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.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.MA - 多代理系统 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.GN - 一般经济学 eess.AS - 语音处理 eess.IV - 图像与视频处理 eess.SP - 信号处理 math.CO - 组合数学 math.OC - 优化与控制 math.PR - 概率 math.ST - 统计理论 physics.med-ph - 医学物理学 q-bio.PE - 人口与发展 q-fin.ST - 统计金融学 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

    • [cs.AI]A Cooperative Memory Network for Personalized Task-oriented Dialogue Systems with Incomplete User Profiles
    • [cs.AI]Accelerated Sparse Neural Training: A Provable and Efficient Method to Find N:M Transposable Masks
    • [cs.AI]Design a Technology Based on the Fusion of Genetic Algorithm, Neural network and Fuzzy logic
    • [cs.AI]Diverse Auto-Curriculum is Critical for Successful Real-World Multiagent Learning Systems
    • [cs.AI]Dynamic Virtual Graph Significance Networks for Predicting Influenza
    • [cs.AI]Dynamic neighbourhood optimisation for task allocation using multi-agent
    • [cs.AI]Engineering Education in the Age of Autonomous Machines
    • [cs.AI]Enhancing Hierarchical Information by Using Metric Cones for Graph Embedding
    • [cs.AI]GraphGallery: A Platform for Fast Benchmarking and Easy Development of Graph Neural Networks Based Intelligent Software
    • [cs.AI]Information Ranking Using Optimum-Path Forest
    • [cs.AI]Music Harmony Generation, through Deep Learning and Using a Multi-Objective Evolutionary Algorithm
    • [cs.AI]Nominal Unification and Matching of Higher Order Expressions with Recursive Let
    • [cs.AI]ResNet-LDDMM: Advancing the LDDMM Framework Using Deep Residual Networks
    • [cs.AI]Resource allocation in dynamic multiagent systems
    • [cs.AI]The Yin-Yang dataset
    • [cs.AI]Transferring Domain Knowledge with an Adviser in Continuous Tasks
    • [cs.AI]Value of Information for Argumentation based Intelligence Analysis
    • [cs.AI]What Do We Want From Explainable Artificial Intelligence (XAI)? — A Stakeholder Perspective on XAI and a Conceptual Model Guiding Interdisciplinary XAI Research
    • [cs.AR]IronMan: GNN-assisted Design Space Exploration in High-Level Synthesis via Reinforcement Learning
    • [cs.CL]Boosting Low-Resource Biomedical QA via Entity-Aware Masking Strategies
    • [cs.CL]End-to-End Automatic Speech Recognition with Deep Mutual Learning
    • [cs.CL]Exploring Transformers in Natural Language Generation: GPT, BERT, and XLNet
    • [cs.CL]FEWS: Large-Scale, Low-Shot Word Sense Disambiguation with the Dictionary
    • [cs.CL]Fast End-to-End Speech Recognition via a Non-Autoregressive Model and Cross-Modal Knowledge Transferring from BERT
    • [cs.CL]Have Attention Heads in BERT Learned Constituency Grammar?
    • [cs.CL]Hierarchical Transformer-based Large-Context End-to-end ASR with Large-Context Knowledge Distillation
    • [cs.CL]How COVID-19 Is Changing Our Language : Detecting Semantic Shift in Twitter Word Embeddings
    • [cs.CL]Large-Context Conversational Representation Learning: Self-Supervised Learning for Conversational Documents
    • [cs.CL]MAPGN: MAsked Pointer-Generator Network for sequence-to-sequence pre-training
    • [cs.CL]Meta Back-translation
    • [cs.CL]NoiseQA: Challenge Set Evaluation for User-Centric Question Answering
    • [cs.CL]Non-Autoregressive Text Generation with Pre-trained Language Models
    • [cs.CL]Revisiting Language Encoding in Learning Multilingual Representations
    • [cs.CR]Machine Learning Based Cyber Attacks Targeting on Controlled Information: A Survey
    • [cs.CR]Recent Developments in Blockchain Technology and their Impact on Energy Consumption
    • [cs.CR]Temporal-Amount Snapshot MultiGraph for Ethereum Transaction Tracking
    • [cs.CV]A Benchmark of Ocular Disease Intelligent Recognition: One Shot for Multi-disease Detection
    • [cs.CV]A Multiscale Graph Convolutional Network for Change Detection in Homogeneous and Heterogeneous Remote Sensing Images
    • [cs.CV]A comparative study on movement feature in different directions for micro-expression recognition
    • [cs.CV]Accurate and Clear Precipitation Nowcasting with Consecutive Attention and Rain-map Discrimination
    • [cs.CV]Ada-SISE: Adaptive Semantic Input Sampling for Efficient Explanation of Convolutional Neural Networks
    • [cs.CV]AlphaNet: Improved Training of Supernet with Alpha-Divergence
    • [cs.CV]Boosting Deep Transfer Learning for COVID-19 Classification
    • [cs.CV]Does deep machine vision have just noticeable difference (JND)?
    • [cs.CV]EfficientLPS: Efficient LiDAR Panoptic Segmentation
    • [cs.CV]Feature Pyramid Network with Multi-Head Attention for Se-mantic Segmentation of Fine-Resolution Remotely Sensed Im-ages
    • [cs.CV]Instance Localization for Self-supervised Detection Pretraining
    • [cs.CV]Integrated Grad-CAM: Sensitivity-Aware Visual Explanation of Deep Convolutional Networks via Integrated Gradient-Based Scoring
    • [cs.CV]Just Noticeable Difference for Machine Perception and Generation of Regularized Adversarial Images with Minimal Perturbation
    • [cs.CV]LEAD: LiDAR Extender for Autonomous Driving
    • [cs.CV]Learning to Recognize Actions on Objects in Egocentric Video with Attention Dictionaries
    • [cs.CV]MITNet: GAN Enhanced Magnetic Induction Tomography Based on Complex CNN
