论文推介 - 今日学术视野(2021.10.30) - 《爱可可老师分享》

astro-ph.EP - 地球与行星天体
cond-mat.soft - 软凝聚物质
cs.AI - 人工智能
cs.AR - 硬件体系结构
cs.CL - 计算与语言
cs.CR - 加密与安全
cs.CV - 机器视觉与模式识别
cs.CY - 计算与社会
cs.DC - 分布式、并行与集群计算
cs.DS - 数据结构与算法
cs.HC - 人机接口
cs.IR - 信息检索
cs.IT - 信息论
cs.LG - 自动学习
cs.MA - 多代理系统
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.SI - 社交网络与信息网络
eess.IV - 图像与视频处理
eess.SP - 信号处理
eess.SY - 系统和控制
math.CO - 组合数学
math.DS - 动力系统
math.FA - 泛函演算
math.OC - 优化与控制
math.PR - 概率
math.ST - 统计理论
physics.ao-ph - 大气和海洋物理
physics.chem-ph -化学物理
physics.flu-dyn - 流体动力学
q-bio.GN - 基因组学
q-bio.NC - 神经元与认知
q-bio.PE - 人口与发展
q-bio.QM - 定量方法
q-fin.ST - 统计金融学
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
stat.OT - 其他统计学

• [astro-ph.EP]Exoplanet atmosphere evolution: emulation with random forests
• [cond-mat.soft]Designing Machine Learning Surrogates using Outputs of Molecular Dynamics Simulations as Soft Labels
• [cs.AI]A Scenario-Based Platform for Testing Autonomous Vehicle Behavior Prediction Models in Simulation
• [cs.AI]Conditional Inference and Activation of Knowledge Entities in ACT-R
• [cs.AI]End-to-End Speech Emotion Recognition: Challenges of Real-Life Emergency Call Centers Data Recordings
• [cs.AI]NeuroComb: Improving SAT Solving with Graph Neural Networks
• [cs.AR]SIMCNN — Exploiting Computational Similarity to Accelerate CNN Training in Hardware
• [cs.CL]A Sequence to Sequence Model for Extracting Multiple Product Name Entities from Dialog
• [cs.CL]Anomaly-Injected Deep Support Vector Data Description for Text Outlier Detection
• [cs.CL]BERTian Poetics: Constrained Composition with Masked LMs
• [cs.CL]Bridge the Gap Between CV and NLP! A Gradient-based Textual Adversarial Attack Framework
• [cs.CL]Cognitive network science quantifies feelings expressed in suicide letters and Reddit mental health communities
• [cs.CL]Combining Vagueness Detection with Deep Learning to Identify Fake News
• [cs.CL]Confounds and Overestimations in Fake Review Detection: Experimentally Controlling for Product-Ownership and Data-Origin
• [cs.CL]Detecting Dementia from Speech and Transcripts using Transformers
• [cs.CL]Diversity-Driven Combination for Grammatical Error Correction
• [cs.CL]Emoji-aware Co-attention Network with EmoGraph2vec Model for Sentiment Anaylsis
• [cs.CL]Empirical Analysis of Korean Public AI Hub Parallel Corpora and in-depth Analysis using LIWC
• [cs.CL]Hate Speech Classifiers Learn Human-Like Social Stereotypes
• [cs.CL]Multi-stage Clarification in Conversational AI: The case of Question-Answering Dialogue Systems
• [cs.CL]Pruning Attention Heads of Transformer Models Using A* Search: A Novel Approach to Compress Big NLP Architectures
• [cs.CL]Semi-Siamese Bi-encoder Neural Ranking Model Using Lightweight Fine-Tuning
• [cs.CL]Towards Realistic Single-Task Continuous Learning Research for NER
• [cs.CL]When is BERT Multilingual? Isolating Crucial Ingredients for Cross-lingual Transfer
• [cs.CL]ÚFAL at MultiLexNorm 2021: Improving Multilingual Lexical Normalization by Fine-tuning ByT5
• [cs.CR]Authentication Attacks on Projection-based Cancelable Biometric Schemes
• [cs.CR]Four-dimensional hybrid chaos system and its application in creating a secure image transfer environment by cellular automata
• [cs.CR]Masked LARk: Masked Learning, Aggregation and Reporting worKflow
• [cs.CV]3D Object Tracking with Transformer
• [cs.CV]A Comparative Study of Coarse to Dense 3D Indoor Scene Registration Algorithms
• [cs.CV]A Geometric Perspective towards Neural Calibration via Sensitivity Decomposition
• [cs.CV]A Survey of Self-Supervised and Few-Shot Object Detection
• [cs.CV]A recursive robust filtering approach for 3D registration
• [cs.CV]Audio-visual Representation Learning for Anomaly Events Detection in Crowds
• [cs.CV]BI-GCN: Boundary-Aware Input-Dependent Graph Convolution Network for Biomedical Image Segmentation
• [cs.CV]Blending Anti-Aliasing into Vision Transformer
• [cs.CV]Bridging Non Co-occurrence with Unlabeled In-the-wild Data for Incremental Object Detection
• [cs.CV]Characterizing and Taming Resolution in Convolutional Neural Networks
• [cs.CV]Contrast and Mix: Temporal Contrastive Video Domain Adaptation with Background Mixing
• [cs.CV]Deformable Registration of Brain MR Images via a Hybrid Loss
• [cs.CV]Dispensed Transformer Network for Unsupervised Domain Adaptation
• [cs.CV]DocScanner: Robust Document Image Rectification with Progressive Learning
• [cs.CV]Dynamic Visual Reasoning by Learning Differentiable Physics Models from Video and Language
• [cs.CV]End-to-end Learning the Partial Permutation Matrix for Robust 3D Point Cloud Registration
• [cs.CV]Explicitly Modeling the Discriminability for Instance-Aware Visual Object Tracking
• [cs.CV]Facial Emotion Recognition: A multi-task approach using deep learning
• [cs.CV]FocusFace: Multi-task Contrastive Learning for Masked Face Recognition
• [cs.CV]GPU based GMM segmentation of kinect data
• [cs.CV]Image Comes Dancing with Collaborative Parsing-Flow Video Synthesis
• [cs.CV]Intermediate Layers Matter in Momentum Contrastive Self Supervised Learning
• [cs.CV]LF-YOLO: A Lighter and Faster YOLO for Weld Defect Detection of X-ray Image
• [cs.CV]MCUNetV2: Memory-Efficient Patch-based Inference for Tiny Deep Learning
• [cs.CV]MEGAN: Memory Enhanced Graph Attention Network for Space-Time Video Super-Resolution
• [cs.CV]MedMNIST v2: A Large-Scale Lightweight Benchmark for 2D and 3D Biomedical Image Classification
• [cs.CV]Meta Guided Metric Learner for Overcoming Class Confusion in Few-Shot Road Object Detection
• [cs.CV]ODMTCNet: An Interpretable Multi-view Deep Neural Network Architecture for Image Feature Representation
• [cs.CV]Privacy Aware Person Detection in Surveillance Data
• [cs.CV]Self-Supervised Learning Disentangled Group Representation as Feature
• [cs.CV]SiamPolar: Semi-supervised Realtime Video Object Segmentation with Polar Representation
• [cs.CV]Skeleton-Based Mutually Assisted Interacted Object Localization and Human Action Recognition
• [cs.CV]Sliding Sequential CVAE with Time Variant Socially-aware Rethinking for Trajectory Prediction
• [cs.CV]SpineOne: A One-Stage Detection Framework for Degenerative Discs and Vertebrae
• [cs.CV]Subpixel object segmentation using wavelets and multi resolution analysis
• [cs.CV]Temporal-attentive Covariance Pooling Networks for Video Recognition
• [cs.CV]Towards Large-Scale Rendering of Simulated Crops for Synthetic Ground Truth Generation on Modular Supercomputers
• [cs.CV]UltraPose: Synthesizing Dense Pose with 1 Billion Points by Human-body Decoupling 3D Model
• [cs.CV]Vision Transformer for Classification of Breast Ultrasound Images
• [cs.CV]XDEEP-MSI: Explainable Bias-Rejecting Microsatellite Instability Deep Learning System In Colorectal Cancer
• [cs.CY]On the Fairness of Machine-Assisted Human Decisions
• [cs.DC]Bolt: Bridging the Gap between Auto-tuners and Hardware-native Performance
• [cs.DC]NetDAM: Network Direct Attached Memory with Programmable In-Memory Computing ISA
• [cs.DC]OneFlow: Redesign the Distributed Deep Learning Framework from Scratch
• [cs.DC]Pipeline Parallelism for Inference on Heterogeneous Edge Computing
• [cs.DC]Xar-Trek: Run-time Execution Migration among FPGAs and Heterogeneous-ISA CPUs
• [cs.DS]Better Sum Estimation via Weighted Sampling
• [cs.HC]An Analysis of Programming Course Evaluations Before and After the Introduction of an Autograder
• [cs.HC]E-ffective: A Visual Analytic System for Exploring the Emotion and Effectiveness of Inspirational Speeches
• [cs.HC]Telling Creative Stories Using Generative Visual Aids
• [cs.IR]An AI-based Approach for Tracing Content Requirements in Financial Documents
• [cs.IR]Cross-Batch Negative Sampling for Training Two-Tower Recommenders
• [cs.IR]D2RLIR : an improved and diversified ranking function in interactive recommendation systems based on deep reinforcement learning
• [cs.IR]Dynamic Review-based Recommenders
• [cs.IR]From Intrinsic to Counterfactual: On the Explainability of Contextualized Recommender Systems
• [cs.IR]Hierarchical User Intent Graph Network forMultimedia Recommendation
• [cs.IR]UltraGCN: Ultra Simplification of Graph Convolutional Networks for Recommendation
• [cs.IT]Color image restoration with impulse noise based on fractional-order total variation and framelet
• [cs.IT]Context-Tree-Based Lossy Compression and Its Application to CSI Representation
• [cs.IT]DeepNP: Deep Learning-Based Noise Prediction for Ultra-Reliable Low-Latency Communications
• [cs.IT]Distributed Joint Multi-cell Optimization of IRS Parameters with Linear Precoders
• [cs.IT]Exact Analytical Model of Age of Information in Multi-source Status Update Systems with Per-source Queueing
• [cs.IT]Feature Learning for Neural-Network-Based Positioning with Channel State Information
• [cs.IT]Identification over Compound MIMO Broadcast Channels
• [cs.IT]Multi-Pair Two-Way Massive MIMO DF Relaying Over Rician Fading Channels Under Imperfect CSI
• [cs.IT]NOMA Joint Decoding based on Soft-Output Ordered-Statistics Decoder for Short Block Codes
• [cs.IT]Pilot Optimization and Channel Estimation for Two-way Relaying Network Aided by IRS with Finite Discrete Phase Shifters
• [cs.LG]A Game-Theoretic Approach for Improving Generalization Ability of TSP Solvers
• [cs.LG]AEVA: Black-box Backdoor Detection Using Adversarial Extreme Value Analysis
• [cs.LG]Accelerating Robotic Reinforcement Learning via Parameterized Action Primitives
• [cs.LG]Active-LATHE: An Active Learning Algorithm for Boosting the Error Exponent for Learning Homogeneous Ising Trees
• [cs.LG]Aggregation as Unsupervised Learning and its Evaluation
• [cs.LG]Algorithmic encoding of protected characteristics and its implications on disparities across subgroups
• [cs.LG]An Operator Theoretic Perspective on Pruning Deep Neural Networks
• [cs.LG]Approximate Decomposable Submodular Function Minimization for Cardinality-Based Components
• [cs.LG]Bayesian Sequential Optimal Experimental Design for Nonlinear Models Using Policy Gradient Reinforcement Learning
• [cs.LG]Brain-inspired feature exaggeration in generative replay for continual learning
• [cs.LG]CAFE: Catastrophic Data Leakage in Vertical Federated Learning
• [cs.LG]CAP: Co-Adversarial Perturbation on Weights and Features for Improving Generalization of Graph Neural Networks
• [cs.LG]Choosing the Best of Both Worlds: Diverse and Novel Recommendations through Multi-Objective Reinforcement Learning
• [cs.LG]Class-wise Thresholding for Detecting Out-of-Distribution Data
• [cs.LG]Colossal-AI: A Unified Deep Learning System For Large-Scale Parallel Training
• [cs.LG]Communication-Efficient ADMM-based Federated Learning
• [cs.LG]Computational Intelligence and Deep Learning for Next-Generation Edge-Enabled Industrial IoT
• [cs.LG]Conditioning Sparse Variational Gaussian Processes for Online Decision-making
• [cs.LG]Confidence-Aware Imitation Learning from Demonstrations with Varying Optimality
• [cs.LG]Coresets for Time Series Clustering
• [cs.LG]Differentiable NAS Framework and Application to Ads CTR Prediction
• [cs.LG]Dist2Cycle: A Simplicial Neural Network for Homology Localization
• [cs.LG]Explaining Latent Representations with a Corpus of Examples
• [cs.LG]Exploration of Algorithmic Trading Strategies for the Bitcoin Market
