu Cai, Weilin Chen, Jie Qiao, Zhifeng Hao
http://arxiv.org/abs/2106.02835v1

• [cs.LG]OoD-Bench: Benchmarking and Understanding Out-of-Distribution Generalization Datasets and Algorithms
Nanyang Ye, Kaican Li, Lanqing Hong, Haoyue Bai, Yiting Chen, Fengwei Zhou, Zhenguo Li
http://arxiv.org/abs/2106.03721v1

• [cs.LG]PAC Best Arm Identification Under a Deadline
Brijen Thananjeyan, Kirthevasan Kandasamy, Ion Stoica, Michael I. Jordan, Ken Goldberg, Joseph E. Gonzalez
http://arxiv.org/abs/2106.03221v1

• [cs.LG]PID-GAN: A GAN Framework based on a Physics-informed Discriminator for Uncertainty Quantification with Physics
Arka Daw, M. Maruf, Anuj Karpatne
http://arxiv.org/abs/2106.02993v1

• [cs.LG]Parameter-free Statistically Consistent Interpolation: Dimension-independent Convergence Rates for Hilbert kernel regression
Partha P Mitra, Clément Sire
http://arxiv.org/abs/2106.03354v1

• [cs.LG]PcDGAN: A Continuous Conditional Diverse Generative Adversarial Network For Inverse Design
Amin Heyrani Nobari, Wei Chen, Faez Ahmed
http://arxiv.org/abs/2106.03620v1

• [cs.LG]Photonic Differential Privacy with Direct Feedback Alignment
Ruben Ohana, Hamlet J. Medina Ruiz, Julien Launay, Alessandro Cappelli, Iacopo Poli, Liva Ralaivola, Alain Rakotomamonjy
http://arxiv.org/abs/2106.03645v1

• [cs.LG]Preservation of the Global Knowledge by Not-True Self Knowledge Distillation in Federated Learning
Gihun Lee, Yongjin Shin, Minchan Jeong, Se-Young Yun
http://arxiv.org/abs/2106.03097v1

• [cs.LG]Proxy-Normalizing Activations to Match Batch Normalization while Removing Batch Dependence
Antoine Labatie, Dominic Masters, Zach Eaton-Rosen, Carlo Luschi
http://arxiv.org/abs/2106.03743v1

• [cs.LG]Quantifying and Improving Transferability in Domain Generalization
Guojun Zhang, Han Zhao, Yaoliang Yu, Pascal Poupart
http://arxiv.org/abs/2106.03632v1

• [cs.LG]Robust Implicit Networks via Non-Euclidean Contractions
Saber Jafarpour, Alexander Davydov, Anton V. Proskurnikov, Francesco Bullo
http://arxiv.org/abs/2106.03194v1

• [cs.LG]SNR optimization of multi-span fiber optic communication systems employing EDFAs with non-flat gain and noise figure
Metodi Plamenov Yankov, Pawel Marcin Kaminski, Henrik Enggaard Hansen, Francesco Da Ros
http://arxiv.org/abs/2106.03639v1

• [cs.LG]Same State, Different Task: Continual Reinforcement Learning without Interference
Samuel Kessler, Jack Parker-Holder, Philip Ball, Stefan Zohren, Stephen J. Roberts
http://arxiv.org/abs/2106.02940v1

• [cs.LG]Scalable Computation of Monge Maps with General Costs
Jiaojiao Fan, Shu Liu, Shaojun Ma, Yongxin Chen, Haomin Zhou
http://arxiv.org/abs/2106.03812v1

• [cs.LG]ScheduleNet: Learn to solve multi-agent scheduling problems with reinforcement learning
Junyoung Park, Sanjar Bakhtiyar, Jinkyoo Park
http://arxiv.org/abs/2106.03051v1

• [cs.LG]Securing Secure Aggregation: Mitigating Multi-Round Privacy Leakage in Federated Learning
Jinhyun So, Ramy E. Ali, Basak Guler, Jiantao Jiao, Salman Avestimehr
http://arxiv.org/abs/2106.03328v1

• [cs.LG]Self-Supervised Graph Learning with Proximity-based Views and Channel Contrast
Wei Zhuo, Guang Tan
http://arxiv.org/abs/2106.03723v1

• [cs.LG]Self-supervised Rubik’s Cube Solver
Kyo Takano
http://arxiv.org/abs/2106.03157v1

• [cs.LG]Semi-Riemannian Graph Convolutional Networks
Bo Xiong, Shichao Zhu, Nico Potyka, Shirui Pan, Chuan Zhou, Steffen Staab
http://arxiv.org/abs/2106.03134v1

• [cs.LG]SizeFlags: Reducing Size and Fit Related Returns in Fashion E-Commerce
Andrea Nestler, Nour Karessli, Karl Hajjar, Rodrigo Weffer, Reza Shirvany
http://arxiv.org/abs/2106.03532v1

• [cs.LG]Smoothness-Aware Quantization Techniques
Bokun Wang, Mher Safaryan, Peter Richtárik
http://arxiv.org/abs/2106.03524v1

• [cs.LG]SoftDICE for Imitation Learning: Rethinking Off-policy Distribution Matching
Mingfei Sun, Anuj Mahajan, Katja Hofmann, Shimon Whiteson
http://arxiv.org/abs/2106.03155v1