    • [cs.CV]Multi-Attribute Enhancement Network for Person Search
    • [cs.CV]PSA-Net: Deep Learning based Physician Style-Aware Segmentation Network for Post-Operative Prostate Cancer Clinical Target Volume
    • [cs.CV]Reciprocal Distance Transform Maps for Crowd Counting and People Localization in Dense Crowd
    • [cs.CV]Restore from Restored: Single-image Inpainting
    • [cs.CV]Self-Supervised Features Improve Open-World Learning
    • [cs.CV]SiMaN: Sign-to-Magnitude Network Binarization
    • [cs.CV]TransFuse: Fusing Transformers and CNNs for Medical Image Segmentation
    • [cs.CV]Twin Augmented Architectures for Robust Classification of COVID-19 Chest X-Ray Images
    • [cs.CV]Uncertainty-based method for improving poorly labeled segmentation datasets
    • [cs.CV]VA-RED今日学术视野(2021.2.18) - 图1: Video Adaptive Redundancy Reduction
    • [cs.CY]A Mental Trespass? Unveiling Truth, Exposing Thoughts and Threatening Civil Liberties with Non-Invasive AI Lie Detection
    • [cs.CY]Conversations Gone Alright: Quantifying and Predicting Prosocial Outcomes in Online Conversations
    • [cs.CY]Could you become more credible by being White? Assessing Impact of Race on Credibility with Deepfakes
    • [cs.CY]Spatio-Temporal Multi-step Prediction of Influenza Outbreaks
    • [cs.CY]Towards an accountable Internet of Things: A call for ‘reviewability’
    • [cs.DC]AdEle: An Adaptive Congestion-and-Energy-Aware Elevator Selection for Partially Connected 3D NoCs
    • [cs.DC]All You Need is DAG
    • [cs.DC]Fast Validated Byzantine Broadcast
    • [cs.DS]Deterministic CONGEST Algorithm for MDS on Bounded Arboricity Graphs
    • [cs.DS]Fair and Optimal Cohort Selection for Linear Utilities
    • [cs.DS]Faster Kernel Matrix Algebra via Density Estimation
    • [cs.DS]Submodular Maximization subject to a Knapsack Constraint: Combinatorial Algorithms with Near-optimal Adaptive Complexity
    • [cs.GT]Follow-the-Regularizer-Leader Routes to Chaos in Routing Games
    • [cs.HC]”From What I see, this makes sense”: Seeing meaning in algorithmic results
    • [cs.IR]KnowledgeCheckR: Intelligent Techniques for Counteracting Forgetting
    • [cs.IR]NeuralNDCG: Direct Optimisation of a Ranking Metric via Differentiable Relaxation of Sorting
    • [cs.IR]Recommender Systems for Configuration Knowledge Engineering
    • [cs.IR]User Embedding based Neighborhood Aggregation Method for Inductive Recommendation
    • [cs.IR]User-Inspired Posterior Network for Recommendation Reason Generation
    • [cs.IT]Active Privacy-utility Trade-off Against a Hypothesis Testing Adversary
    • [cs.IT]Belief Propagation List Ordered Statistics Decoding of Polar Codes
    • [cs.IT]Capacity-Achieving Private Information Retrieval Schemes from Uncoded Storage Constrained Servers with Low Sub-packetization
    • [cs.IT]Channel Estimation and Hybrid Combining for Wideband Terahertz Massive MIMO Systems
    • [cs.IT]Coupled-Channel Enhanced SSFM for Digital Backpropagation in WDM Systems
    • [cs.IT]Lower bound on Wyner’s Common Information
    • [cs.IT]Polar Codes for Automorphism Ensemble Decoding
    • [cs.IT]Sparse Channel Reconstruction With Nonconvex Regularizer via DC Programming for Massive MIMO Systems
    • [cs.IT]Speeding Up Private Distributed Matrix Multiplication via Bivariate Polynomial Codes
    • [cs.LG]A Generic Descent Aggregation Framework for Gradient-based Bi-level Optimization
    • [cs.LG]A Koopman Approach to Understanding Sequence Neural Models
    • [cs.LG]A Sub-band Approach to Deep Denoising Wavelet Networks and a Frequency-adaptive Loss for Perceptual Quality
    • [cs.LG]A Survey of Machine Learning for Computer Architecture and Systems
    • [cs.LG]A Thorough View of Exact Inference in Graphs from the Degree-4 Sum-of-Squares Hierarchy
    • [cs.LG]Adaptive Weighting Scheme for Automatic Time-Series Data Augmentation
    • [cs.LG]Adversarial Targeted Forgetting in Regularization and Generative Based Continual Learning Models
    • [cs.LG]An AutoML-based Approach to Multimodal Image Sentiment Analysis
    • [cs.LG]Analysis of feature learning in weight-tied autoencoders via the mean field lens
    • [cs.LG]COMBO: Conservative Offline Model-Based Policy Optimization
    • [cs.LG]CTAB-GAN: Effective Table Data Synthesizing
    • [cs.LG]Certified Robustness to Programmable Transformations in LSTMs
    • [cs.LG]Chickenpox Cases in Hungary: a Benchmark Dataset for Spatiotemporal Signal Processing with Graph Neural Networks
    • [cs.LG]Classification of multivariate weakly-labelled time-series with attention
    • [cs.LG]Communication-Efficient Distributed Cooperative Learning with Compressed Beliefs
    • [cs.LG]Constructing Multiclass Classifiers using Binary Classifiers Under Log-Loss
    • [cs.LG]Dataset Condensation with Differentiable Siamese Augmentation
    • [cs.LG]Differential Privacy and Byzantine Resilience in SGD: Do They Add Up?
    • [cs.LG]Differentially Private Quantiles
    • [cs.LG]Distributionally-Constrained Policy Optimization via Unbalanced Optimal Transport
    • [cs.LG]Don’t Just Blame Over-parametrization for Over-confidence: Theoretical Analysis of Calibration in Binary Classification
    • [cs.LG]EDITH :ECG biometrics aided by Deep learning for reliable Individual auTHentication