• [cs.LG]Exploring Covariate and Concept Shift for Detection and Calibration of Out-of-Distribution Data
• [cs.LG]Extracting Clinician’s Goals by What-if Interpretable Modeling
• [cs.LG]FeO2: Federated Learning with Opt-Out Differential Privacy
• [cs.LG]Fighting the curse of dimensionality: A machine learning approach to finding global optima
• [cs.LG]Finite Horizon Q-learning: Stability, Convergence and Simulations
• [cs.LG]Generalized Anomaly Detection
• [cs.LG]Generalized Depthwise-Separable Convolutions for Adversarially Robust and Efficient Neural Networks
• [cs.LG]Generating Table Vector Representations
• [cs.LG]Gradient Inversion with Generative Image Prior
• [cs.LG]Guided Evolution for Neural Architecture Search
• [cs.LG]Hindsight Goal Ranking on Replay Buffer for Sparse Reward Environment
• [cs.LG]How to boost autoencoders?
• [cs.LG]Identifiable Generative Models for Missing Not at Random Data Imputation
• [cs.LG]Improving Causal Effect Estimation of Weighted RegressionBased Estimator using Neural Networks
• [cs.LG]L2ight: Enabling On
aef
-Chip Learning for Optical Neural Networks via Efficient in-situ Subspace Optimization
• [cs.LG]Learning Aggregations of Binary Activated Neural Networks with Probabilities over Representations
• [cs.LG]Learning Deep Representation with Energy-Based Self-Expressiveness for Subspace Clustering
• [cs.LG]Learning Domain Invariant Representations in Goal-conditioned Block MDPs
• [cs.LG]Learning to Control using Image Feedback
• [cs.LG]Learning to Ground Multi-Agent Communication with Autoencoders
• [cs.LG]Leveraging Recursive Gumbel-Max Trick for Approximate Inference in Combinatorial Spaces
• [cs.LG]Lightweight Mobile Automated Assistant-to-physician for Global Lower-resource Areas
• [cs.LG]MOOMIN: Deep Molecular Omics Network for Anti-Cancer Drug Combination Therapy
• [cs.LG]Meta-Learning Sparse Implicit Neural Representations
• [cs.LG]Modeling Heterogeneous Hierarchies with Relation-specific Hyperbolic Cones
• [cs.LG]Mosaicking to Distill: Knowledge Distillation from Out-of-Domain Data
• [cs.LG]Multi-Task Processes
• [cs.LG]Multivariate Empirical Mode Decomposition based Hybrid Model for Day-ahead Peak Load Forecasting
• [cs.LG]Normality-Calibrated Autoencoder for Unsupervised Anomaly Detection on Data Contamination
• [cs.LG]OMASGAN: Out-of-Distribution Minimum Anomaly Score GAN for Sample Generation on the Boundary
• [cs.LG]On Provable Benefits of Depth in Training Graph Convolutional Networks
• [cs.LG]On the explainability of hospitalization prediction on a large COVID-19 patient dataset
• [cs.LG]Preventing posterior collapse in variational autoencoders for text generation via decoder regularization
• [cs.LG]Proximal Reinforcement Learning: Efficient Off-Policy Evaluation in Partially Observed Markov Decision Processes
• [cs.LG]RGP: Neural Network Pruning through Its Regular Graph Structure
• [cs.LG]RIM: Reliable Influence-based Active Learning on Graphs
• [cs.LG]Rademacher Random Projections with Tensor Networks
• [cs.LG]Regularized Frank-Wolfe for Dense CRFs: Generalizing Mean Field and Beyond
• [cs.LG]Reinforcement Learning in Linear MDPs: Constant Regret and Representation Selection
• [cs.LG]Residual Relaxation for Multi-view Representation Learning
• [cs.LG]Roto-translated Local Coordinate Frames For Interacting Dynamical Systems
• [cs.LG]SIM-ECG: A Signal Importance Mask-driven ECGClassification System
• [cs.LG]SMORE: Knowledge Graph Completion and Multi-hop Reasoning in Massive Knowledge Graphs
• [cs.LG]Sayer: Using Implicit Feedback to Optimize System Policies
• [cs.LG]Scatterbrain: Unifying Sparse and Low-rank Attention Approximation
• [cs.LG]Selective Sampling for Online Best-arm Identification
• [cs.LG]Self-Supervised Representation Learning on Neural Network Weights for Model Characteristic Prediction
• [cs.LG]TRAIL: Near-Optimal Imitation Learning with Suboptimal Data
• [cs.LG]Teaching an Active Learner with Contrastive Examples
• [cs.LG]Temporal-Difference Value Estimation via Uncertainty-Guided Soft Updates
• [cs.LG]The chemical space of terpenes: insights from data science and AI
• [cs.LG]The magnitude vector of images
• [cs.LG]Towards Evaluating the Robustness of Neural Networks Learned by Transduction
• [cs.LG]Towards Hyperparameter-free Policy Selection for Offline Reinforcement Learning
• [cs.LG]Towards Model Agnostic Federated Learning Using Knowledge Distillation
• [cs.LG]Towards a Taxonomy of Graph Learning Datasets
• [cs.LG]URLB: Unsupervised Reinforcement Learning Benchmark
• [cs.LG]Understanding How Encoder-Decoder Architectures Attend
• [cs.LG]Using Time-Series Privileged Information for Provably Efficient Learning of Prediction Models
• [cs.LG]Wasserstein Distance Maximizing Intrinsic Control
• [cs.LG]You Are the Best Reviewer of Your Own Papers: An Owner-Assisted Scoring Mechanism
• [cs.MA]Integrated Task Assignment and Path Planning for Capacitated Multi-Agent Pickup and Delivery
• [cs.NI]Deep Learning Aided Packet Routing in Aeronautical Ad-Hoc Networks Relying on Real Flight Data: From Single-Objective to Near-Pareto Multi-Objective Optimization
• [cs.NI]Deep Learning Aided Routing for Space-Air-Ground Integrated Networks Relying on Real Satellite, Flight, and Shipping Data
• [cs.NI]Deep Reinforcement Learning Aided Packet-Routing For Aeronautical Ad-Hoc Networks Formed by Passenger Planes
• [cs.RO]A Novel Sample-efficient Deep Reinforcement Learning with Episodic Policy Transfer for PID-Based Control in Cardiac Catheterization Robots
• [cs.RO]An Adaptable Approach to Learn Realistic Legged Locomotion without Examples
• [cs.RO]An Autonomous Probing System for Collecting Measurements at Depth from Small Surface Vehicles
• [cs.RO]An Improved Positioning Accuracy Method of a Robot Based on Particle Filter
• [cs.RO]Efficient Placard Discovery for Semantic Mapping During Frontier Exploration
• [cs.RO]From Machine Learning to Robotics: Challenges and Opportunities for Embodied Intelligence
• [cs.RO]GOMP-FIT: Grasp-Optimized Motion Planning for Fast Inertial Transport
• [cs.RO]Learning Actions for Drift-Free Navigation in Highly Dynamic Scenes
• [cs.RO]Learning Feasibility to Imitate Demonstrators with Different Dynamics
• [cs.RO]Learning to Jump from Pixels
• [cs.RO]Millimeter Wave Wireless-Assisted Robotic Navigation with Link State Classification
• [cs.RO]Modeling, simulation, and optimization of a monopod hopping on yielding terrain
• [cs.RO]Multimotion Visual Odometry (MVO)
• [cs.RO]Orientation Probabilistic Movement Primitives on Riemannian Manifolds
• [cs.RO]Sensing Anomalies as Potential Hazards: Datasets and Benchmarks
• [cs.RO]Similarity-Aware Skill Reproduction based on Multi-Representational Learning from Demonstration
• [cs.RO]Spatial Constraint Generation for Motion Planning in Dynamic Environments
• [cs.SD]Neural Analysis and Synthesis: Reconstructing Speech from Self-Supervised Representations
• [cs.SI]CIIA:A New Algorithm for Community Detection
• [cs.SI]Finding a Concise, Precise, and Exhaustive Set of Near Bi-Cliques in Dynamic Graphs
• [cs.SI]Graph Communal Contrastive Learning
• [cs.SI]Streaming Local Community Detection through Approximate Conductance
• [eess.IV]Alternating Learning Approach for Variational Networks and Undersampling Pattern in Parallel MRI Applications
• [eess.IV]Deep Learning Analysis of Cardiac MRI in Legacy Datasets: Multi-Ethnic Study of Atherosclerosis
• [eess.IV]Degraded Reference Image Quality Assessment
• [eess.IV]Diagnosis of COVID-19 Using Machine Learning and Deep Learning: A review
• [eess.IV]Improving Super-Resolution Performance using Meta-Attention Layers
• [eess.IV]Lung Cancer Lesion Detection in Histopathology Images Using Graph-Based Sparse PCA Network
• [eess.IV]SCALP — Supervised Contrastive Learning for Cardiopulmonary Disease Classification and Localization in Chest X-rays using Patient Metadata
• [eess.IV]Sharp-GAN: Sharpness Loss Regularized GAN for Histopathology Image Synthesis
• [eess.SP]Self-supervised EEG Representation Learning for Automatic Sleep Staging
• [eess.SP]V2iFi: in-Vehicle Vital Sign Monitoring via Compact RF Sensing
• [eess.SY]Cooperative Deep $今日学术视野(2021.10.30) - 图1$ -learning Framework for Environments Providing Image Feedback
• [math.CO]Labeled sample compression schemes for complexes of oriented matroids
• [math.DS]Deeptime: a Python library for machine learning dynamical models from time series data
• [math.FA]Sobolev-type embeddings for neural network approximation spaces
• [math.OC]A first-order primal-dual method with adaptivity to local smoothness
• [math.OC]Meta Subspace Optimization
• [math.OC]Spectrahedral Regression
• [math.PR]Convergence of Conditional Entropy for Long Range Dependent Markov Chains
• [math.PR]Stable distributions and domains of attraction for unitarily invariant Hermitian random matrix ensembles
• [math.ST]Algebraic algorithm for direct sampling from toric models
• [math.ST]Approximately low-rank recovery from noisy and local measurements by convex program
• [math.ST]Nearest neighbor process: weak convergence and non-asymptotic bound
• [physics.ao-ph]Using Non-Linear Causal Models to Study Aerosol-Cloud Interactions in the Southeast Pacific
• [physics.chem-ph]Generalizability of density functionals learned from differentiable programming on weakly correlated spin-polarized systems
• [physics.flu-dyn]Stabilising viscous extensional flows using Reinforcement Learning
• [q-bio.GN]MutFormer: A context-dependent transformer-based model to predict pathogenic missense mutations
• [q-bio.NC]Counterfactual Explanation of Brain Activity Classifiers using Image-to-Image Transfer by Generative Adversarial Network
• [q-bio.PE]Thermodynamics of Evolution and the Origin of Life
• [q-bio.QM]Generating 3D Molecules Conditional on Receptor Binding Sites with Deep Generative Models
• [q-fin.ST]Deep Calibration of Interest Rates Model
• [q-fin.TR]Trading via Selective Classification
• [quant-ph]Subtleties in the trainability of quantum machine learning models
• [quant-ph]Ultimate Limits of Quantum Channel Discrimination
• [stat.AP]A spatio-temporal analysis of NO $今日学术视野(2021.10.30) - 图2$ concentrations during the Italian 2020 COVID-19 lockdown
• [stat.ME]Efficient Algorithms and Implementation of a Semiparametric Joint Model for Longitudinal and Competing Risks Data: With Applications to Massive Biobank Data
• [stat.ME]Living on the Edge: An Unified Approach to Antithetic Sampling
• [stat.ME]Location-Adaptive Change-Point Testing for Time Series
• [stat.ME]Robust model-based estimation for binary outcomes in genomics studies
• [stat.ME]The Balancing Act in Causal Inference
• [stat.ME]Warped Dynamic Linear Models for Time Series of Counts
• [stat.ML]An $今日学术视野(2021.10.30) - 图3$ -based Kernel Conditional Independence Test
• [stat.ML]Convolutional Deep Exponential Families
• [stat.ML]Generalized Shape Metrics on Neural Representations
• [stat.ML]MMD Aggregated Two-Sample Test
• [stat.ML]Minimax Optimal Quantile and Semi-Adversarial Regret via Root-Logarithmic Regularizers
• [stat.ML]Probabilistic Autoencoder using Fisher Information
• [stat.ML]VACA: Design of Variational Graph Autoencoders for Interventional and Counterfactual Queries
• [stat.ML]Vector-valued Gaussian Processes on Riemannian Manifolds via Gauge Equivariant Projected Kernels
• [stat.OT]On rereading Savage