• [cs.LG]Solving hybrid machine learning tasks by traversing weight space geodesics
Guruprasad Raghavan, Matt Thomson
http://arxiv.org/abs/2106.02793v1

• [cs.LG]Stability of Manifold Neural Networks to Deformations
Zhiyang Wang, Luana Ruiz, Alejandro Ribeiro
http://arxiv.org/abs/2106.03725v1

• [cs.LG]Stateful Strategic Regression
Keegan Harris, Hoda Heidari, Zhiwei Steven Wu
http://arxiv.org/abs/2106.03827v1

• [cs.LG]Sum of Ranked Range Loss for Supervised Learning
Shu Hu, Yiming Ying, Xin Wang, Siwei Lyu
http://arxiv.org/abs/2106.03300v1

• [cs.LG]Tabular Data: Deep Learning is Not All You Need
Ravid Shwartz-Ziv, Amitai Armon
http://arxiv.org/abs/2106.03253v1

• [cs.LG]TabularNet: A Neural Network Architecture for Understanding Semantic Structures of Tabular Data
Lun Du, Fei Gao, Xu Chen, Ran Jia, Junshan Wang, Shi Han, Dongmei Zhang
http://arxiv.org/abs/2106.03096v1

• [cs.LG]Tensor Normal Training for Deep Learning Models
Yi Ren, Donald Goldfarb
http://arxiv.org/abs/2106.02925v1

• [cs.LG]The Fine-Grained Hardness of Sparse Linear Regression
Aparna Gupte, Vinod Vaikuntanathan
http://arxiv.org/abs/2106.03131v1

• [cs.LG]The Power of Exploiter: Provable Multi-Agent RL in Large State Spaces
Chi Jin, Qinghua Liu, Tiancheng Yu
http://arxiv.org/abs/2106.03352v1

• [cs.LG]Top-KAST: Top-K Always Sparse Training
Siddhant M. Jayakumar, Razvan Pascanu, Jack W. Rae, Simon Osindero, Erich Elsen
http://arxiv.org/abs/2106.03517v1

• [cs.LG]Topological Measurement of Deep Neural Networks Using Persistent Homology
Satoru Watanabe, Hayato Yamana
http://arxiv.org/abs/2106.03016v1

• [cs.LG]Towards robust and domain agnostic reinforcement learning competitions
William Hebgen Guss, Stephanie Milani, Nicholay Topin, Brandon Houghton, Sharada Mohanty, Andrew Melnik, Augustin Harter, Benoit Buschmaas, Bjarne Jaster, Christoph Berganski, Dennis Heitkamp, Marko Henning, Helge Ritter, Chengjie Wu, Xiaotian Hao, Yiming Lu, Hangyu Mao, Yihuan Mao, Chao Wang, Michal Opanowicz, Anssi Kanervisto, Yanick Schraner, Christian Scheller, Xiren Zhou, Lu Liu, Daichi Nishio, Toi Tsuneda, Karolis Ramanauskas, Gabija Juceviciute
http://arxiv.org/abs/2106.03748v1

• [cs.LG]Training Robust Graph Neural Networks with Topology Adaptive Edge Dropping
Zhan Gao, Subhrajit Bhattacharya, Leiming Zhang, Rick S. Blum, Alejandro Ribeiro, Brian M. Sadler
http://arxiv.org/abs/2106.02892v1

• [cs.LG]Understand and Improve Contrastive Learning Methods for Visual Representation: A Review
Ran Liu
http://arxiv.org/abs/2106.03259v1

• [cs.LG]Vanishing Curvature and the Power of Adaptive Methods in Randomly Initialized Deep Networks
Antonio Orvieto, Jonas Kohler, Dario Pavllo, Thomas Hofmann, Aurelien Lucchi
http://arxiv.org/abs/2106.03763v1

• [cs.LG]Variational Leakage: The Role of Information Complexity in Privacy Leakage
Amir Ahooye Atashin, Behrooz Razeghi, Deniz Gündüz, Slava Voloshynovskiy
http://arxiv.org/abs/2106.02818v1

• [cs.LG]Zero-Shot Knowledge Distillation from a Decision-Based Black-Box Model
Zi Wang
http://arxiv.org/abs/2106.03310v1

• [cs.LG]k-Mixup Regularization for Deep Learning via Optimal Transport
Kristjan Greenewald, Anming Gu, Mikhail Yurochkin, Justin Solomon, Edward Chien
http://arxiv.org/abs/2106.02933v1

• [cs.NE]MoleHD: Automated Drug Discovery using Brain-Inspired Hyperdimensional Computing
Dongning Ma, Xun Jiao
http://arxiv.org/abs/2106.02894v1

• [cs.NE]One-shot learning of paired associations by a reservoir computing model with Hebbian plasticity
M Ganesh Kumar, Cheston Tan, Camilo Libedinsky, Shih-Cheng Yen, Andrew Yong-Yi Tan
http://arxiv.org/abs/2106.03580v1

• [cs.NE]SpikePropamine: Differentiable Plasticity in Spiking Neural Networks
Samuel Schmidgall, Julia Ashkanazy, Wallace Lawson, Joe Hays
http://arxiv.org/abs/2106.02681v1

• [cs.NE]Subject Independent Emotion Recognition using EEG Signals Employing Attention Driven Neural Networks
Arjun, Aniket Singh Rajpoot, Mahesh Raveendranatha Panicker
http://arxiv.org/abs/2106.03461v1