    • [cs.LG]EPE-NAS: Efficient Performance Estimation Without Training for Neural Architecture Search
    • [cs.LG]Efficient Competitions and Online Learning with Strategic Forecasters
    • [cs.LG]Efficient Learning with Arbitrary Covariate Shift
    • [cs.LG]Few-Shot Graph Learning for Molecular Property Prediction
    • [cs.LG]GP-Tree: A Gaussian Process Classifier for Few-Shot Incremental Learning
    • [cs.LG]GradInit: Learning to Initialize Neural Networks for Stable and Efficient Training
    • [cs.LG]HDMI: High-order Deep Multiplex Infomax
    • [cs.LG]Hierarchical VAEs Know What They Don’t Know
    • [cs.LG]How to Learn when Data Reacts to Your Model: Performative Gradient Descent
    • [cs.LG]Identifying Misinformation from Website Screenshots
    • [cs.LG]Improper Learning with Gradient-based Policy Optimization
    • [cs.LG]Improving Bayesian Inference in Deep Neural Networks with Variational Structured Dropout
    • [cs.LG]Improving the Accuracy Of MEPDG Climate Modeling Using Radial Basis Function
    • [cs.LG]IntSGD: Floatless Compression of Stochastic Gradients
    • [cs.LG]Integrating Floor Plans into Hedonic Models for Rent Price Appraisal
    • [cs.LG]Inverse Reinforcement Learning in the Continuous Setting with Formal Guarantees
    • [cs.LG]Joint self-supervised blind denoising and noise estimation
    • [cs.LG]KNH: Multi-View Modeling with K-Nearest Hyperplanes Graph for Misinformation Detection
    • [cs.LG]Learning Invariant Representations using Inverse Contrastive Loss
    • [cs.LG]Local Hyper-flow Diffusion
    • [cs.LG]Low Curvature Activations Reduce Overfitting in Adversarial Training
    • [cs.LG]MARINA: Faster Non-Convex Distributed Learning with Compression
    • [cs.LG]Maximizing Conditional Entropy for Batch-Mode Active Learning of Perceptual Metrics
    • [cs.LG]Message Passing Descent for Efficient Machine Learning
    • [cs.LG]Model-based Meta Reinforcement Learning using Graph Structured Surrogate Models
    • [cs.LG]Momentum Residual Neural Networks
    • [cs.LG]Offline Model-Based Optimization via Normalized Maximum Likelihood Estimation
    • [cs.LG]One Line To Rule Them All: Generating LO-Shot Soft-Label Prototypes
    • [cs.LG]Online hyperparameter optimization by real-time recurrent learning
    • [cs.LG]Online learning of Riemannian hidden Markov models in homogeneous Hadamard spaces
    • [cs.LG]Optimal Algorithms for Private Online Learning in a Stochastic Environment
    • [cs.LG]Orthogonal Features-based EEG Signal Denoising using Fractionally Compressed AutoEncoder
    • [cs.LG]Phase-Modulated Radar Waveform Classification Using Deep Networks
    • [cs.LG]Posterior-Aided Regularization for Likelihood-Free Inference
    • [cs.LG]RMIX: Learning Risk-Sensitive Policies for Cooperative Reinforcement Learning Agents
    • [cs.LG]Scaling Up Exact Neural Network Compression by ReLU Stability
    • [cs.LG]Steadily Learn to Drive with Virtual Memory
    • [cs.LG]Structured Graph Learning for Scalable Subspace Clustering: From Single-view to Multi-view
    • [cs.LG]Successive Pruning for Model Compression via Rate Distortion Theory
    • [cs.LG]TeraPipe: Token-Level Pipeline Parallelism for Training Large-Scale Language Models
    • [cs.LG]Topological Deep Learning: Classification Neural Networks
    • [cs.LG]Topological Graph Neural Networks
    • [cs.LG]Training Larger Networks for Deep Reinforcement Learning
    • [cs.LG]Training Stacked Denoising Autoencoders for Representation Learning
    • [cs.LG]Unified Shapley Framework to Explain Prediction Drift
    • [cs.LG]Unifying Lower Bounds on Prediction Dimension of Consistent Convex Surrogates
    • [cs.LG]Using Data Assimilation to Train a Hybrid Forecast System that Combines Machine-Learning and Knowledge-Based Components
    • [cs.LG]Zero-Shot Adaptation for mmWave Beam-Tracking on Overhead Messenger Wires through Robust Adversarial Reinforcement Learning
    • [cs.MA]DFAC Framework: Factorizing the Value Function via Quantile Mixture for Multi-Agent Distributional Q-Learning
    • [cs.MA]Quantifying environment and population diversity in multi-agent reinforcement learning
    • [cs.NE]A Federated Data-Driven Evolutionary Algorithm
    • [cs.NI]Automated Identification of Vulnerable Devices in Networks using Traffic Data and Deep Learning
    • [cs.NI]Federated Learning over Wireless Networks: A Band-limited Coordinated Descent Approach
    • [cs.RO]Composing Pick-and-Place Tasks By Grounding Language
    • [cs.RO]Darboux-Frame-Based Parametrization for a Spin-Rolling Sphere on a Plane: A Nonlinear Transformation of Underactuated System to Fully-Actuated Model
    • [cs.RO]Design Iterations for Passive Aerial Manipulator
    • [cs.RO]Graph-based Motion Planning for Automated Vehicles using Multi-model Branching and Admissible Heuristics
    • [cs.RO]Hough2Map — Iterative Event-based Hough Transform for High-Speed Railway Mapping
    • [cs.RO]Learning the Noise of Failure: Intelligent System Tests for Robots
    • [cs.RO]Optimal Mixed Discrete-Continuous Planningfor Linear Hybrid Systems
    • [cs.RO]Probabilistic Localization of Insect-Scale Drones on Floating-Gate Inverter Arrays
    • [cs.RO]Supervised Training of Dense Object Nets using Optimal Descriptors for Industrial Robotic Applications