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• [astro-ph.EP]Exoplanet atmosphere evolution: emulation with random forests
James G. Rogers, Clàudia Janó Muñoz, James E. Owen, Richard A. Booth
http://arxiv.org/abs/2110.15162v1

• [cond-mat.soft]Designing Machine Learning Surrogates using Outputs of Molecular Dynamics Simulations as Soft Labels
J. C. S. Kadupitiya, Nasim Anousheh, Vikram Jadhao
http://arxiv.org/abs/2110.14714v1

• [cs.AI]A Scenario-Based Platform for Testing Autonomous Vehicle Behavior Prediction Models in Simulation
Francis Indaheng, Edward Kim, Kesav Viswanadha, Jay Shenoy, Jinkyu Kim, Daniel J. Fremont, Sanjit A. Seshia
http://arxiv.org/abs/2110.14870v1

• [cs.AI]Conditional Inference and Activation of Knowledge Entities in ACT-R
Marco Wilhelm, Diana Howey, Gabriele Kern-Isberner, Kai Sauerwald, Christoph Beierle
http://arxiv.org/abs/2110.15214v1

• [cs.AI]End-to-End Speech Emotion Recognition: Challenges of Real-Life Emergency Call Centers Data Recordings
Théo Deschamps-Berger, Lori Lamel, Laurence Devillers
http://arxiv.org/abs/2110.14957v1

• [cs.AI]NeuroComb: Improving SAT Solving with Graph Neural Networks
Wenxi Wang, Yang Hu, Mohit Tiwari, Sarfraz Khurshid, Kenneth McMillan, Risto Miikkulainen
http://arxiv.org/abs/2110.14053v2

• [cs.AR]SIMCNN — Exploiting Computational Similarity to Accelerate CNN Training in Hardware
Vahid Janfaza, Kevin Weston, Moein Razavi, Shantanu Mandal, Abdullah Muzahid
http://arxiv.org/abs/2110.14904v1

• [cs.CL]A Sequence to Sequence Model for Extracting Multiple Product Name Entities from Dialog
Praneeth Gubbala, Xuan Zhang
http://arxiv.org/abs/2110.14843v1

• [cs.CL]Anomaly-Injected Deep Support Vector Data Description for Text Outlier Detection
Zeyu You, Yichu Zhou, Tao Yang, Wei Fan
http://arxiv.org/abs/2110.14729v1

• [cs.CL]BERTian Poetics: Constrained Composition with Masked LMs
Christopher Akiki, Martin Potthast
http://arxiv.org/abs/2110.15181v1

• [cs.CL]Bridge the Gap Between CV and NLP! A Gradient-based Textual Adversarial Attack Framework
Lifan Yuan, Yichi Zhang, Yangyi Chen, Wei Wei
http://arxiv.org/abs/2110.15317v1

• [cs.CL]Cognitive network science quantifies feelings expressed in suicide letters and Reddit mental health communities
Simmi Marina Joseph, Salvatore Citraro, Virginia Morini, Giulio Rossetti, Massimo Stella
http://arxiv.org/abs/2110.15269v1

• [cs.CL]Combining Vagueness Detection with Deep Learning to Identify Fake News
Paul Guélorget, Benjamin Icard, Guillaume Gadek, Souhir Ghabiche, Sylvain Gatepaille, Ghislain Atemezing, Paul Égré
http://arxiv.org/abs/2110.14780v1

• [cs.CL]Confounds and Overestimations in Fake Review Detection: Experimentally Controlling for Product-Ownership and Data-Origin
Felix Soldner, Bennett Kleinberg, Shane Johnson
http://arxiv.org/abs/2110.15130v1

• [cs.CL]Detecting Dementia from Speech and Transcripts using Transformers
Loukas Ilias, Dimitris Askounis, John Psarras
http://arxiv.org/abs/2110.14769v1

• [cs.CL]Diversity-Driven Combination for Grammatical Error Correction
Wenjuan Han, Hwee Tou Ng
http://arxiv.org/abs/2110.15149v1

• [cs.CL]Emoji-aware Co-attention Network with EmoGraph2vec Model for Sentiment Anaylsis
Xiaowei Yuan, Jingyuan Hu, Xiaodan Zhang, Honglei Lv, Hao Liu
http://arxiv.org/abs/
a64
2110.14636v1
a64
2110.14636v1)

• [cs.CL]Empirical Analysis of Korean Public AI Hub Parallel Corpora and in-depth Analysis using LIWC
Chanjun Park, Midan Shim, Sugyeong Eo, Seolhwa Lee, Jaehyung Seo, Hyeonseok Moon, Heuiseok Lim
http://arxiv.org/abs/2110.15023v1

• [cs.CL]Hate Speech Classifiers Learn Human-Like Social Stereotypes
Aida Mostafazadeh Davani, Mohammad Atari, Brendan Kennedy, Morteza Dehghani
http://arxiv.org/abs/2110.14839v1

• [cs.CL]Multi-stage Clarification in Conversational AI: The case of Question-Answering Dialogue Systems
Hadrien Lautraite, Nada Naji, Louis Marceau, Marc Queudot, Eric Charton
http://arxiv.org/abs/2110.15235v1

• [cs.CL]**Pruning Attention Heads of Transformer Models Using A Search: A Novel Approach to Compress Big NLP Architectures
Archit Parnami, Rahul Singh, Tarun Joshi
http://arxiv.org/abs/2110.15225v1

• [cs.CL]Semi-Siamese Bi-encoder Neural Ranking Model Using Lightweight Fine-Tuning
Euna Jung, Jaekeol Choi, Wonjong Rhee
http://arxiv.org/abs/2110.14943v1

• [cs.CL]Towards Realistic Single-Task Continuous Learning Research for NER
Justin Payan, Yuval Merhav, He Xie, Satyapriya Krishna, Anil Ramakrishna, Mukund Sridhar, Rahul Gupta
http://arxiv.org/abs/2110.14694v1

• [cs.CL]When is BERT Multilingual? Isolating Crucial Ingredients for Cross-lingual Transfer
Ameet Deshpande, Partha Talukdar, Karthik Narasimhan
http://arxiv.org/abs/2110.14782v1

• [cs.CL]ÚFAL at MultiLexNorm 2021: Improving Multilingual Lexical Normalization by Fine-tuning ByT5
David Samuel, Milan Straka
http://arxiv.org/abs/2110.15248v1

• [cs.CR]Authentication Attacks on Projection-based Cancelable Biometric Schemes
Axel Durbet, Pascal Lafourcade, Denis Migdal, Kevin Thiry-Atighehchi, Paul-Marie Grollemund
http://arxiv.org/abs/2110.15163v1

• [cs.CR]Four-dimensional hybrid chaos system and its application in creating a secure image transfer environment by cellular automata
R. Parvaz, Y. Khedmati, Y. Behroo
http://arxiv.org/abs/2110.15196v1

• [cs.CR]Masked LARk: Masked Learning, Aggregation and Reporting worKflow
Joseph J. Pfeiffer III, Denis Charles, Davis Gilton, Young Hun Jung, Mehul Parsana, Erik Anderson
http://arxiv.org/abs/2110.14794v1

• [cs.CV]3D Object Tracking with Transformer
Yubo Cui, Zheng Fang, Jiayao Shan, Zuoxu Gu, Sifan Zhou
http://arxiv.org/abs/2110.14921v1

• [cs.CV]A Comparative Study of Coarse to Dense 3D Indoor Scene Registration Algorithms
Abdenour Amamra, Khalid Boumaza
http://arxiv.org/abs/2110.15179v1

• [cs.CV]A Geometric Perspective towards Neural Calibration via Sensitivity Decomposition
Junjiao Tian, Dylan Yung, Yen-Chang Hsu, Zsolt Kira
http://arxiv.org/abs/2110.14577v2