• [cs.NI]Immediate Proximity Detection Using Wi-Fi-Enabled Smartphones
Zach Van Hyfte, Avideh Zakhor
http://arxiv.org/abs/2106.02777v1

• [cs.NI]The Four Levels of Fixed-Points in Mean-Field Models
Sarath Yasodharan, Rajesh Sundaresan
http://arxiv.org/abs/2106.02807v1

• [cs.RO]A Split-face Study of Novel Robotic Prototype vs Human Operator in Skin Rejuvenation Using Q-switched Nd:Yag Laser: Accuracy, Efficacy and Safety
Si Un Chan, Cheong Cheong Ip, Chengxiang Lian, Muhammad Muddassir, Domingo Gomez Dominguez, Wai Kit Ming, Jianhao Du, Yue Zheng, David Navarro-Alarcon, Lie Hua Deng
http://arxiv.org/abs/2106.02829v1

• [cs.RO]Brno Urban Dataset: Winter Extention
Adam Ligocki, Ales Jelinek, Ludek Zalud
http://arxiv.org/abs/2106.02952v1

• [cs.RO]Collective transport via sequential caging
Vivek Shankar Vardharajan, Karthik Soma, Giovanni Beltrame
http://arxiv.org/abs/2106.03132v1

• [cs.RO]Cost-effective Mapping of Mobile Robot Based on the Fusion of UWB and Short-range 2D LiDAR
Ran Liu, Yongping He, Chau Yuen, Billy Pik Lik Lau, Rashid Ali, Wenpeng Fu, Zhiqiang Cao
http://arxiv.org/abs/2106.03648v1

• [cs.RO]Design of hazard based model and collision avoidance system
Md Faysal Kabir, Sahadev Roy
http://arxiv.org/abs/2106.02984v1

• [cs.RO]Distributed Task Allocation in Homogeneous Swarms Using Language Measure Theory
Devesh K. Jha
http://arxiv.org/abs/2106.02992v1

• [cs.RO]FACT: A Full-body Ad-hoc Collaboration Test
1bea
bed for Modeling Complex Teamwork
Gopika Ajaykumar, Annie Mao, Jeremy Brown, Chien-Ming Huang
http://arxiv.org/abs/2106.03290v1

• [cs.RO]Hierarchical Robot Navigation in Novel Environments using Rough 2-D Maps
Chengguang Xu, Christopher Amato, Lawson L. S. Wong
http://arxiv.org/abs/2106.03665v1

• [cs.RO]Inferring Objectives in Continuous Dynamic Games from Noise-Corrupted Partial State Observations
Lasse Peters, David Fridovich-Keil, Vicenç Rubies-Royo, Claire J. Tomlin, Cyrill Stachniss
http://arxiv.org/abs/2106.03611v1

• [cs.RO]Motion Planning Transformers: One Model to Plan Them All
Jacob J. Johnson, Linjun Li, Ahmed H. Qureshi, Michael C. Yip
http://arxiv.org/abs/2106.02791v1

• [cs.RO]Multi-goal path planning using multiple random trees
Jaroslav Janoš, Vojtěch Vonásek, Robert Pěnička
http://arxiv.org/abs/2106.03407v1

• [cs.RO]Negotiation-Aware Reachability-Based Safety Verification for AutonomousDriving in Interactive Scenarios
Ran Tian, Anjian Li, Masayoshi Tomizuka, Liting Sun
http://arxiv.org/abs/2106.02737v1

• [cs.RO]On Healthcare Robots: Concepts, definitions, and considerations for healthcare robot governance
Eduard Fosch-Villaronga, Hadassah Drukarch
http://arxiv.org/abs/2106.03468v1

• [cs.RO]PYROBOCOP : Python-based Robotic Control & Optimization Package for Manipulation and Collision Avoidance
Arvind U. Raghunathan, Devesh K. Jha, Diego Romeres
http://arxiv.org/abs/2106.03220v1

• [cs.RO]Planning Multimodal Exploratory Actions for Online Robot Attribute Learning
Xiaohan Zhang, Jivko Sinapov, Shiqi Zhang
http://arxiv.org/abs/2106.03029v1

• [cs.RO]Real-time Identification and Tuning of Multirotors Based on Deep Neural Networks for Accurate Trajectory Tracking Under Wind Disturbances
AbdulAziz Y. AlKayas, Mohamad Chehadeh, Abdulla Ayyad, Yahya Zweiri
http://arxiv.org/abs/2106.03459v1

• [cs.RO]Robotic Electrospinning Actuated by Non-Circular Joint Continuum Manipulator for Endoluminal Therapy
Zicong Wu, Chuqian Lou, Zhu Jin, Shaoping Huang, Ning Liu, Yun Zou, Mirko Kovac, Anzhu Gao, Guang-Zhong Yang
http://arxiv.org/abs/2106.03562v1

• [cs.RO]Stein ICP for Uncertainty Estimation in Point Cloud Matching
Fahira Afzal Maken, Fabio Ramos, Lionel Ott
http://arxiv.org/abs/2106.03287v1

• [cs.RO]Terrain Adaptive Gait Transitioning for a Quadruped Robot using Model Predictive Control
Prathamesh Saraf, Abhishek Sarkar, Arshad Javed
http://arxiv.org/abs/2106.03307v1