    • [cs.SD]Anomalous Sound Detection with Machine Learning: A Systematic Review
    • [cs.SD]Improving Deep-learning-based Semi-supervised Audio Tagging with Mixup
    • [cs.SD]Improving speech recognition models with small samples for air traffic control systems
    • [cs.SD]Semi Supervised Learning For Few-shot Audio Classification By Episodic Triplet Mining
    • [cs.SE]D2A: A Dataset Built for AI-Based Vulnerability Detection Methods Using Differential Analysis
    • [cs.SI]A Hidden Challenge of Link Prediction: Which Pairs to Check?
    • [cs.SI]Deciphering Social Opinion Polarization Towards Political Event Based on Content and Structural Analysis
    • [cs.SI]Empirical Characterization of Graph Sampling Algorithms
    • [cs.SI]Evaluating Node Embeddings of Complex Networks
    • [cs.SI]Meta-Path-Free Representation Learning on Heterogeneous Networks
    • [cs.SI]User Characteristics of Olympic Gold Medallists on Instagram: A Quantitative Analysis of Rio2016
    • [econ.GN]An Effort to Measure Customer Relationship Performance in Indonesia’s Fintech Industry
    • [econ.GN]Interdependencies between Mining Costs, Mining Rewards and Blockchain Security
    • [econ.GN]Supportive 5G infrastructure policies are essential for universal 6G: Evidence from an open-source techno-economic simulation model using remote sensing
    • [eess.AS]Axial Residual Networks for CycleGAN-based Voice Conversion
    • [eess.AS]Context-Aware Prosody Correction for Text-Based Speech Editing
    • [eess.AS]PeriodNet: A non-autoregressive waveform generation model with a structure separating periodic and aperiodic components
    • [eess.IV]A Deep-Learning Approach For Direct Whole-Heart Mesh Reconstruction
    • [eess.IV]Deep Equilibrium Architectures for Inverse Problems in Imaging
    • [eess.IV]Going Beyond Saliency Maps: Training Deep Models to Interpret Deep Models
    • [eess.IV]On Mathews Correlation Coefficient and Improved Distance Map Loss for Automatic Glacier Calving Front Segmentation in SAR Imagery
    • [eess.SP]Enhancing the Spatio-temporal Observability of Grid-Edge Resources in Distribution Grids
    • [eess.SP]On the use of generative deep neural networks to synthesize artificial multichannel EEG signals
    • [math.CO]Designs in finite metric spaces: a probabilistic approach
    • [math.OC]Decentralized Distributed Optimization for Saddle Point Problems
    • [math.OC]Efficient Discretizations of Optimal Transport
    • [math.OC]Learning Symbolic Expressions: Mixed-Integer Formulations, Cuts, and Heuristics
    • [math.OC]Stochastic Variance Reduction for Variational Inequality Methods
    • [math.PR]Concentration of measure and generalized product ofrandom vectors with an application to Hanson-Wright-like inequalities
    • [math.ST]Distribution-Free Conditional Median Inference
    • [math.ST]Exponential confidence interval based on the recursive Wolverton-Wagner density estimation
    • [math.ST]Signed variable optimal kernel for non-parametric density estimation
    • [math.ST]Tight Risk Bound for High Dimensional Time Series Completion
    • [math.ST]Unbiased simulation of rare events in continuous time
    • [physics.med-ph]Corneal Pachymetry by AS-OCT after Descemet’s Membrane Endothelial Keratoplasty
    • [physics.med-ph]DAN-Net: Dual-Domain Adaptive-Scaling Non-local Network for CT Metal Artifact Reduction
    • [q-bio.PE]A stochastic SIR model for the analysis of the COVID-19 Italian epidemic
    • [q-fin.ST]On Technical Trading and Social Media Indicators in Cryptocurrencies’ Price Classification Through Deep Learning
    • [quant-ph]Universal Adversarial Examples and Perturbations for Quantum Classifiers
    • [stat.AP]Simple statistical models and sequential deep learning for Lithium-ion batteries degradation under dynamic conditions: Fractional Polynomials vs Neural Networks
    • [stat.ME]A Bayesian Framework for Generation of Fully Synthetic Mixed Datasets
    • [stat.ME]A New Method to Determine the Presence of Continuous Variation in Parameters of Biological Growth Curve Models
    • [stat.ME]Controlling False Discovery Rates Using Null Bootstrapping
    • [stat.ME]The DeCAMFounder: Non-Linear Causal Discovery in the Presence of Hidden Variables
    • [stat.ME]The MELODIC family for simultaneous binary logistic regression in a reduced space
    • [stat.ME]Trees-Based Models for Correlated Data
    • [stat.ML]A Law of Robustness for Weight-bounded Neural Networks
    • [stat.ML]Capturing the learning curves of generic features maps for realistic data sets with a teacher-student model
    • [stat.ML]Conditional Distributional Treatment Effect with Kernel Conditional Mean Embeddings and U-Statistic Regression
    • [stat.ML]Differentiable Particle Filtering via Entropy-Regularized Optimal Transport
    • [stat.ML]Double-descent curves in neural networks: a new perspective using Gaussian processes
    • [stat.ML]Making the most of your day: online learning for optimal allocation of time
    • [stat.ML]The Randomized Elliptical Potential Lemma with an Application to Linear Thompson Sampling
    • [stat.ML]Tighter Bounds on the Log Marginal Likelihood of Gaussian Process Regression Using Conjugate Gradients
    • [stat.ML]Top-今日学术视野(2021.2.18) - 图2 eXtreme Contextual Bandits with Arm Hierarchy