• [cs.CV]A Survey of Self-Supervised and Few-Shot Object Detection
Gabriel Huang, Issam Laradji, David Vazquez, Simon Lacoste-Julien, Pau Rodriguez
http://arxiv.org/abs/2110.14711v1

• [cs.CV]A recursive robust filtering approach for 3D registration
Abdenour Amamra, Nabil Aouf, Dowling Stuart, Mark Richardson
http://arxiv.org/abs/2110.14932v1

• [cs.CV]Audio-visual Representation Learning for Anomaly Events Detection in Crowds
Junyu Gao, Maoguo Gong, Xuelong Li
http://arxiv.org/abs/2110.14862v1

• [cs.CV]BI-GCN: Boundary-Aware Input-Dependent Graph Convolution Network for Biomedical Image Segmentation
Yanda Meng, Hongrun Zhang, Dongxu Gao, Yitian Zhao, Xiaoyun Yang, Xuesheng Qian, Xiaowei Huang, Yalin Zheng
http://arxiv.org/abs/2110.14775v1

• [cs.CV]Blending Anti-Aliasing into Vision Transformer
Shengju Qian, Hao Shao, Yi Zhu, Mu Li, Jiaya Jia
http://arxiv.org/abs/2110.15156v1

• [cs.CV]Bridging Non Co-occurrence with Unlabeled In-the-wild Data for Incremental Object Detection
Na Dong, Yongqiang Zhang, Mingli Ding, Gim Hee Lee
http://arxiv.org/abs/2110.15017v1

• [cs.CV]Characterizing and Taming Resolution in Convolutional Neural Networks
Eddie Yan, Liang Luo, Luis Ceze
http://arxiv.org/abs/2110.14819v1

• [cs.CV]Contrast and Mix: Temporal Contrastive Video Domain Adaptation with Background Mixing
Aadarsh Sahoo, Rutav Shah, Rameswar Panda, Kate Saenko, Abir Das
http://arxiv.org/abs/2110.15128v1

• [cs.CV]Deformable Registration of Brain MR Images via a Hybrid Loss
Luyi Han, Haoran Dou, Yunzhi Huang, Pew-Thian Yap
http://arxiv.org/abs/2110.15027v1

• [cs.CV]Dispensed Transformer Network for Unsupervised Domain Adaptation
Yunxiang Li, Jingxiong Li, Ruilong Dan, Shuai Wang, Kai Jin, Guodong Zeng, Jun Wang, Xiangji Pan, Qianni Zhang, Huiyu Zhou, Qun Jin, Li Wang, Yaqi Wang
http://arxiv.org/abs/2110.14944v1

• [cs.CV]DocScanner: Robust Document Image Rectification with Progressive Learning
Hao Feng, Wengang Zhou, Jiajun Deng, Qi Tian, Houqiang Li
http://arxiv.org/abs/2110.14968v1

• [cs.CV]Dynamic Visual Reasoning by Learning Differentiable Physics Models from Video and Language
Mingyu Ding, Zhenfang Chen, Tao Du, Ping Luo, Joshua B. Tenenbaum, Chuang Gan
http://arxiv.org/abs/2110.15358v1

• [cs.CV]End-to-end Learning the Partial Permutation Matrix for Robust 3D Point Cloud Registration
Zhiyuan Zhang, Jiadai Sun, Yuchao Dai, Dingfu Zhou, Xibin Song, Mingyi He
http://arxiv.org/abs/2110.15250v1

• [cs.CV]Explicitly Modeling the Discriminability for Instance-Aware Visual Object Tracking
Mengmeng Wang, Xiaoqian Yang, Yong Liu
http://arxiv.org/abs/2110.15030v1

• [cs.CV]Facial Emotion Recognition: A multi-task approach using deep learning
Aakash Saroop, Pathik Ghugare, Sashank Mathamsetty, Vaibhav Vasani
http://arxiv.org/abs/2110.15028v1

• [cs.CV]FocusFace: Multi-task Contrastive Learning for Masked Face Recognition
Pedro C. Neto, Fadi Boutros, João Ribeiro Pinto, Naser Damer, Ana F. Sequeira, Jaime S. Cardoso
http://arxiv.org/abs/2110.14940v1

• [cs.CV]GPU based GMM segmentation of kinect data
Abdenour Amamra, Tarek Mouats, Nabil Aouf
http://arxiv.org/abs/2110.14934v1

• [cs.CV]Image Comes Dancing with Collaborative Parsing-Flow Video Synthesis
Bowen Wu, Zhenyu Xie, Xiaodan Liang, Yubei Xiao, Haoye Dong, Liang Lin
http://arxiv.org/abs/2110.14147v2

• [cs.CV]Intermediate Layers Matter in Momentum Contrastive Self Supervised Learning
Aakash Kaku, Sahana Upadhya, Narges Razavian
http://arxiv.org/abs/2110.14805v1

• [cs.CV]LF-YOLO: A Lighter and Faster YOLO for Weld Defect Detection of X-ray Image
Moyun Liu, Youping Chen, Lei He, Yang Zhang, Jingming Xie
http://arxiv.org/abs/2110.15045v1

• [cs.CV]MCUNetV2: Memory-Efficient Patch-based Inference for Tiny Deep Learning
Ji Lin, Wei-Ming Chen, Han Cai, Chuang Gan, Song Han
http://arxiv.org/abs/2110.15352v1

• [cs.CV]MEGAN: Memory Enhanced Graph Attention Network for Space-Time Video Super-Resolution
Chenyu You, Lianyi Han, Aosong Feng, Ruihan Zhao, Hui Tang, Wei Fan
http://arxiv.org/abs/2110.15327v1

• [cs.CV]MedMNIST v2: A Large-Scale Lightweight Benchmark for 2D and 3D Biomedical Image Classification
Jiancheng Yang, Rui Shi, Donglai Wei, Zequan Liu, Lin Zhao, Bilian Ke, Hanspeter Pfister, Bingbing Ni
http://arxiv.org/abs/2110.14795v1

• [cs.CV]Meta Guided Metric Learner for Overcoming Class Confusion in Few-Shot Road Object Detection
Anay Majee, Anbumani Subramanian, Kshitij Agrawal
http://arxiv.org/abs/2110.15074v1

• [cs.CV]ODMTCNet: An Interpretable Multi-view Deep Neural Network Architecture for Image Feature Representation
Lei Gao, Zheng Guo, Ling Guan
http://arxiv.org/abs/2110.14830v1

• [cs.CV]Privacy Aware Person Detection in Surveillance Data
Sander De Coninck, Sam Leroux, Pieter Simoens
http://arxiv.org/abs/2110.15171v1

• [cs.CV]Self-Supervised Learning Disentangled Group Representation as Feature
Tan Wang, Zhongqi Yue, Jianqiang Huang, Qianru Sun, Hanwang Zhang
http://arxiv.org/abs/2110.15255v1

• [cs.CV]SiamPolar: Semi-supervised Realtime Video Object Segmentation with Polar Representation
Yaochen Li, Yuhui Hong, Yonghong Song, Chao Zhu, Ying Zhang, Ruihao Wang
http://arxiv.org/abs/2110.14773v1

• [cs.CV]Skeleton-Based Mutually Assisted Interacted Object Localization and Human Action Recognition
Liang Xu, Cuiling Lan, Wenjun Zeng, Cewu Lu
http://arxiv.org/abs/2110.14994v1

• [cs.CV]Sliding Sequential CVAE with Time Variant Socially-aware Rethinking for Trajectory Prediction
Hao Zhou, Dongchun Ren, Xu Yang, Mingyu Fan, Hai Huang
http://arxiv.org/abs/2110.15016v1

• [cs.CV]SpineOne: A One-Stage Detection Framework for Degenerative Discs and Vertebrae
Jiabo He, Wei Liu, Yu Wang, Xingjun Ma, Xian-Sheng Hua
http://arxiv.org/abs/2110.15082v1

• [cs.CV]Subpixel object segmentation using wavelets and multi resolution analysis
Ray Sheombarsing, Nikita Moriakov, Jan-Jakob Sonke, Jonas Teuwen
http://arxiv.org/abs/2110.15233v1

• [cs.CV]Temporal-attentive Covariance Pooling Networks for Video Recognition
Zilin Gao, Qilong Wang, Bingbing Zhang, Qinghua Hu, Peihua Li
http://arxiv.org/abs/2110.14381v2

• [cs.CV]Towards Large-Scale Rendering of Simulated Crops for Synthetic Ground Truth Generation on Modular Supercomputers
Dirk Norbert Helmrich, Jens Henrik Göbbert, Mona Giraud, Hanno Scharr, Andrea Schnepf, Morris Riedel
http://arxiv.org/abs/2110.14946v1

• [cs.CV]UltraPose: Synthesizing Dense Pose with 1 Billion Points by Human-body Decoupling 3D Model
Haonan Yan, Jiaqi Chen, Xujie Zhang, Shengkai Zhang, Nianhong Jiao, Xiaodan Liang, Tianxiang Zheng
http://arxiv.org/abs/2110.15267v1

• [cs.CV]Vision Transformer for Classification of Breast Ultrasound Images
Behnaz Gheflati, Hassan Rivaz
http://arxiv.org/abs/2110.14731v1

• [cs.CV]XDEEP-MSI: Explainable Bias-Rejecting Microsatellite Instability Deep Learning System In Colorectal Cancer
Aurelia Bustos, Artemio Payá, Andres Torrubia, Rodrigo Jover, Xavier Llor, Xavier Bessa, Antoni Castells, Cristina Alenda
http://arxiv.org/abs/2110.15350v1

• [cs.CY]On the Fairness of Machine-Assisted Human Decisions
Talia Gillis, Bryce McLaughlin, Jann Spiess
http://arxiv.org/abs/2110.15310v1

• [cs.DC]Bolt: Bridging the Gap between Auto-tuners and Hardware-native Performance
Jiarong Xing, Leyuan Wang, Shang Zhang, Jack Chen, Ang Chen, Yibo Zhu
http://arxiv.org/abs/2110.15238v1

• [cs.DC]NetDAM: Network Direct Attached Memory with Programmable In-Memory Computing ISA
Kevin Fang, David Peng
http://arxiv.org/abs/2110.14902v1

• [cs.DC]OneFlow: Redesign the Distributed Deep Learning Framework from Scratch
Jinhui Yuan, Xinqi Li, Cheng Cheng, Juncheng Liu, Ran Guo, Shenghang Cai, Chi Yao, Fei Yang, Xiaodong Yi, Chuan Wu, Haoran Zhang, Jie Zhao
http://arxiv.org/abs/2110.15032v1

• [cs.DC]Pipeline Parallelism for Inference on Heterogeneous Edge Computing
Yang Hu, Connor Imes, Xuanang Zhao, Souvik Kundu, Peter A. Beerel, Stephen P. Crago, John Paul N. Walters
http://arxiv.org/abs/2110.14895v1