• [cs.RO]Towards a Multi-purpose Robotic Nursing Assistant
Krishna Chaitanya Kodur, Kaustubh Rajpathak, Akilesh Rajavenkatanarayanan, Maria Kyrarini, Fillia Makedon
http://arxiv.org/abs/2106.03683v1

• [cs.RO]Trajectory Optimization of Chance-Constrained Nonlinear Stochastic Systems for Motion Planning and Control
Yashwanth Kumar Nakka, Soon-Jo Chung
http://arxiv.org/abs/2106.02801v1

• [cs.RO]Tunable Trajectory Planner Using G3 Curves
Alexander Botros, Stephen L. Smith
http://arxiv.org/abs/2106.03836v1

• [cs.SD]Active Speaker Detection as a Multi-Objective Optimization with Uncertainty-based Multimodal Fusion
Baptiste Pouthier, Laurent Pilati, Leela K. Gudupudi, Charles Bouveyron, Frederic Precioso
http://arxiv.org/abs/2106.03821v1

• [cs.SE]Clone-Seeker: Effective Code Clone Search Using Annotations
Muhammad Hammad, Önder Babur, Hamid Abdul Basit, Mark van den Brand
http://arxiv.org/abs/2106.03042v1

• [cs.SE]Deterministic Iteratively Built KD-Tree with KNN Search for Exact Applications
Aryan Naim, Joseph Bowkett, Sisir Karumanchi, Peyman Tavallali, Brett Kennedy
http://arxiv.org/abs/2106.03799v1

• [cs.SE]Discovery of Layered Software Architecture from Source Code Using Ego Networks
Sanjay Thakare, Arvind W Kiwelekar
http://arxiv.org/abs/2106.03040v1

• [cs.SE]Redefining measures of Layered Architecture
Sanjay Thakare, Arvind W Kiwelekar
http://arxiv.org/abs/2106.03079v1

• [cs.SE]Understanding Neural Code Intelligence Through Program Simplification
Md Rafiqul Islam Rabin, Vincent J. Hellendoorn, Mohammad Amin Alipour
http://arxiv.org/abs/2106.03353v1

• [cs.SI]A Generative Node-attribute Network Model for Detecting Generalized Structure
Wei Liu, Zhenhai Chang, Caiyan Jia, Yimei Zheng
http://arxiv.org/abs/2106.02878v1

• [cs.SI]A Pre-training Oracle for Predicting Distances in Social Networks
Gunjan Mahindre, Randy Paffenroth, Anura Jayasumana, Rasika Karkare
http://arxiv.org/abs/2106.03233v1

• [cs.SI]Assessing Attendance by Peer Information
Pan Deng, Jianjun Zhou, Jing Lyu, Zitong Zhao
http://arxiv.org/abs/2106.03148v1

• [cs.SI]DyDiff-VAE: A Dynamic Variational Framework for Information Diffusion Prediction
Ruijie Wang, Zijie Huang, Shengzhong Liu, Huajie Shao, Dongxin Liu, Jinyang Li, Tianshi Wang, Dachun Sun, Shuochao Yao, Tarek Abdelzaher
http://arxiv.org/abs/2106.03251v1

• [cs.SI]Faster and Generalized Temporal Triangle Counting, via Degeneracy Ordering
Noujan Pashanasangi, C. Seshadhri
http://arxiv.org/abs/2106.02762v1

• [cs.SI]IM-META: Influence Maximization Using Node Metadata in Networks With Unknown Topology
Cong Tran, Won-Yong Shin, Andreas Spitz
http://arxiv.org/abs/2106.02926v1

• [cs.SI]Network Inference and Influence Maximization from Samples
Wei Chen, Xiaoming Sun, Jialin Zhang, Zhijie Zhang
http://arxiv.org/abs/2106.03403v1

• [cs.SI]Popularity is linked to neural coordination: Neural evidence for an Anna Karenina principle in social networks
Elisa C. Baek, Ryan Hyon, Karina López, Emily S. Finn, Mason A. Porter, Carolyn Parkinson
http://arxiv.org/abs/2106.02726v1

• [cs.SI]The spreading potential problem
Balázs R. Sziklai, Balázs Lengyel
http://arxiv.org/abs/2106.02707v1

• [econ.EM]On the “mementum” of Meme Stocks
Michele Costola, Matteo Iacopini, Carlo R. M. A. Santagiustina
http://arxiv.org/abs/2106.03691v1

• [eess.AS]An Attribute-Aligned Strategy for Learning Speech Representation
Yu-Lin Huang, Bo-Hao Su, Y. -W. Peter Hong, Chi-Chun Lee
http://arxiv.org/abs/2106.02810v1

• [eess.AS]Meta-StyleSpeech : Multi-Speaker Adaptive Text-to-Speech Generation
Dongchan Min, Dong Bok Lee, Eunho Yang, Sung Ju Hwang
http://arxiv.org/abs/2106.03153v1

• [eess.AS]Unsupervised Clustered Federated Learning in Complex Multi-source Acoustic Environments
Alexandru Nelus, Rene Glitza, Rainer Martin
http://arxiv.org/abs/2106.03671v1

• [eess.AS]Weakly-supervised word-level pronunciation error detection in non-native English speech
Daniel Korzekwa, Jaime Lorenzo-Trueba, Thomas Drugman, Shira Calamaro, Bozena Kostek
http://arxiv.org/abs/2106.03494v1