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    • [cs.AI]A Cooperative Memory Network for Personalized Task-oriented Dialogue Systems with Incomplete User Profiles
    Jiahuan Pei, Pengjie Ren, Maarten de Rijke
    http://arxiv.org/abs/2102.08322v1

    • [cs.AI]Accelerated Sparse Neural Training: A Provable and Efficient Method to Find N:M Transposable Masks
    Itay Hubara, Brian Chmiel, Moshe Island, Ron Banner, Seffi Naor, Daniel Soudry
    http://arxiv.org/abs/2102.08124v1

    • [cs.AI]Design a Technology Based on the Fusion of Genetic Algorithm, Neural network and Fuzzy logic
    Raid R. Al-Nima, Fawaz S. Abdullah, Ali N. Hamoodi
    http://arxiv.org/abs/2102.08035v1

    • [cs.AI]Diverse Auto-Curriculum is Critical for Successful Real-World Multiagent Learning Systems
    Yaodong Yang, Jun Luo, Ying Wen, Oliver Slumbers, Daniel Graves, Haitham Bou Ammar, Jun Wang, Matthew E. Taylor
    http://arxiv.org/abs/2102.07659v2

    • [cs.AI]Dynamic Virtual Graph Significance Networks for Predicting Influenza
    Jie Zhang, Pengfei Zhou, Hongyan Wu
    http://arxiv.org/abs/2102.08122v1

    • [cs.AI]Dynamic neighbourhood optimisation for task allocation using multi-agent
    Niall Creech, Natalia Criado Pacheco, Simon Miles
    http://arxiv.org/abs/2102.08307v1

    • [cs.AI]Engineering Education in the Age of Autonomous Machines
    Shaoshan Liu, Jean-Luc Gaudiot, Hironori Kasahara
    http://arxiv.org/abs/2102.07900v1

    • [cs.AI]Enhancing Hierarchical Information by Using Metric Cones for Graph Embedding
    Daisuke Takehara, Kei Kobayashi
    http://arxiv.org/abs/2102.08014v1

    • [cs.AI]GraphGallery: A Platform for Fast Benchmarking and Easy Development of Graph Neural Networks Based Intelligent Software
    Jintang Li, Kun Xu, Liang Chen, Zibin Zheng, Xiao Liu
    http://arxiv.org/abs/2102.07933v1

    • [cs.AI]Information Ranking Using Optimum-Path Forest
    Nathalia Q. Ascenção, Luis C. S. Afonso, Danilo Colombo, Luciano Oliveira, João P. Papa
    http://arxiv.org/abs/2102.07917v1

    • [cs.AI]Music Harmony Generation, through Deep Learning and Using a Multi-Objective Evolutionary Algorithm
    Maryam Majidi, Rahil Mahdian Toroghi
    http://arxiv.org/abs/2102.07960v1

    • [cs.AI]Nominal Unification and Matching of Higher Order Expressions with Recursive Let
    Manfred Schmidt-Schauß, Temur Kutsia, Jordi Levy, Mateu Villaret, Yunus Kutz
    http://arxiv.org/abs/2102.08146v1

    • [cs.AI]ResNet-LDDMM: Advancing the LDDMM Framework Using Deep Residual Networks
    Boulbaba Ben Amor, Sylvain Arguillère, Ling Shao
    http://arxiv.org/abs/2102.07951v1

    • [cs.AI]Resource allocation in dynamic multiagent systems
    Niall Creech, Natalia Criado Pacheco, Simon Miles
    http://arxiv.org/abs/2102.08317v1

    • [cs.AI]The Yin-Yang dataset
    Laura Kriener, Julian Göltz, Mihai A. Petrovici
    http://arxiv.org/abs/2102.08211v1

    • [cs.AI]Transferring Domain Knowledge with an Adviser in Continuous Tasks
    Rukshan Wijesinghe, Kasun Vithanage, Dumindu Tissera, Alex Xavier, Subha Fernando, Jayathu Samarawickrama
    http://arxiv.org/abs/2102.08029v1

    • [cs.AI]Value of Information for Argumentation based Intelligence Analysis
    Todd Robinson
    http://arxiv.org/abs/2102.08180v1

    • [cs.AI]What Do We Want From Explainable Artificial Intelligence (XAI)? — A Stakeholder Perspective on XAI and a Conceptual Model Guiding Interdisciplinary XAI Research
    Markus Langer, Daniel Oster, Timo Speith, Holger Hermanns, Lena Kästner, Eva Schmidt, Andreas Sesing, Kevin Baum
    http://arxiv.org/abs/2102.07817v1

    • [cs.AR]IronMan: GNN-assisted Design Space Exploration in High-Level Synthesis via Reinforcement Learning
    Nan Wu, Yuan Xie, Cong Hao
    http://arxiv.org/abs/2102.08138v1

    • [cs.CL]Boosting Low-Resource Biomedical QA via Entity-Aware Masking Strategies
    Gabriele Pergola, Elena Kochkina, Lin Gui, Maria Liakata, Yulan He
    http://arxiv.org/abs/2102.08366v1

    • [cs.CL]End-to-End Automatic Speech Recognition with Deep Mutual Learning
    Ryo Masumura, Mana Ihori, Akihiko Takashima, Tomohiro Tanaka, Takanori Ashihara
    http://arxiv.org/abs/2102.08154v1

    • [cs.CL]Exploring Transformers in Natural Language Generation: GPT, BERT, and XLNet
    M. Onat Topal, Anil Bas, Imke van Heerden
    http://arxiv.org/abs/2102.08036v1

    • [cs.CL]FEWS: Large-Scale, Low-Shot Word Sense Disambiguation with the Dictionary
    Terra Blevins, Mandar Joshi, Luke Zettlemoyer
    http://arxiv.org/abs/2102.07983v1

    • [cs.CL]Fast End-to-End Speech Recognition via a Non-Autoregressive Model and Cross-Modal Knowledge Transferring from BERT
    Ye Bai, Jiangyan Yi, Jianhua Tao, Zhengkun Tian, Zhengqi Wen, Shuai Zhang
    http://arxiv.org/abs/2102.07594v2

    • [cs.CL]Have Attention Heads in BERT Learned Constituency Grammar?
    Ziyang Luo
    http://arxiv.org/abs/2102.07926v1

    • [cs.CL]Hierarchical Transformer-based Large-Context End-to-end ASR with Large-Context Knowledge Distillation
    Ryo Masumura, Naoki Makishima, Mana Ihori, Akihiko Takashima, Tomohiro Tanaka, Shota Orihashi
    http://arxiv.org/abs/2102.07935v1

    • [cs.CL]How COVID-19 Is Changing Our Language : Detecting Semantic Shift in Twitter Word Embeddings
    Yanzhu Guo, Christos Xypolopoulos, Michalis Vazirgiannis
    http://arxiv.org/abs/2102.07836v1

    • [cs.CL]Large-Context Conversational Representation Learning: Self-Supervised Learning for Conversational Documents
    Ryo Masumura, Naoki Makishima, Mana Ihori, Akihiko Takashima, Tomohiro Tanaka, Shota Orihashi
    http://arxiv.org/abs/2102.08147v1

    • [cs.CL]MAPGN: MAsked Pointer-Generator Network for sequence-to-sequence pre-training
    Mana Ihori, Naoki Makishima, Tomohiro Tanaka, Akihiko Takashima, Shota Orihashi, Ryo Masumura
    http://arxiv.org/abs/2102.07380v2