• [cs.DC]Xar-Trek: Run-time Execution Migration among FPGAs and Heterogeneous-ISA CPUs
Edson Horta, Ho-Ren Chuang, Naarayanan Rao VSathish, Cesar Philippidis, Antonio Barbalace, Pierre Olivier, Binoy Ravindran
http://arxiv.org/abs/2110.14751v1

• [cs.DS]Better Sum Estimation via Weighted Sampling
Lorenzo Beretta, Jakub Tětek
http://arxiv.org/abs/2110.14948v1

• [cs.HC]An Analysis of Programming Course Evaluations Before and After the Introduction of an Autograder
Gerhard Hagerer, Laura Lahesoo, Miriam Anschütz, Stephan Krusche, Georg Groh
http://arxiv.org/abs/2110.15134v1

• [cs.HC]E-ffective: A Visual Analytic System for Exploring the Emotion and Effectiveness of Inspirational Speeches
Kevin Maher, Zeyuan Huang, Jiancheng Song, Xiaoming Deng, Yu-Kun Lai, Cuixia Ma, Hao Wang, Yong-Jin Liu, Hongan Wang
http://arxiv.org/abs/2110.14908v1

• [cs.HC]Telling Creative Stories Using Generative Visual Aids
Safinah Ali, Devi Parikh
http://arxiv.org/abs/2110.14810v1

• [cs.IR]An AI-based Approach for Tracing Content Requirements in Financial Documents
Xiaochen Li, Domenico Bianculli, Lionel C. Briand
http://arxiv.org/abs/2110.14960v1

• [cs.IR]Cross-Batch Negative Sampling for Training Two-Tower Recommenders
Jinpeng Wang, Jieming Zhu, Xiuqiang He
http://arxiv.org/abs/2110.15154v1

• [cs.IR]D2RLIR : an improved and diversified ranking function in interactive recommendation systems based on deep reinforcement learning
Vahid Baghi, Seyed Mohammad Seyed Motehayeri, Ali Moeini, Rooholah Abedian
http://arxiv.org/abs/2110.15089v1

• [cs.IR]Dynamic Review-based Recommenders
Kostadin Cvejoski, Ramses J. Sanchez, Christian Bauckhage, Cesar Ojeda
http://arxiv.org/abs/2110.14747v1

• [cs.IR]From Intrinsic to Counterfactual: On the Explainability of Contextualized Recommender Systems
Yao Zhou, Haonan Wang, Jingrui He, Haixun Wang
http://arxiv.org/abs/2110.14844v1

• [cs.IR]Hierarchical User Intent Graph Network forMultimedia Recommendation
Wei Yinwei, Wang Xiang, He Xiangnan, Nie Liqiang, Rui Yong, Chua Tat-Seng
http://arxiv.org/abs/2110.14925v1

• [cs.IR]UltraGCN: Ultra Simplification of Graph Convolutional Networks for Recommendation
Kelong Mao, Jieming Zhu, Xi Xiao, Biao Lu, Zhaowei Wang, Xiuqiang He
http://arxiv.org/abs/2110.15114v1

• [cs.IT]Color image restoration with impulse noise based on fractional-order total variation and framelet
Reza Parvaz
http://arxiv.org/abs/2110.15170v1

• [cs.IT]Context-Tree-Based Lossy Compression and Its Application to CSI Representation
Henrique K. Miyamoto, Sheng Yang
http://arxiv.org/abs/2110.14748v1

• [cs.IT]DeepNP: Deep Learning-Based Noise Prediction for Ultra-Reliable Low-Latency Communications
Alejandro Cohen, Amit Solomon, Nir Shlezinger
http://arxiv.org/abs/2110.15328v1

• [cs.IT]Distributed Joint Multi-cell Optimization of IRS Parameters with Linear Precoders
Reinhard Wiesmayr, Michael Honig, Michael Joham, Wolfgang Utschick
http://arxiv.org/abs/2110.14906v1

• [cs.IT]Exact Analytical Model of Age of Information in Multi-source Status Update Systems with Per-source Queueing
Ege Orkun Gamgam, Nail Akar
http://arxiv.org/abs/2110.15024v1

• [cs.IT]Feature Learning for Neural-Network-Based Positioning with Channel State Information
Emre Gönültaş, Sueda Taner, Howard Huang, Christoph Studer
http://arxiv.org/abs/2110.15160v1

• [cs.IT]Identification over Compound MIMO Broadcast Channels
Johannes Rosenberger, Uzi Pereg, Christian Deppe
http://arxiv.org/abs/2110.15101v1

• [cs.IT]Multi-Pair Two-Way Massive MIMO DF Relaying Over Rician Fading Channels Under Imperfect CSI
Zhangjie Peng, Shuxian Wang, Cunhua Pan, Xianzhe Chen, Julian Cheng, Lajos Hanzo
http://arxiv.org/abs/2110.15242v1

• [cs.IT]NOMA Joint Decoding based on Soft-Output Ordered-Statistics Decoder for Short Block Codes
Chentao Yue, Alva Kosasih, Mahyar Shirvanimoghaddam, Giyoon Park, Ok-Sun Park, Wibowo Hardjawana, Branka Vucetic, Yonghui Li
http://arxiv.org/abs/2110.15010v1

• [cs.IT]Pilot Optimization and Channel Estimation for Two-way Relaying Network Aided by IRS with Finite Discrete Phase Shifters
Zhongwen Sun, Xuehui Wang, Siling Feng, Xinrong Guan, Feng Shu, Jiangzhou Wang
http://arxiv.org/abs/2110.14879v1

• [cs.LG]A Game-Theoretic Approach for Improving Generalization Ability of TSP Solvers
Chenguang Wang, Yaodong Yang, Oliver Slumbers, Congying Han, Tiande Guo, Haifeng Zhang, Jun Wang
http://arxiv.org/abs/2110.15105v1

• [cs.LG]AEVA: Black-box Backdoor Detection Using Adversarial Extreme Value Analysis
Junfeng Guo, Ang Li, Cong Liu
http://arxiv.org/abs/2110.14880v1

• [cs.LG]Accelerating Robotic Reinforcement Learning via Parameterized Action Primitives
Murtaza Dalal, Deepak Pathak, Ruslan Salakhutdinov
http://arxiv.org/abs/2110.15360v1

• [cs.LG]Active-LATHE: An Active Learning Algorithm for Boosting the Error Exponent for Learning Homogeneous Ising Trees
Fengzhuo Zhang, Anshoo Tandon, Vincent Y. F. Tan
http://arxiv.org/abs/2110.14341v2

• [cs.LG]Aggregation as Unsupervised Learning and its Evaluation
Maria Ulan, Welf Löwe, Morgan Ericsson, Anna Wingkvist
http://arxiv.org/abs/org/abs/2110.15136v1

• [cs.LG]Algorithmic encoding of protected characteristics and its implications on disparities across subgroups
Ben Glocker, Stefan Winzeck
http://arxiv.org/abs/2110.14755v1

• [cs.LG]An Operator Theoretic Perspective on Pruning Deep Neural Networks
William T. Redman, Maria Fonoberova, Ryan Mohr, Ioannis G. Kevrekidis, Igor Mezic
http://arxiv.org/abs/2110.14856v1

• [cs.LG]Approximate Decomposable Submodular Function Minimization for Cardinality-Based Components
Nate Veldt, Austin R. Benson, Jon Kleinberg
http://arxiv.org/abs/2110.14859v1

• [cs.LG]Bayesian Sequential Optimal Experimental Design for Nonlinear Models Using Policy Gradient Reinforcement Learning
Wanggang Shen, Xun Huan
http://arxiv.org/abs/2110.15335v1

• [cs.LG]Brain-inspired feature exaggeration in generative replay for continual learning
Jack Millichamp, Xi Chen
http://arxiv.org/abs/2110.15056v1

• [cs.LG]CAFE: Catastrophic Data Leakage in Vertical Federated Learning
Xiao Jin, Pin-Yu Chen, Chia-Yi Hsu, Chia-Mu Yu, Tianyi Chen
http://arxiv.org/abs/2110.15122v1

• [cs.LG]CAP: Co-Adversarial Perturbation on Weights and Features for Improving Generalization of Graph Neural Networks
Haotian Xue, Kaixiong Zhou, Tianlong Chen, Kai Guo, Xia Hu, Yi Chang, Xin Wang
http://arxiv.org/abs/2110.14855v1

• [cs.LG]Choosing the Best of Both Worlds: Diverse and Novel Recommendations through Multi-Objective Reinforcement Learning
Dusan Stamenkovic, Alexandros Karatzoglou, Ioannis Arapakis, Xin Xin, Kleomenis Katevas
http://arxiv.org/abs/2110.15097v1

• [cs.LG]Class-wise Thresholding for Detecting Out-of-Distribution Data
Matteo Guarrera, Baihong Jin, Tung-Wei Lin, Maria Zuluaga, Yuxin Chen, Alberto Sangiovanni-Vincentelli
http://arxiv.org/abs/2110.15292v1

• [cs.LG]Colossal-AI: A Unified Deep Learning System For Large-Scale Parallel Training
Zhengda Bian, Hongxin Liu, Boxiang Wang, Haichen Huang, Yongbin Li, Chuanrui Wang, Fan Cui, Yang You
http://arxiv.org/abs/2110.14883v1

• [cs.LG]Communication-Efficient ADMM-based Federated Learning
Shenglong Zhou, Geoffrey Ye Li
http://arxiv.org/abs/2110.15318v1

• [cs.LG]Computational Intelligence and Deep Learning for Next-Generation Edge-Enabled Industrial IoT
Shunpu Tang, Lunyuan Chen, Ke HeJunjuan Xia, Lisheng Fan, Arumugam Nallanathan
http://arxiv.org/abs/2110.14937v1

• [cs.LG]Conditioning Sparse Variational Gaussian Processes for Online Decision-making
Wesley J. Maddox, Samuel Stanton, Andrew Gordon Wilson
http://arxiv.org/abs/2110.15172v1

• [cs.LG]Confidence-Aware Imitation Learning from Demonstrations with Varying Optimality
Songyuan Zhang, Zhangjie Cao, Dorsa Sadigh, Yanan Sui
http://arxiv.org/abs/2110.14754v1

• [cs.LG]Coresets for Time Series Clustering
Lingxiao Huang, K. Sudhir, Nisheeth K. Vishnoi
http://arxiv.org/abs/2110.15263v1

• [cs.LG]Differentiable NAS Framework and Application to Ads CTR Prediction
Ravi Krishna, Aravind Kalaiah, Bichen Wu, Maxim Naumov, Dheevatsa Mudigere, Misha Smelyanskiy, Kurt Keutzer
http://arxiv.org/abs/2110.14812v1

• [cs.LG]Dist2Cycle: A Simplicial Neural Network for Homology Localization
Alexandros Dimitrios Keros, Vidit Nanda, Kartic Subr
http://arxiv.org/abs/2110.15182v1

• [cs.LG]Explaining Latent Representations with a Corpus of Examples
Jonathan Crabbé, Zhaozhi Qian, Fergus Imrie, Mihaela van der Schaar
http://arxiv.org/abs/2110.15355v1

• [cs.LG]Exploration of Algorithmic Trading Strategies for the Bitcoin Market
Nathan Crone, Eoin Brophy, Tomas Ward
http://arxiv.org/abs/2110.14936v1