• [eess.IV]A Deep Variational Bayesian Framework for Blind Image Deblurring
Hui Wang, Zongsheng Yue, Qian Zhao, Deyu Meng
http://arxiv.org/abs/2106.02884v1

• [eess.IV]AOSLO-net: A deep learning-based method for automatic segmentation of retinal microaneurysms from adaptive optics scanning laser ophthalmoscope images
Qian Zhang, Konstantina Sampani, Mengjia Xu, Shengze Cai, Yixiang Deng, He Li, Jennifer K. Sun, George Em Karniadakis
http://arxiv.org/abs/2106.02800v1

• [eess.IV]Brain Age Estimation From MRI Using Cascade Networks with Ranking Loss
Jian Cheng, Ziyang Liu, Hao Guan, Zhenzhou Wu, Haogang Zhu, Jiyang Jiang, Wei Wen, Dacheng Tao, Tao Liu
http://arxiv.org/abs/2106.03052v1

• [eess.IV]Deep Learning-based Type Identification of Volumetric MRI Sequences
Jean Pablo Vieira de Mello, Thiago M. Paixão, Rodrigo Berriel, Mauricio Reyes, Claudine Badue, Alberto F. De Souza, Thiago Oliveira-Santos
http://arxiv.org/abs/2106.03208v1

• [eess.IV]Deep Neural Network-based Enhancement for Image and Video Streaming Systems: A Survey and Future Directions
Royson Lee, Stylianos I. Venieris, Nicholas D. Lane
http://arxiv.org/abs/2106.03727v1

• [eess.IV]Hierarchical Temperature Imaging Using Pseudo-Inversed Convolutional Neural Network Aided TDLAS Tomography
Jingjing Si, Guoliang Li, Yinbo Cheng, Rui Zhang, Godwin Enemali, Chang Liu
http://arxiv.org/abs/2106.02901v1

• [eess.IV]Knowledge-aware Deep Framework for Collaborative Skin Lesion Segmentation and Melanoma Recognition
Xiaohong Wang, Xudong Jiang, Henghui Ding, Yuqian Zhao, Jun Liu
http://arxiv.org/abs/2106.03455v1

• [eess.IV]Pointwise visual field estimation from optical coherence tomography in glaucoma: a structure-function analysis using deep learning
Ruben Hemelings, Bart Elen, João Barbosa Breda, Erwin Bellon, Matthew B Blaschko, Patrick De Boever, Ingeborg Stalmans
http://arxiv.org/abs/2106.03793v1

• [eess.SP]3D UAV Trajectory and Data Collection Optimisation via Deep Reinforcement Learning
Khoi Khac Nguyen, Trung Q. Duong, Tan Do-Duy, Holger Claussen, and Lajos Hanzo
http://arxiv.org/abs/2106.03129v1

• [eess.SP]Closed-Loop Wireless Power Transfer with Adaptive Waveform and Beamforming: Design, Prototype, and Experiment
Shanpu Shen, Junghoon Kim, Bruno Clerckx
http://arxiv.org/abs/2106.03519v1

• [eess.SP]Deep Unfolding of Iteratively Reweighted ADMM for Wireless RF Sensing
Udaya S. K. P. Miriya Thanthrige, Peter Jung, Aydin Sezgin
http://arxiv.org/abs/2106.03686v1

• [eess.SP]Machine Learning Based Anxiety Detection in Older Adults using Wristband Sensors and Context Feature
Rajdeep Kumar Nath, Himanshu Thapliyal
http://arxiv.org/abs/2106.03019v1

• [eess.SY]Controller Synthesis for Omega-Regular and Steady-State Specifications
Alvaro Velasquez, Ashutosh Trivedi, Ismail Alkhouri, Andre Beckus, George Atia
http://arxiv.org/abs/2106.02951v1

• [eess.SY]Effect of Adaptive and Fixed Shared Steering Control on Distracted Driver Behavior
Zheng Wang, Satoshi Suga, Edric John Cruz Nacpil, Bo Yang, Kimihiko Nakano
http://arxiv.org/abs/2106.03364v1

• [eess.SY]Multi-armed Bandit Algorithms on System-on-Chip: Go Frequentist or Bayesian?
S. V. Sai Santosh, Sumit J. Darak
http://arxiv.org/abs/2106.02855v1

• [eess.SY]Singular Dynamic Mode Decompositions
Joel A. Rosenfeld, Rushikesh Kamalapurkar
http://arxiv.org/abs/2106.02639v1

• [math.
93e
AG]Neurons on Amoebae
Jiakang Bao, Yang-Hui He, Edward Hirst
http://arxiv.org/abs/2106.03695v1

• [math.AC]Learning a performance metric of Buchberger’s algorithm
Jelena Mojsilović, Dylan Peifer, Sonja Petrović
http://arxiv.org/abs/2106.03676v1

• [math.OC]Decentralized Optimization with Heterogeneous Delays: a Continuous-Time Approach
Mathieu Even, Hadrien Hendrikx, Laurent Massoulie
http://arxiv.org/abs/2106.03585v1

• [math.OC]Dynamics of Stochastic Momentum Methods on Large-scale, Quadratic Models
Courtney Paquette, Elliot Paquette
http://arxiv.org/abs/2106.03696v1