    • [cs.CL]Meta Back-translation
    Hieu Pham, Xinyi Wang, Yiming Yang, Graham Neubig
    http://arxiv.org/abs/2102.07847v1

    • [cs.CL]NoiseQA: Challenge Set Evaluation for User-Centric Question Answering
    Abhilasha Ravichander, Siddharth Dalmia, Maria Ryskina, Florian Metze, Eduard Hovy, Alan W Black
    http://arxiv.org/abs/2102.08345v1

    • [cs.CL]Non-Autoregressive Text Generation with Pre-trained Language Models
    Yixuan Su, Deng Cai, Yan Wang, David Vandyke, Simon Baker, Piji Li, Nigel Collier
    http://arxiv.org/abs/2102.08220v1

    • [cs.CL]Revisiting Language Encoding in Learning Multilingual Representations
    Shengjie Luo, Kaiyuan Gao, Shuxin Zheng, Guolin Ke, Di He, Liwei Wang, Tie-Yan Liu
    http://arxiv.org/abs/2102.08357v1

    • [cs.CR]Machine Learning Based Cyber Attacks Targeting on Controlled Information: A Survey
    Yuantian Miao, Chao Chen, Lei Pan, Qing-Long Han, Jun Zhang, Yang Xiang
    http://arxiv.org/abs/2102.07969v1

    • [cs.CR]Recent Developments in Blockchain Technology and their Impact on Energy Consumption
    Johannes Sedlmeir, Hans Ulrich Buhl, Gilbert Fridgen, Robert Keller
    http://arxiv.org/abs/2102.07886v1

    • [cs.CR]Temporal-Amount Snapshot MultiGraph for Ethereum Transaction Tracking
    Yunyi Xie, Jie Jin, Jian Zhang, Shanqing Yu, Qi Xuan
    http://arxiv.org/abs/2102.08013v1

    • [cs.CV]A Benchmark of Ocular Disease Intelligent Recognition: One Shot for Multi-disease Detection
    Ning Li, Tao Li, Chunyu Hu, Kai Wang, Hong Kang
    http://arxiv.org/abs/2102.07978v1

    • [cs.CV]A Multiscale Graph Convolutional Network for Change Detection in Homogeneous and Heterogeneous Remote Sensing Images
    Junzheng Wu, Biao Li, Yao Qin, Weiping Ni, Han Zhang, Yuli Sun
    http://arxiv.org/abs/2102.08041v1

    • [cs.CV]A comparative study on movement feature in different directions for micro-expression recognition
    Jinsheng Wei, Guanming Lu, Jingjie Yan
    http://arxiv.org/abs/2102.08068v1

    • [cs.CV]Accurate and Clear Precipitation Nowcasting with Consecutive Attention and Rain-map Discrimination
    Ashesh, Buo-Fu Chen, Treng-Shi Huang, Boyo Chen, Chia-Tung Chang, Hsuan-Tien Lin
    http://arxiv.org/abs/2102.08175v1

    • [cs.CV]Ada-SISE: Adaptive Semantic Input Sampling for Efficient Explanation of Convolutional Neural Networks
    Mahesh Sudhakar, Sam Sattarzadeh, Konstantinos N. Plataniotis, Jongseong Jang, Yeonjeong Jeong, Hyunwoo Kim
    http://arxiv.org/abs/2102.07799v1

    • [cs.CV]AlphaNet: Improved Training of Supernet with Alpha-Divergence
    Dilin Wang, Chengyue Gong, Meng Li, Qiang Liu, Vikas Chandra
    http://arxiv.org/abs/2102.07954v1

    • [cs.CV]Boosting Deep Transfer Learning for COVID-19 Classification
    Fouzia Altaf, Syed M. S. Islam, Naeem K. Janjua, Naveed Akhtar
    http://arxiv.org/abs/2102.08085v1

    • [cs.CV]Does deep machine vision have just noticeable difference (JND)?
    Jian Jin, Xingxing Zhang, Xin Fu, Huan Zhang, Weisi Lin, Jian Lou, Yao Zhao
    http://arxiv.org/abs/2102.08168v1

    • [cs.CV]EfficientLPS: Efficient LiDAR Panoptic Segmentation
    Kshitij Sirohi, Rohit Mohan, Daniel Büscher, Wolfram Burgard, Abhinav Valada
    http://arxiv.org/abs/2102.08009v1

    • [cs.CV]Feature Pyramid Network with Multi-Head Attention for Se-mantic Segmentation of Fine-Resolution Remotely Sensed Im-ages
    Rui Li, Shunyi Zheng, Chenxi Duan
    http://arxiv.org/abs/2102.07997v1

    • [cs.CV]Instance Localization for Self-supervised Detection Pretraining
    Ceyuan Yang, Zhirong Wu, Bolei Zhou, Stephen Lin
    http://arxiv.org/abs/2102.08318v1

    • [cs.CV]Integrated Grad-CAM: Sensitivity-Aware Visual Explanation of Deep Convolutional Networks via Integrated Gradient-Based Scoring
    Sam Sattarzadeh, Mahesh Sudhakar, Konstantinos N. Plataniotis, Jongseong Jang, Yeonjeong Jeong, Hyunwoo Kim
    http://arxiv.org/abs/2102.07805v1

    • [cs.CV]Just Noticeable Difference for Machine Perception and Generation of Regularized Adversarial Images with Minimal Perturbation
    Adil Kaan Akan, Emre Akbas, Fatos T. Yarman Vural
    http://arxiv.org/abs/2102.08079v1

    • [cs.CV]LEAD: LiDAR Extender for Autonomous Driving
    Jianing Zhang, Wei Li, Honggang Gou, Lu Fang, Ruigang Yang
    http://arxiv.org/abs/2102.07989v1

    • [cs.CV]Learning to Recognize Actions on Objects in Egocentric Video with Attention Dictionaries
    Swathikiran Sudhakaran, Sergio Escalera, Oswald Lanz
    http://arxiv.org/abs/2102.08065v1