• [cs.LG]Exploring Covariate and Concept Shift for Detection and Calibration of Out-of-Distribution Data
Junjiao Tian, Yen-Change Hsu, Yilin Shen, Hongxia Jin, Zsolt Kira
http://arxiv.org/abs/2110.15231v1

• [cs.LG]Extracting Clinician’s Goals by What-if Interpretable Modeling
Chun-Hao Chang, George Alexandru Adam, Rich Caruana, Anna Goldenberg
http://arxiv.org/abs/2110.15165v1

• [cs.LG]FeO2: Federated Learning with Opt-Out Differential Privacy
Nasser Aldaghri, Hessam Mahdavifar, Ahmad Beirami
http://arxiv.org/abs/2110.15252v1

• [cs.LG]Fighting the curse of dimensionality: A machine learning approach to finding global optima
Julian F. Schumann, Alejandro M. Aragón
http://arxiv.org/abs/2110.14985v1

• [cs.LG]Finite Horizon Q-learning: Stability, Convergence and Simulations
Vivek VP, Dr. Shalabh Bhatnagar
http://arxiv.org/abs/2110.15093v1

• [cs.LG]Generalized Anomaly Detection
Suresh Singh, Minwei Luo, Yu Li
http://arxiv.org/abs/2110.15108v1

• [cs.LG]Generalized Depthwise-Separable Convolutions for Adversarially Robust and Efficient Neural Networks
Hassan Dbouk, Naresh R. Shanbhag
http://arxiv.org/abs/2110.14871v1

• [cs.LG]Generating Table Vector Representations
Aneta Koleva, Martin Ringsquandl, Mitchell Joblin, Volker Tresp
http://arxiv.org/abs/2110.15132v1

• [cs.LG]Gradient Inversion with Generative Image Prior
Jinwoo Jeon, Jaechang Kim, Kangwook Lee, Sewoong Oh, Jungseul Ok
http://arxiv.org/abs/2110.14962v1

• [cs.LG]Guided Evolution for Neural Architecture Search
Vasco Lopes, Miguel Santos, Bruno Degardin, Luís A. Alexandre
http://arxiv.org/abs/2110.15232v1

• [cs.LG]Hindsight Goal Ranking on Replay Buffer for Sparse Reward Environment
Tung M. Luu, Chang D. Yoo
http://arxiv.org/abs/2110.15043v1

• [cs.LG]How to boost autoencoders?
Sai Krishna, Thulasi Tholeti, Sheetal Kalyani
http://arxiv.org/abs/2110.15307v1

• [cs.LG]Identifiable Generative Models for Missing Not at Random Data Imputation
Chao Ma, Cheng Zhang
http://arxiv.org/abs/2110.14708v1

• [cs.LG]Improving Causal Effect Estimation of Weighted RegressionBased Estimator using Neural Networks
Plabon Shaha, Talha Islam Zadid, Ismat Rahman, Md. Mosaddek Khan
http://arxiv.org/abs/2110.15075v1

• [cs.LG]L2ight: Enabling On
aef
-Chip Learning for Optical Neural Networks via Efficient in-situ Subspace Optimization
Jiaqi Gu, Hanqing Zhu, Chenghao Feng, Zixuan Jiang, Ray T. Chen, David Z. Pan
http://arxiv.org/abs/2110.14807v1

• [cs.LG]Learning Aggregations of Binary Activated Neural Networks with Probabilities over Representations
Louis Fortier-Dubois, Gaël Letarte, Benjamin Leblanc, François Laviolette, Pascal Germain
http://arxiv.org/abs/2110.15137v1

• [cs.LG]Learning Deep Representation with Energy-Based Self-Expressiveness for Subspace Clustering
Yanming Li, Changsheng Li, Shiye Wang, Ye Yuan, Guoren Wang
http://arxiv.org/abs/2110.15037v1

• [cs.LG]Learning Domain Invariant Representations in Goal-conditioned Block MDPs
Beining Han, Chongyi Zheng, Harris Chan, Keiran Paster, Michael R. Zhang, Jimmy Ba
http://arxiv.org/abs/2110.14248v2

• [cs.LG]Learning to Control using Image Feedback
Krishnan Raghavan, Vignesh Narayanan, Jagannathan Saraangapani
http://arxiv.org/abs/2110.15290v1

• [cs.LG]Learning to Ground Multi-Agent Communication with Autoencoders
Toru Lin, Minyoung Huh, Chris Stauffer, Ser-Nam Lim, Phillip Isola
http://arxiv.org/abs/2110.15349v1

• [cs.LG]Leveraging Recursive Gumbel-Max Trick for Approximate Inference in Combinatorial Spaces
Kirill Struminsky, Artyom Gadetsky, Denis Rakitin, Danil Karpushkin, Dmitry Vetrov
http://arxiv.org/abs/2110.15072v1

• [cs.LG]Lightweight Mobile Automated Assistant-to-physician for Global Lower-resource Areas
Chao Zhang, Hanxin Zhang, Atif Khan, Ted Kim, Olasubomi Omoleye, Oluwamayomikun Abiona, Amy Lehman, Christopher O. Olopade, Olufunmilayo I. Olopade, Pedro Lopes, Andrey Rzhetsky
http://arxiv.org/abs/2110.15127v1

• [cs.LG]MOOMIN: Deep Molecular Omics Network for Anti-Cancer Drug Combination Therapy
Benedek Rozemberczki, Anna Gogleva, Sebastian Nilsson, Gavin Edwards, Andriy Nikolov, Eliseo Papa
http://arxiv.org/abs/2110.15087v1

• [cs.LG]Meta-Learning Sparse Implicit Neural Representations
Jaeho Lee, Jihoon Tack, Namhoon Lee, Jinwoo Shin
http://arxiv.org/abs/2110.14678v1

• [cs.LG]Modeling Heterogeneous Hierarchies with Relation-specific Hyperbolic Cones
Yushi Bai, Rex Ying, Hongyu Ren, Jure Leskovec
http://arxiv.org/abs/2110.14923v1

• [cs.LG]Mosaicking to Distill: Knowledge Distillation from Out-of-Domain Data
Gongfan Fang, Yifan Bao, Jie Song, Xinchao Wang, Donglin Xie, Chengchao Shen, Mingli Song
http://arxiv.org/abs/2110.15094v1

• [cs.LG]Multi-Task Processes
Donggyun Kim, Seongwoong Cho, Wonkwang Lee, Seunghoon Hong
http://arxiv.org/abs/2110.14953v1

• [cs.LG]Multivariate Empirical Mode Decomposition based Hybrid Model for Day-ahead Peak Load Forecasting
Yanmei Huang, Najmul Hasan, Changrui Deng, Yukun Bao
http://arxiv.org/abs/2110.14980v1

• [cs.LG]Normality-Calibrated Autoencoder for Unsupervised Anomaly Detection on Data Contamination
Jongmin Yu, Hyeontaek Oh, Minkyung Kim, Junsik Kim
http://arxiv.org/abs/2110.14825v1

• [cs.LG]OMASGAN: Out-of-Distribution Minimum Anomaly Score GAN for Sample Generation on the Boundary
Nikolaos Dionelis
http://arxiv.org/abs/2110.15273v1

• [cs.LG]On Provable Benefits of Depth in Training Graph Convolutional Networks
Weilin Cong, Morteza Ramezani, Mehrdad Mahdavi
http://arxiv.org/abs/2110.15174v1

• [cs.LG]On the explainability of hospitalization prediction on a large COVID-19 patient dataset
Ivan Girardi, Panagiotis Vagenas, Dario Arcos-Díaz, Lydia Bessaï, Alexander Büsser, Ludovico Furlan, Raffaello Furlan, Mauro Gatti, Andrea Giovannini, Ellen Hoeven, Chiara Marchiori
http://arxiv.org/abs/2110.15002v1

• [cs.LG]Preventing posterior collapse in variational autoencoders for text generation via decoder regularization
Alban Petit, Caio Corro
http://arxiv.org/abs/2110.14945v1

• [cs.LG]Proximal Reinforcement Learning: Efficient Off-Policy Evaluation in Partially Observed Markov Decision Processes
Andrew Bennett, Nathan Kallus
http://arxiv.org/abs/2110.15332v1

• [cs.LG]RGP: Neural Network Pruning through Its Regular Graph Structure
Zhuangzhi Chen, Jingyang Xiang, Yao Lu, Qi Xuan
http://arxiv.org/abs/2110.15192v1

• [cs.LG]RIM: Reliable Influence-based Active Learning on Graphs
Wentao Zhang, Yexin Wang, Zhenbang You, Meng Cao, Ping Huang, Jiulong Shan, Zhi Yang, Bin Cui
http://arxiv.org/abs/2110.14854v1

• [cs.LG]Rademacher Random Projections with Tensor Networks
Beheshteh T. Rakhshan, Guillaume Rabusseau
http://arxiv.org/abs/2110.13970v2

• [cs.LG]Regularized Frank-Wolfe for Dense CRFs: Generalizing Mean Field and Beyond
Đ. Khuê Lê-Huu, Karteek Alahari
http://arxiv.org/abs/2110.14759v1

• [cs.LG]Reinforcement Learning in Linear MDPs: Constant Regret and Representation Selection
Matteo Papini, Andrea Tirinzoni, Aldo Pacchiano, Marcello Restelli, Alessandro Lazaric, Matteo Pirotta
http://arxiv.org/abs/2110.14798v1

• [cs.LG]Residual Relaxation for Multi-view Representation Learning
Yifei Wang, Zhengyang Geng, Feng Jiang, Chuming Li, Yisen Wang, Jiansheng Yang, Zhouchen Lin
http://arxiv.org/abs/2110.15348v1

• [cs.LG]Roto-translated Local Coordinate Frames For Interacting Dynamical Systems
Miltiadis Kofinas, Naveen Shankar Nagaraja, Efstratios Gavves
http://arxiv.org/abs/2110.14961v1

• [cs.LG]SIM-ECG: A Signal Importance Mask-driven ECGClassification System
Dharma KC, Chicheng Zhang, Chris Gniady, Parth Sandeep Agarwal, Sushil Sharma
http://arxiv.org/abs/2110.14835v1

• [cs.LG]SMORE: Knowledge Graph Completion and Multi-hop Reasoning in Massive Knowledge Graphs
Hongyu Ren, Hanjun Dai, Bo Dai, Xinyun Chen, Denny Zhou, Jure Leskovec, Dale Schuurmans
http://arxiv.org/abs/2110.14890v1

• [cs.LG]Sayer: Using Implicit Feedback to Optimize System Policies
Mathias Lécuyer, Sang Hoon Kim, Mihir Nanavati, Junchen Jiang, Siddhartha Sen, Amit Sharma, Aleksandrs Slivkins
http://arxiv.org/abs/2110.14874v1

• [cs.LG]Scatterbrain: Unifying Sparse and Low-rank Attention Approximation
Beidi Chen, Tri Dao, Eric Winsor, Zhao Song, Atri Rudra, Christopher Ré
http://arxiv.org/abs/2110.15343v1