• [math.OC]MURANA: A Generic Framework for Stochastic Variance-Reduced Optimization
Laurent Condat, Peter Richtárik
http://arxiv.org/abs/2106.03056v1

• [math.OC]Minibatch and Momentum Model-based Methods for Stochastic Non-smooth Non-convex Optimization
Qi Deng, Wenzhi Gao
http://arxiv.org/abs/2106.03034v1

• [math.OC]On the Optimality of Backward Regression: Sparse Recovery and Subset Selection
Sebatian Ament, Carla Gomes
http://arxiv.org/abs/2106.03235v1

• [math.OC]Random features for adaptive nonlinear control and prediction
Nicholas M. Boffi, Stephen Tu, Jean-Jacques E. Slotine
http://arxiv.org/abs/2106.03589v1

• [math.OC]Signatured Deep Fictitious Play for Mean Field Games with Common Noise
Ming Min, Ruimeng Hu
http://arxiv.org/abs/2106.03272v1

• [math.ST]A sparse model with covariates for directed networks
Qiuping Wang
http://arxiv.org/abs/2106.03285v1

• [math.ST]Confidence bands for exponential distribution functions under progressive type-II censoring
Stefan Bedbur, Fabian Mies
http://arxiv.org/abs/2106.02727v1

• [math.ST]Semiparametric inference on Gini indices of two semicontinuous populations under density ratio models
Meng Yuan, Pengfei Li, Changbao Wu
http://arxiv.org/abs/2106.02741v1

• [math.ST]Superconsistency of tests in high dimensions
Anders Bredahl Kock, David Preinerstorfer
http://arxiv.org/abs/2106.03700v1

• [math.ST]Tempered Stable Autoregressive Models
Niharika Bhootna, Arun Kumar
http://arxiv.org/abs/2106.03187v1

• [math.ST]The basic distributional theory for the product of zero mean correlated normal random variables
Robert E. Gaunt
http://arxiv.org/abs/2106.02897v1

• [math.ST]Towards Practical Mean Bounds for Small Samples
My Phan, Philip S. Thomas, Erik Learned-Miller
http://arxiv.org/abs/2106.03163v1

• [q-bio.NC]A Computational Model of Representation Learning in the Brain Cortex, Integrating Unsupervised and Reinforcement Learning
Giovanni Granato, Emilio Cartoni, Federico Da Rold, Andrea Mattera, Gianluca Baldassarre
http://arxiv.org/abs/2106.03688v1

• [q-bio.NC]Neural dSCA: demixing multimodal interaction among brain areas during naturalistic experiments
Yu Takagi, Laurence T. Hunt, Ryu Ohata, Hiroshi Imamizu, Jun-ichiro Hirayama
http://arxiv.org/abs/2106.02948v1

• [q-bio.PE]The evolving usefulness of the Test-Negative Design in studying risk factors for COVID-19 due to changes in testing policy
Jan P Vandenbroucke, Elizabeth B Brickley, Christina M. J. E. Vandenbroucke-Grauls, Neil Pearce
http://arxiv.org/abs/2106.03713v1

• [q-fin.TR]Online Trading Models in the Forex Market Considering Transaction Costs
Koya Ishikawa, Kazuhide Nakata
http://arxiv.org/abs/2106.03035v1

• [quant-ph]A Review of Machine Learning Classification Using Quantum Annealing for Real-world Applications
Rajdeep Kumar Nath, Himanshu Thapliyal, Travis S. Humble
http://arxiv.org/abs/2106.02964v1

• [quant-ph]Predicting Quantum Potentials by Deep Neural Network and Metropolis Sampling
Rui Hong, Peng-Fei Zhou, Bin Xi, Jie Hu, An-Chun Ji, Shi-Ju Ran
http://arxiv.org/abs/2106.03126v1

• [quant-ph]The Inductive Bias of Quantum Kernels
Jonas M. Kübler, Simon Buchholz, Bernhard Schölkopf
http://arxiv.org/abs/2106.03747v1

• [stat.AP]Bayesian Time Varying Coefficient Model with Applications to Marketing Mix Modeling
Edwin Ng, Zhishi Wang, Athena Dai
http://arxiv.org/abs/2106.03322v1

• [stat.AP]Connection between forest fire emission and COVID-19 incidents in West Coast regions of the United States
Srikanta Sannigrahi, Arabinda Maiti, Francesco Pilla, Qi Zhang, Somnath Bar, Saskia Keesstra, Artemi Cerda
http://arxiv.org/abs/2106.03130v1

• [stat.AP]Estimating the number of entities with vacancies using administrative and online data
Maciej Beręsewicz, Herman Cherniaiev, Robert Pater
http://arxiv.org/abs/2106.03263v1

• [stat.AP]Latent class growth analysis for ordinal response data in the Distress Assessment and Response Tool: an evaluation of state-of-the-art implementations
Jianhui Gao, Aliza Panjwani, Madeline Li, Osvaldo Espin-Garcia
http://arxiv.org/abs/2106.03697v1

• [stat.AP]Odds Ratios are far from “portable”: A call to use realistic models for effect variation in meta-analysis
Mengli Xiao, Haitao Chu, Stephen Cole, Yong Chen, Richard MacLehose, David Richardson, Sander Greenland
http://arxiv.org/abs/2106.02673v1