    • [cs.CV]MITNet: GAN Enhanced Magnetic Induction Tomography Based on Complex CNN
    Zuohui Chen, Qing Yuan, Xujie Song, Cheng Chen, Dan Zhang, Yun Xiang, Ruigang Liu, Qi Xuan
    http://arxiv.org/abs/2102.07911v1

    • [cs.CV]Multi-Attribute Enhancement Network for Person Search
    Lequan Chen, Wei Xie, Zhigang Tu, Yaping Tao, Xinming Wang
    http://arxiv.org/abs/2102.07968v1

    • [cs.CV]PSA-Net: Deep Learning based Physician Style-Aware Segmentation Network for Post-Operative Prostate Cancer Clinical Target Volume
    Anjali Balagopal, Howard Morgan, Michael Dohopoloski, Ramsey Timmerman, Jie Shan, Daniel F. Heitjan, Wei Liu, Dan Nguyen, Raquibul Hannan, Aurelie Garant, Neil Desai, Steve Jiang
    http://arxiv.org/abs/2102.07880v1

    • [cs.CV]Reciprocal Distance Transform Maps for Crowd Counting and People Localization in Dense Crowd
    Dingkang Liang, Wei Xu, Yingying Zhu, Yu Zhou
    http://arxiv.org/abs/2102.07925v1

    • [cs.CV]Restore from Restored: Single-image Inpainting
    Eun Hye Lee, Jeong Mu Kim, Ji Su Kim, Tae Hyun Kim
    http://arxiv.org/abs/2102.08078v1

    • [cs.CV]Self-Supervised Features Improve Open-World Learning
    Akshay Raj Dhamija, Touqeer Ahmad, Jonathan Schwan, Mohsen Jafarzadeh, Chunchun Li, Terrance E. Boult
    http://arxiv.org/abs/2102.07848v1

    • [cs.CV]SiMaN: Sign-to-Magnitude Network Binarization
    Mingbao Lin, Rongrong Ji, Zihan Xu, Baochang Zhang, Fei Chao, Mingliang Xu, Chia-Wen Lin, Ling Shao
    http://arxiv.org/abs/2102.07981v1

    • [cs.CV]TransFuse: Fusing Transformers and CNNs for Medical Image Segmentation
    Yundong Zhang, Huiye Liu, Qiang Hu
    http://arxiv.org/abs/2102.08005v1

    • [cs.CV]Twin Augmented Architectures for Robust Classification of COVID-19 Chest X-Ray Images
    Kartikeya Badola, Sameer Ambekar, Himanshu Pant, Sumit Soman, Anuradha Sural, Rajiv Narang, Suresh Chandra, Jayadeva
    http://arxiv.org/abs/2102.07975v1

    • [cs.CV]Uncertainty-based method for improving poorly labeled segmentation datasets
    Ekaterina Redekop, Alexey Chernyavskiy
    http://arxiv.org/abs/2102.08021v1

    • [cs.CV]VA-RED今日学术视野(2021.2.18) - 图3: Video Adaptive Redundancy Reduction
    Bowen Pan, Rameswar Panda, Camilo Fosco, Chung-Ching Lin, Alex Andonian, Yue Meng, Kate Saenko, Aude Oliva, Rogerio Feris
    http://arxiv.org/abs/2102.07887v1

    • [cs.CY]A Mental Trespass? Unveiling Truth, Exposing Thoughts and Threatening Civil Liberties with Non-Invasive AI Lie Detection
    Taylan Sen, Kurtis Haut, Denis Lomakin, Ehsan Hoque
    http://arxiv.org/abs/2102.08004v1

    • [cs.CY]Conversations Gone Alright: Quantifying and Predicting Prosocial Outcomes in Online Conversations
    Jiajun Bao, Junjie Wu, Yiming Zhang, Eshwar Chandrasekharan, David Jurgens
    http://arxiv.org/abs/2102.08368v1

    • [cs.CY]Could you become more credible by being White? Assessing Impact of Race on Credibility with Deepfakes
    Kurtis Haut, Caleb Wohn, Victor Antony, Aidan Goldfarb, Melissa Welsh, Dillanie Sumanthiran, Ji-ze Jang, Md. Rafayet Ali, Ehsan Hoque
    http://arxiv.org/abs/2102.08054v1

    • [cs.CY]Spatio-Temporal Multi-step Prediction of Influenza Outbreaks
    Jie Zhang, Kazumitsu Nawata, Hongyan Wu
    http://arxiv.org/abs/2102.08137v1

    • [cs.CY]Towards an accountable Internet of Things: A call for ‘reviewability’
    Chris Norval, Jennifer Cobbe, Jatinder Singh
    http://arxiv.org/abs/2102.08132v1

    • [cs.DC]AdEle: An Adaptive Congestion-and-Energy-Aware Elevator Selection for Partially Connected 3D NoCs
    Ebadollah Taheri, Ryan G. Kim, Mahdi Nikdast
    http://arxiv.org/abs/2102.08323v1

    • [cs.DC]All You Need is DAG
    Idit Keidar, Eleftherios Kokoris-Kogias, Oded Naor, Alexander Spiegelman
    http://arxiv.org/abs/2102.08325v1

    • [cs.DC]Fast Validated Byzantine Broadcast
    Ittai Abraham, Kartik Nayak, Ling Ren, Zhuolun Xiang
    http://arxiv.org/abs/2102.07932v1

    • [cs.DS]Deterministic CONGEST Algorithm for MDS on Bounded Arboricity Graphs
    Saeed Akhoondian Amiri
    http://arxiv.org/abs/2102.08076v1

    • [cs.DS]Fair and Optimal Cohort Selection for Linear Utilities
    Konstantina Bairaktari, Huy Le Nguyen, Jonathan Ullman
    http://arxiv.org/abs/2102.07684v2

    • [cs.DS]Faster Kernel Matrix Algebra via Density Estimation
    Arturs Backurs, Piotr Indyk, Cameron Musco, Tal Wagner
    http://arxiv.org/abs/2102.08341v1

    • [cs.DS]Submodular Maximization subject to a Knapsack Constraint: Combinatorial Algorithms with Near-optimal Adaptive Complexity
    Georgios Amanatidis, Federico Fusco, Philip Lazos, Stefano Leonardi, Alberto Marchetti Spaccamela, Rebecca Reiffenhäuser
    http://arxiv.org/abs/2102.08327v1