• [cs.LG]Selective Sampling for Online Best-arm Identification
Romain Camilleri, Zhihan Xiong, Maryam Fazel, Lalit Jain, Kevin Jamieson
http://arxiv.org/abs/2110.14864v1

• [cs.LG]Self-Supervised Representation Learning on Neural Network Weights for Model Characteristic Prediction
Konstantin Schürholt, Dimche Kostadinov, Damian Borth
http://arxiv.org/abs/2110.15288v1

• [cs.LG]TRAIL: Near-Optimal Imitation Learning with Suboptimal Data
Mengjiao Yang, Sergey Levine, Ofir Nachum
http://arxiv.org/abs/2110.14770v1

• [cs.LG]Teaching an Active Learner with Contrastive Examples
Chaoqi Wang, Adish Singla, Yuxin Chen
http://arxiv.org/abs/2110.14888v1

• [cs.LG]Temporal-Difference Value Estimation via Uncertainty-Guided Soft Updates
Litian Liang, Yaosheng Xu, Stephen McAleer, Dailin Hu, Alexander Ihler, Pieter Abbeel, Roy Fox
http://arxiv.org/abs/2110.14818v1

• [cs.LG]The chemical space of terpenes: insights from data science and AI
Morteza Hosseini, David M. Pereira
http://arxiv.org/abs/2110.15047v1

• [cs.LG]The magnitude vector of images
Michael F. Adamer, Leslie O’Bray, Edward De Brouwer, Bastian Rieck, Karsten Borgwardt
http://arxiv.org/abs/2110.15188v1

• [cs.LG]Towards Evaluating the Robustness of Neural Networks Learned by Transduction
Jiefeng Chen, Xi Wu, Yang Guo, Yingyu Liang, Somesh Jha
http://arxiv.org/abs/2110.14735v1

• [cs.LG]Towards Hyperparameter-free Policy Selection for Offline Reinforcement Learning
Siyuan Zhang, Nan Jiang
http://arxiv.org/abs/2110.14000v2

• [cs.LG]Towards Model Agnostic Federated Learning Using Knowledge Distillation
Andrei Afonin, Sai Praneeth Karimireddy
http://arxiv.org/abs/2110.15210v1

• [cs.LG]Towards a Taxonomy of Graph Learning Datasets
Renming Liu, Semih Cantürk, Frederik Wenkel, Dylan Sandfelder, Devin Kreuzer, Anna Little, Sarah McGuire, Leslie O’Bray, Michael Perlmutter, Bastian Rieck, Matthew Hirn, Guy Wolf, Ladislav Rampášek
http://arxiv.org/abs/2110.14809v1

• [cs.LG]URLB: Unsupervised Reinforcement Learning Benchmark
Michael Laskin, Denis Yarats, Hao Liu, Kimin Lee, Albert Zhan, Kevin Lu, Catherine Cang, Lerrel Pinto, Pieter Abbeel
http://arxiv.org/abs/2110.15191v1

• [cs.LG]Understanding How Encoder-Decoder Architectures Attend
Kyle Aitken, Vinay V Ramasesh, Yuan Cao, Niru Maheswaranathan
http://arxiv.org/abs/2110.15253v1

• [cs.LG]Using Time-Series Privileged Information for Provably Efficient Learning of Prediction Models
Rickard Karlsson, Martin Willbo, Zeshan Hussain, Rahul G. Krishnan, David Sontag, Fredrik D. Johansson
http://arxiv.org/abs/2110.14993v1

• [cs.LG]Wasserstein Distance Maximizing Intrinsic Control
Ishan Durugkar, Steven Hansen, Stephen Spencer, Volodymyr Mnih
http://arxiv.org/abs/2110.15331v1

• [cs.LG]You Are the Best Reviewer of Your Own Papers: An Owner-Assisted Scoring Mechanism
Weijie J. Su
http://arxiv.org/abs/2110.14802v1

• [cs.MA]Integrated Task Assignment and Path Planning for Capacitated Multi-Agent Pickup and Delivery
Zhe Chen, Javier Alonso-Mora, Xiaoshan Bai, Daniel D. Harabor, Peter J. Stuckey
http://arxiv.org/abs/2110.14891v1

• [cs.NI]Deep Learning Aided Packet Routing in Aeronautical Ad-Hoc Networks Relying on Real Flight Data: From Single-Objective to Near-Pareto Multi-Objective Optimization
Dong Liu, Jiankang Zhang, Jingjing Cui, Soon-Xin Ng, Robert G. Maunder, Lajos Hanzo
http://arxiv.org/abs/2110.15145v1

• [cs.NI]Deep Learning Aided Routing for Space-Air-Ground Integrated Networks Relying on Real Satellite, Flight, and Shipping Data
Dong Liu, Jiankang Zhang, Jingjing Cui, Soon-Xin Ng, Robert G. Maunder, Lajos Hanzo
http://arxiv.org/abs/2110.15138v1

• [cs.NI]Deep Reinforcement Learning Aided Packet-Routing For Aeronautical Ad-Hoc Networks Formed by Passenger Planes
Dong Liu, Jingjing Cui, Jiankang Zhang, Chenyang Yang, Lajos Hanzo
http://arxiv.org/abs/2110.15146v1

• [cs.RO]A Novel Sample-efficient Deep Reinforcement Learning with Episodic Policy Transfer for PID-Based Control in Cardiac Catheterization Robots
Olatunji Mumini Omisore, Toluwanimi Akinyemi, Wenke Duan, Wenjing Du, Lei Wang
http://arxiv.org/abs/2110.14941v1

• [cs.RO]An Adaptable Approach to Learn Realistic Legged Locomotion without Examples
Daniel Felipe Ordoñez Apraez, Antonio Agudo, Francesc Moreno-Noguer, Mario Martin
http://arxiv.org/abs/2110.14998v1

• [cs.RO]An Autonomous Probing System for Collecting Measurements at Depth from Small Surface Vehicles
Yuying Huang, Yiming Yao, Johanna Hansen, Jeremy Mallette, Sandeep Manjanna, Gregory Dudek, David Meger
http://arxiv.org/abs/2110.14738v1

• [cs.RO]An Improved Positioning Accuracy Method of a Robot Based on Particle Filter
Rashid Ali, Dil Nawaz Hakro, Yongping He, Wenpeng Fu, Zhiqiang Cao
http://arxiv.org/abs/2110.14635v1

• [cs.RO]Efficient Placard Discovery for Semantic Mapping During Frontier Exploration
David Balaban, Harshavardhan Jagannathan, Henry Liu, Justin Hart
http://arxiv.org/abs/2110.14742v1

• [cs.RO]From Machine Learning to Robotics: Challenges and Opportunities for Embodied Intelligence
Nicholas Roy, Ingmar Posner, Tim Barfoot, Philippe Beaudoin, Yoshua Bengio, Jeannette Bohg, Oliver Brock, Isabelle Depatie, Dieter Fox, Dan Koditschek, Tomas Lozano-Perez, Vikash Mansinghka, Christopher Pal, Blake Richards, Dorsa Sadigh, Stefan Schaal, Gaurav Sukhatme, Denis Therien, Marc Toussaint, Michiel Van de Panne
http://arxiv.org/abs/2110.15245v1

• [cs.RO]GOMP-FIT: Grasp-Optimized Motion Planning for Fast Inertial Transport
Jeffrey Ichnowski, Yahav Avigal, Yi Liu, Ken Goldberg
http://arxiv.org/abs/2110.15326v1

• [cs.RO]Learning Actions for Drift-Free Navigation in Highly Dynamic Scenes
Mohd Omama, Sundar Sripada V. S., Sandeep Chinchali, K. Madhava Krishna
http://arxiv.org/abs/2110.14928v1

• [cs.RO]Learning Feasibility to Imitate Demonstrators with Different Dynamics
Zhangjie Cao, Yilun Hao, Mengxi Li, Dorsa Sadigh
http://arxiv.org/abs/2110.15142v1

• [cs.RO]Learning to Jump from Pixels
Gabriel B. Margolis, Tao Chen, Kartik Paigwar, Xiang Fu, Donghyun Kim, Sangbae Kim, Pulkit Agrawal
http://arxiv.org/abs/2110.15344v1

• [cs.RO]Millimeter Wave Wireless-Assisted Robotic Navigation with Link State Classification
Mingsheng Yin, Akshaj Veldanda, Amee Trivedi, Jeff Zhang, Kai Pfeiffer, Yaqi Hu, Siddharth Garg, Elza Erkip, Ludovic Righetti, Sundeep Rangan
http://arxiv.org/abs/2110.14789v1

• [cs.RO]Modeling, simulation, and optimization of a monopod hopping on yielding terrain
Juntao He
http://arxiv.org/abs/2110.14867v1

• [cs.RO]Multimotion Visual Odometry (MVO)
Kevin M. Judd, Jonathan D. Gammell
http://arxiv.org/abs/2110.15169v1

• [cs.RO]Orientation Probabilistic Movement Primitives on Riemannian Manifolds
Leonel Rozo, Vedant Dave
http://arxiv.org/abs/2110.15036v1

• [cs.RO]Sensing Anomalies as Potential Hazards: Datasets and Benchmarks
Dario Mantegazza, Carlos Redondo, Fran Espada, Luca M. Gambardella, Alessandro Giusti, Jérôme Guzzi
http://arxiv.org/abs/2110.14706v1

• [cs.RO]Similarity-Aware Skill Reproduction based on Multi-Representational Learning from Demonstration
Brendan Hertel, S. Reza Ahmadzadeh
http://arxiv.org/abs/2110.14817v1

• [cs.RO]Spatial Constraint Generation for Motion Planning in Dynamic Environments
Han Hu, Peyman Yadmellat
http://arxiv.org/abs/2110.14786v1

• [cs.SD]Neural Analysis and Synthesis: Reconstructing Speech from Self-Supervised Representations
Hyeong-Seok Choi, Juheon Lee, Wansoo Kim, Jie Hwan Lee, Hoon Heo, Kyogu Lee
http://arxiv.org/abs/2110.14513v2

• [cs.SI]CIIA:A New Algorithm for Community Detection
Zhang Renquan, Wang Yu, Wang Xiaolin, Sun Yuze, Tai Jilei
http://arxiv.org/abs/2110.15264v1

• [cs.SI]Finding a Concise, Precise, and Exhaustive Set of Near Bi-Cliques in Dynamic Graphs
Hyeonjeong Shin, Taehyung Kwon, Neil Shah, Kijung Shin
http://arxiv.org/abs/2110.14875v1

• [cs.SI]Graph Communal Contrastive Learning
Bolian Li, Baoyu Jing, Hanghang Tong
http://arxiv.org/abs/2110.14863v1

• [cs.SI]Streaming Local Community Detection through Approximate Conductance
Yanhao Yang, Meng Wang, David Bindel, Kun He
http://arxiv.org/abs/2110.14972v1

• [eess.IV]Alternating Learning Approach for Variational Networks and Undersampling Pattern in Parallel MRI Applications
Marcelo V. W. Zibetti, Florian Knoll, Ravinder R. Regatte
http://arxiv.org/abs/2110.14703v1