• [stat.AP]Proper Scoring Rules for Missing Value Imputation
Loris Michel, Jeffrey Näf, Meta-Lina Spohn, Nicolai Meinshausen
http://arxiv.org/abs/2106.03742v1

• [stat.ME]A consistent nonparametric test of the effect of dementia duration on mortality
L. Radloff, R. Weissbach, C. Reinke, G. Doblhammer
http://arxiv.org/abs/2106.03372v1

• [stat.ME]A multivariate Gaussian random field prior against spatial confounding
Isa Marques, Thomas Kneib, Nadja Klein
http://arxiv.org/abs/2106.03737v1

• [stat.ME]Bayesian graphical modelling for heterogeneous causal effects
Federico Castelletti, Guido Consonni
http://arxiv.org/abs/2106.03252v1

• [stat.ME]Causal aggregation: estimation and inference of causal effects by constraint-based data fusion
Jaime Roquero Gimenez, Dominik Rothenhäusler
http://arxiv.org/abs/2106.03024v1

• [stat.ME]Cluster Analysis via Random Partition Distributions
David B. Dahl, Jacob Andros, J. Brandon Carter
http://arxiv.org/abs/2106.02760v1

• [stat.ME]Estimating the size of a closed population by modeling latent and observed heterogeneity
Antonio Forcina, Francesco Bartolucci
http://arxiv.org/abs/2106.03811v1

• [stat.ME]Fisher-Pitman permutation tests based on nonparametric Poisson mixtures with application to single cell genomics
Zhen Miao, Weihao Kong, Ramya Korlakai Vinayak, Wei Sun, Fang Han
http://arxiv.org/abs/2106.03022v1

• [stat.ME]Hierarchical Bayesian Mixture Models for Time Series Using Context Trees as State Space Partitions
Ioannis Papageorgiou, Ioannis Kontoyiannis
http://arxiv.org/abs/2106.03023v1

• [stat.ME]High-dimensional Bayesian model selection by proximal nested sampling
Xiaohao Cai, Jason D. McEwen, Marcelo Pereyra
http://arxiv.org/abs/2106.03646v1

• [stat.ME]Hypothesis Testing for Hierarchical Structures in Cognitive Diagnosis Models
Chenchen Ma, Gongjun Xu
http://arxiv.org/abs/2106.03218v1

• [stat.ME]Joint Learning of Multiple Differential Networks with fMRI data for Brain Connectivity Alteration Detection
Hao Chen, Ying Guo, Yong He, Dong Liu, Lei Liu, Xiao-Hua Zhou
http://arxiv.org/abs/2106.03334v1

• [stat.ME]Modeling Nonstationary Time Series using Locally Stationary Basis Processes
Shreyan Ganguly, Peter F. Craigmile
http://arxiv.org/abs/2106.03533v1

• [stat.ME]Parameter Estimation for Grouped Data Using EM and MCEM Algorithms
Zahra A. Shirazi, João Pedro A. R. da Silva, Camila P. E. de Souza
http://arxiv.org/abs/2106.02909v1

• [stat.ME]Safe Tests and Always-Valid Confidence Intervals for contingency tables and beyond
Rosanne Turner, Alexander Ly, Peter Grünwald
http://arxiv.org/abs/2106.02693v1

• [stat.ME]Seemingly Unrelated Multi-State processes: a Bayesian semiparametric approach
Andrea Cremaschi, Raffele Argiento, Maria De Iorio, Cai Shirong, Yap Seng Chong, Michael J. Meaney, Michelle Z. L. Kee
http://arxiv.org/abs/2106.03072v1

• [stat.ME]Semi-Supervised Statistical Inference for High-Dimensional Linear Regression with Blockwise Missing Data
Fei Xue, Rong Ma, Hongzhe Li
http://arxiv.org/abs/2106.03344v1

• [stat.ME]Simultaneous Confidence Corridors for Mean Functions in Functional Data Analysis of Imaging Data
Yueying Wang, Guannan Wang, Li Wang, R. Todd Ogden
http://arxiv.org/abs/2106.02718v1

• [stat.ME]Statistical Inference for Cox Proportional Hazards Models with a Diverging Number of Covariates
Lu Xia, Bin Nan, Yi Li
http://arxiv.org/abs/2106.03244v1

• [stat.ME]Statistical summaries of unlabelled evolutionary trees and ranked hierarchical clustering trees
Samyak Rajanala, Julia A. Palacios
http://arxiv.org/abs/2106.02724v1

• [stat.ML]A Distance Covariance-based Kernel for Nonlinear Causal Clustering in Heterogeneous Populations
Alex Markham, Moritz Grosse-Wentrup
http://arxiv.org/abs/2106.03480v1

• [stat.ML]Accurate and robust Shapley Values for explaining predictions and focusing on local important variables
Salim I. Amoukou, Nicolas J-B. Brunel, Tangi Salaün
http://arxiv.org/abs/2106.03820v1

• [stat.ML]BayesIMP: Uncertainty Quantification for Causal Data Fusion
Siu Lun Chau, Jean-François Ton, Javier González, Yee Whye Teh, Dino Sejdinovic
http://arxiv.org/abs/2106.03477v1

• [stat.ML]Calibrating multi-dimensional complex ODE from noisy data via deep neural networks
Kexuan Li, Fangfang Wang, Ruiqi Liu, Fan Yang, Zuofeng Shang
http://arxiv.org/abs/2106.03591v1

• [stat.ML]Can a single neuron learn quantiles?
Edgardo Solano-Carrillo
http://arxiv.org/abs/2106.03702v1

• [stat.ML]Causal Bandits with Unknown Graph Structure
Yangyi Lu, Amirhossein Meisami, Ambuj Tewari
http://arxiv.org/abs/2106.02988v1

• [stat.ML]Data-driven discovery of interacting particle systems using Gaussian processes
Jinchao Feng, Yunxiang Ren, Sui Tang
http://arxiv.org/abs/2106.02735v1

• [stat.ML]Deep Particulate Matter Forecasting Model Using Correntropy-Induced Loss
Jongsu Kim, Changhoon Lee
http://arxiv.org/abs/2106.03032v1

• [stat.ML]Evaluating State-of-the-Art Classification Models Against Bayes Optimality
Ryan Theisen, Huan Wang, Lav R. Varshney, Caiming Xiong, Richard Socher
http://arxiv.org/abs/2106.03357v1

• [stat.ML]Fast and Robust Online Inference with Stochastic Gradient Descent via Random Scaling
Sokbae Lee, Yuan Liao, Myung Hwan Seo, Youngki Shin
http://arxiv.org/abs/2106.03156v1

• [stat.ML]Generalized Linear Bandits with Local Differential Privacy
Yuxuan Han, Zhipeng Liang, Yang Wang, Jiheng Zhang
http://arxiv.org/abs/2106.03365v1

• [stat.ML]Heavy Tails in SGD and Compressibility of Overparametrized Neural Networks
Melih Barsbey, Milad Sefidgaran, Murat A. Erdogdu, Gaël Richard, Umut Şimşekli
http://arxiv.org/abs/2106.03795v1

• [stat.ML]How Tight Can PAC-Bayes be in the Small Data Regime?
Andrew Y. K. Foong, Wessel P. Bruinsma, David R. Burt, Richard E. Turner
http://arxiv.org/abs/2106.03542v1

• [stat.ML]How to Evaluate Uncertainty Estimates in Machine Learning for Regression?
Laurens Sluijterman, Eric Cator, Tom Heskes
http://arxiv.org/abs/2106.03395v1

• [stat.ML]Improved Predictive Uncertainty using Corruption-based Calibration
Tiago Salvador, Vikram Voleti, Alexander Iannantuono, Adam Oberman
http://arxiv.org/abs/2106.03762v1

• [stat.ML]Learning Curves for SGD on Structured Features
Blake Bordelon, Cengiz Pehlevan
http://arxiv.org/abs/2106.02713v1

• [stat.ML]Learning Gaussian Mixtures with Generalised Linear Models: Precise Asymptotics in High-dimensions
Bruno Loureiro, Gabriele Sicuro, Cédric Gerbelot, Alessandro Pacco, Florent Krzakala, Lenka Zdeborová
http://arxiv.org/abs/2106.03791v1

• [stat.ML]Learning Treatment Effects in Panels with General Intervention Patterns
Vivek F. Farias, Andrew A. Li, Tianyi Peng
http://arxiv.org/abs/2106.02780v1

• [stat.ML]Multivariate Probabilistic Regression with Natural Gradient Boosting
Michael O’Malley, Adam M. Sykulski, Rick Lumpkin, Alejandro Schuler
http://arxiv.org/abs/2106.03823v1

• [stat.ML]Navigating to the Best Policy in Markov Decision Processes
Aymen Al Marjani, Aurélien Garivier, Alexandre Proutiere
http://arxiv.org/abs/2106.02847v1

• [stat.ML]Network Estimation by Mixing: Adaptivity and More
Tianxi Li, Can M. Le
http://arxiv.org/abs/2106.02803v1

• [stat.ML]Neural Tangent Kernel Maximum Mean Discrepancy
Xiuyuan Cheng, Yao Xie
http://arxiv.org/abs/2106.03227v1

• [stat.ML]On Inductive Biases for Heterogeneous Treatment Effect Estimation
Alicia Curth, Mihaela van der Schaar
http://arxiv.org/abs/2106.03765v1

• [stat.ML]Regularization in ResNet with Stochastic Depth
Soufiane Hayou, Fadhel Ayed
http://arxiv.org/abs/2106.03091v1

• [stat.ML]Representation mitosis in wide neural networks
Diego Doimo, Aldo Glielmo, Sebastian Goldt, Alessandro Laio
http://arxiv.org/abs/2106.03485v1

• [stat.ML]Robust Stochastic Linear Contextual Bandits Under Adversarial Attacks
Qin Ding, Cho-Jui Hsieh, James Sharpnack
http://arxiv.org/abs/2106.02978v1

• [stat.ML]Syndicated Bandits: A Framework for Auto Tuning Hyper-parameters in Contextual Bandit Algorithms
Qin Ding, Yi-Wei Liu, Cho-Jui Hsieh, James Sharpnack
http://arxiv.org/abs/2106.02979v1

• [stat.ML]Towards an Understanding of Benign Overfitting in Neural Networks
Zhu Li, Zhi-Hua Zhou, Arthur Gretton
http://arxiv.org/abs/2106.03212v1

• [stat.ML]Unbiased Self-Play
Shohei Ohsawa
http://arxiv.org/abs/2106.03007v1