• [eess.IV]Deep Learning Analysis of Cardiac MRI in Legacy Datasets: Multi-Ethnic Study of Atherosclerosis
Avan Suinesiaputra, Charlene A Mauger, Bharath Ambale-Venkatesh, David A Bluemke, Josefine Dam Gade, Kathleen Gilbert, Mark Janse, Line Sofie Hald, Conrad Werkhoven, Colin Wu, Joao A Lima, Alistair A Young
http://arxiv.org/abs/2110.15144v1

• [eess.IV]Degraded Reference Image Quality Assessment
Shahrukh Athar, Zhou Wang
http://arxiv.org/abs/2110.14899v1

• [eess.IV]Diagnosis of COVID-19 Using Machine Learning and Deep Learning: A review
M. Rubaiyat Hossain Mondal, Subrato Bharati, Prajoy Podder
http://arxiv.org/abs/2110.14910v1

• [eess.IV]Improving Super-Resolution Performance using Meta-Attention Layers
Matthew Aquilina, Christian Galea, John Abela, Kenneth P. Camilleri, Reuben A. Farrugia
http://arxiv.org/abs/2110.14638v1

• [eess.IV]Lung Cancer Lesion Detection in Histopathology Images Using Graph-Based Sparse PCA Network
Sundaresh Ram, Wenfei Tang, Alexander J. Bell, Cara Spencer, Alexander Buschhaus, Charles R. Hatt, Marina Pasca diMagliano, Jeffrey J. Rodriguez, Stefanie Galban, Craig J. Galban
http://arxiv.org/abs/2110.14728v1

• [eess.IV]SCALP — Supervised Contrastive Learning for Cardiopulmonary Disease Classification and Localization in Chest X-rays using Patient Metadata
Ajay Jaiswal, Tianhao Li, Cyprian Zander, Yan Han, Justin F. Rousseau, Yifan Peng, Ying Ding
http://arxiv.org/abs/2110.14787v1

• [eess.IV]Sharp-GAN: Sharpness Loss Regularized GAN for Histopathology Image Synthesis
Sujata Butte, Haotian Wang, Min Xian, Aleksandar Vakanski
http://arxiv.org/abs/2110.14709v1

• [eess.SP]Self-supervised EEG Representation Learning for Automatic Sleep Staging
Chaoqi Yang, Danica Xiao, M. Brandon Westover, Jimeng Sun
http://arxiv.org/abs/2110.15278v1

• [eess.SP]V2iFi: in-Vehicle Vital Sign Monitoring via Compact RF Sensing
Tianyue Zheng, Zhe Chen, Chao Cai, Jun Luo, Xu Zhang
http://arxiv.org/abs/2110.14848v1

• [eess.SY]Cooperative Deep $今日学术视野(2021.10.30) - 图4$ -learning Framework for Environments Providing Image Feedback
Krishnan Raghavan, Vignesh Narayanan, Jagannathan Sarangapani
http://arxiv.org/abs/2110.15305v1

• [math.CO]Labeled sample compression schemes for complexes of oriented matroids
Victor Chepoi, Kolja Knauer, Manon Philibert
http://arxiv.org/abs/2110.15168v1

• [math.DS]Deeptime: a Python library for machine learning dynamical models from time series data
Moritz Hoffmann, Martin Scherer, Tim Hempel, Andreas Mardt, Brian de Silva, Brooke E. Husic, Stefan Klus, Hao Wu, Nathan Kutz, Steven L. Brunton, Frank Noé
http://arxiv.org/abs/2110.15013v1

• [math.FA]Sobolev-type embeddings for neural network approximation spaces
Philipp Grohs, Felix Voigtlaender
http://arxiv.org/abs/2110.15304v1

• [math.OC]A first-order primal-dual method with adaptivity to local smoothness
Maria-Luiza Vladarean, Yura Malitsky, Volkan Cevher
http://arxiv.org/abs/2110.15148v1

• [math.OC]Meta Subspace Optimization
Yoni Choukroun, Michael Katz
http://arxiv.org/abs/2110.14920v1

• [math.OC]Spectrahedral Regression
Eliza O’Reilly, Venkat Chandrasekaran
http://arxiv.org/abs/2110.14779v1

• [math.PR]Convergence of Conditional Entropy for Long Range Dependent Markov Chains
Andrew Feutrill, Matthew Roughan
http://arxiv.org/abs/2110.14881v1

• [math.PR]Stable distributions and domains of attraction for unitarily invariant Hermitian random matrix ensembles
Mario Kieburg, Jiyuan Zhang
http://arxiv.org/abs/2110.14877v1

• [math.ST]Algebraic algorithm for direct sampling from toric models
Shuhei Mano, Nobuki Takayama
http://arxiv.org/abs/2110.14992v1

• [math.ST]Approximately low-rank recovery from noisy and local measurements by convex program
Kiryung Lee, Rakshith Sharma Srinivasa, Marius Junge, Justin Romberg
http://arxiv.org/abs/2110.15205v1

• [math.ST]Nearest neighbor process: weak convergence and non-asymptotic bound
François Portier
http://arxiv.org/abs/2110.15083v1

• [physics.ao-ph]Using Non-Linear Causal Models to Study Aerosol-Cloud Interactions in the Southeast Pacific
Andrew Jesson, Peter Manshausen, Alyson Douglas, Duncan Watson-Parris, Yarin Gal, Philip Stier
http://arxiv.org/abs/2110.15084v1

• [physics.chem-ph]Generalizability of density functionals learned from differentiable programming on weakly correlated spin-polarized systems
Bhupalee Kalita, Ryan Pederson, Li Li, Kieron Burke
http://arxiv.org/abs/2110.14846v1

• [physics.flu-dyn]Stabilising viscous extensional flows using Reinforcement Learning
Marco Vona, Eric Lauga
http://arxiv.org/abs/2110.14677v1

• [q-bio.GN]MutFormer: A context-dependent transformer-based model to predict pathogenic missense mutations
Theodore Jiang, Li Fang, Kai Wang
http://arxiv.org/abs/2110.14746v1

• [q-bio.NC]Counterfactual Explanation of Brain Activity Classifiers using Image-to-Image Transfer by Generative Adversarial Network
Teppei Matsui, Masato Taki, Trung Quang Pham, Junichi Chikazoe, Koji Jimura
http://arxiv.org/abs/2110.14927v1

• [q-bio.PE]Thermodynamics of Evolution and the Origin of Life
Vitaly Vanchurin, Yuri I. Wolf, Eugene V. Koonin, Mikhail I. Katsnelson
http://arxiv.org/abs/2110.15066v1

• [q-bio.QM]Generating 3D Molecules Conditional on Receptor Binding Sites with Deep Generative Models
Matthew Ragoza, Tomohide Masuda, David Ryan Koes
http://arxiv.org/abs/2110.15200v1

• [q-fin.ST]Deep Calibration of Interest Rates Model
Mohamed Ben Alaya, Ahmed Kebaier, Djibril Sarr
http://arxiv.org/abs/2110.15133v1

• [q-fin.TR]Trading via Selective Classification
Nestoras Chalkidis, Rahul Savani
http://arxiv.org/abs/2110.14914v1

• [quant-ph]Subtleties in the trainability of quantum machine learning models
Supanut Thanasilp, Samson Wang, Nhat A. Nghiem, Patrick J. Coles, M. Cerezo
http://arxiv.org/abs/2110.14753v1

• [quant-ph]Ultimate Limits of Quantum Channel Discrimination
Kun Fang, Gilad Gour, Xin Wang
http://arxiv.org/abs/2110.14842v1

• [stat.AP]A spatio-temporal analysis of NO $今日学术视野(2021.10.30) - 图5$ concentrations during the Italian 2020 COVID-19 lockdown
Guido Fioravanti, Michela Cameletti, Sara Martino, Giorgio Cattani, Enrico Pisoni
http://arxiv.org/abs/2110.15020v1

• [stat.ME]Efficient Algorithms and Implementation of a Semiparametric Joint Model for Longitudinal and Competing Risks Data: With Applications to Massive Biobank Data
Shanpeng Li, Ning Li, Hong Wang, Jin Zhou, Hua Zhou, Gang Li
http://arxiv.org/abs/2110.14822v1

• [stat.ME]Living on the Edge: An Unified Approach to Antithetic Sampling
Roberto Casarin, Radu V. Craiu, Lorenzo Frattarolo, Christian P. Robert
http://arxiv.org/abs/2110.15124v1

• [stat.ME]Location-Adaptive Change-Point Testing for Time Series
Linlin Dai, Rui She
http://arxiv.org/abs/2110.15071v1

• [stat.ME]Robust model-based estimation for binary outcomes in genomics studies
Suyoung Park, Alexander E. Lipka, Daniel J. Eck
http://arxiv.org/abs/2110.15189v1

• [stat.ME]The Balancing Act in Causal Inference
Eli Ben-Michael, Avi Feller, David A. Hirshberg, José R. Zubizarreta
http://arxiv.org/abs/2110.14831v1

• [stat.ME]Warped Dynamic Linear Models for Time Series of Counts
Brian King, Daniel R. Kowal
http://arxiv.org/abs/2110.14790v1

• [stat.ML]An $今日学术视野(2021.10.30) - 图6$ -based Kernel Conditional Independence Test
Meyer Scetbon, Laurent Meunier, Yaniv Romano
http://arxiv.org/abs/2110.14868v1

• [stat.ML]Convolutional Deep Exponential Families
Chengkuan Hong, Christian R. Shelton
http://arxiv.org/abs/2110.14800v1

• [stat.ML]Generalized Shape Metrics on Neural Representations
Alex H. Williams, Erin Kunz, Simon Kornblith, Scott W. Linderman
http://arxiv.org/abs/2110.14739v1

• [stat.ML]MMD Aggregated Two-Sample Test
Antonin Schrab, Ilmun Kim, Mélisande Albert, Béatrice Laurent, Benjamin Guedj, Arthur Gretton
http://arxiv.org/abs/2110.15073v1

• [stat.ML]Minimax Optimal Quantile and Semi-Adversarial Regret via Root-Logarithmic Regularizers
Jeffrey Negrea, Blair Bilodeau, Nicolò Campolongo, Francesco Orabona, Daniel M. Roy
http://arxiv.org/abs/2110.14804v1

• [stat.ML]Probabilistic Autoencoder using Fisher Information
Johannes Zacherl, Philipp Frank, Torsten A. Enßlin
http://arxiv.org/abs/2110.14947v1

• [stat.ML]VACA: Design of Variational Graph Autoencoders for Interventional and Counterfactual Queries
Pablo Sanchez-Martin, Miriam Rateike, Isabel Valera
http://arxiv.org/abs/2110.14690v1

• [stat.ML]Vector-valued Gaussian Processes on Riemannian Manifolds via Gauge Equivariant Projected Kernels
Michael Hutchinson, Alexander Terenin, Viacheslav Borovitskiy, So Takao, Yee Whye Teh, Marc Peter Deisenroth
http://arxiv.org/abs/2110.14423v2

• [stat.OT]On rereading Savage
Yudi Pawitan, Youngjo Lee
http://arxiv.org/abs/2110.15012v1