    • [cs.GT]Follow-the-Regularizer-Leader Routes to Chaos in Routing Games
    Jakub Bielawski, Thiparat Chotibut, Fryderyk Falniowski, Grzegorz Kosiorowski, Michał Misiurewicz, Georgios Piliouras
    http://arxiv.org/abs/2102.07974v1

    • [cs.HC]“From What I see, this makes sense”: Seeing meaning in algorithmic results
    Samir Passi
    http://arxiv.org/abs/2102.07844v1

    • [cs.IR]KnowledgeCheckR: Intelligent Techniques for Counteracting Forgetting
    Martin Stettinger, Trang Tran, Ingo Pribik, Gerhard Leitner, Alexander Felfernig, Ralph Samer, Muesluem Atas, Manfred Wundara
    http://arxiv.org/abs/2102.07825v1

    • [cs.IR]NeuralNDCG: Direct Optimisation of a Ranking Metric via Differentiable Relaxation of Sorting
    Przemysław Pobrotyn, Radosław Białobrzeski
    http://arxiv.org/abs/2102.07831v1

    • [cs.IR]Recommender Systems for Configuration Knowledge Engineering
    Alexander Felfernig, Stefan Reiterer, Martin Stettinger, Florian Reinfrank, Michael Jeran, Gerald Ninaus
    http://arxiv.org/abs/2102.08113v1

    • [cs.IR]User Embedding based Neighborhood Aggregation Method for Inductive Recommendation
    Rahul Ragesh, Sundararajan Sellamanickam, Vijay Lingam, Arun Iyer, Ramakrishna Bairi
    http://arxiv.org/abs/2102.07575v2

    • [cs.IR]User-Inspired Posterior Network for Recommendation Reason Generation
    Haolan Zhan, Hainan Zhang, Hongshen Chen, Lei Shen, Yanyan Lan, Zhuoye Ding, Dawei Yin
    http://arxiv.org/abs/2102.07919v1

    • [cs.IT]Active Privacy-utility Trade-off Against a Hypothesis Testing Adversary
    Ecenaz Erdemir, Pier Luigi Dragotti, Deniz Gunduz
    http://arxiv.org/abs/2102.08308v1

    • [cs.IT]Belief Propagation List Ordered Statistics Decoding of Polar Codes
    Guy Mogilevsky, David Burshtein
    http://arxiv.org/abs/2102.07994v1

    • [cs.IT]Capacity-Achieving Private Information Retrieval Schemes from Uncoded Storage Constrained Servers with Low Sub-packetization
    Jinbao Zhu, Qifa Yan, Xiaohu Tang, Ying Miao
    http://arxiv.org/abs/2102.08058v1

    • [cs.IT]Channel Estimation and Hybrid Combining for Wideband Terahertz Massive MIMO Systems
    Konstantinos Dovelos, Michail Matthaiou, Hien Quoc Ngo, Boris Bellalta
    http://arxiv.org/abs/2102.06772v1

    • [cs.IT]Coupled-Channel Enhanced SSFM for Digital Backpropagation in WDM Systems
    S. Civelli, E. Forestieri, A. Lotsmanov, D. Razdoburdin, M. Secondini
    http://arxiv.org/abs/2102.08215v1

    • [cs.IT]Lower bound on Wyner’s Common Information
    Erixhen Sula, Michael Gastpar
    http://arxiv.org/abs/2102.08157v1

    • [cs.IT]Polar Codes for Automorphism Ensemble Decoding
    Charles Pillet, Valerio Bioglio, Ingmar Land
    http://arxiv.org/abs/2102.08250v1

    • [cs.IT]Sparse Channel Reconstruction With Nonconvex Regularizer via DC Programming for Massive MIMO Systems
    Pengxia Wu, Hui Ma, Julian Cheng
    http://arxiv.org/abs/2102.07803v1

    • [cs.IT]Speeding Up Private Distributed Matrix Multiplication via Bivariate Polynomial Codes
    Burak Hasircioglu, Jesus Gomez-Vilardebo, Deniz Gunduz
    http://arxiv.org/abs/2102.08304v1

    • [cs.LG]A Generic Descent Aggregation Framework for Gradient-based Bi-level Optimization
    Risheng Liu, Pan Mu, Xiaoming Yuan, Shangzhi Zeng, Jin Zhang
    http://arxiv.org/abs/2102.07976v1

    • [cs.LG]A Koopman Approach to Understanding Sequence Neural Models
    Ilan Naiman, Omri Azencot
    http://arxiv.org/abs/2102.07824v1

    • [cs.LG]A Sub-band Approach to Deep Denoising Wavelet Networks and a Frequency-adaptive Loss for Perceptual Quality
    Caglar Aytekin, Sakari Alenius, Dmytro Paliy, Juuso Gren
    http://arxiv.org/abs/2102.07973v1

    • [cs.LG]A Survey of Machine Learning for Computer Architecture and Systems
    Nan Wu, Yuan Xie
    http://arxiv.org/abs/2102.07952v1

    • [cs.LG]A Thorough View of Exact Inference in Graphs from the Degree-4 Sum-of-Squares Hierarchy
    Kevin Bello, Chuyang Ke, Jean Honorio
    http://arxiv.org/abs/2102.08019v1

    • [cs.LG]Adaptive Weighting Scheme for Automatic Time-Series Data Augmentation
    Elizabeth Fons, Paula Dawson, Xiao-jun Zeng, John Keane, Alexandros Iosifidis
    http://arxiv.org/abs/2102.08310v1

    • [cs.LG]Adversarial Targeted Forgetting in Regularization and Generative Based Continual Learning Models
    Muhammad Umer, Robi Polikar
    http://arxiv.org/abs/2102.08355v1

    • [cs.LG]An AutoML-based Approach to Multimodal Image Sentiment Analysis
    Vasco Lopes, António Gaspar, Luís A. Alexandre, João Cordeiro
    http://arxiv.org/abs/2102.08092v1

    • [cs.LG]Analysis of feature learning in weight-tied autoencoders via the mean field lens
    Phan-Minh Nguyen
    http://arxiv.org/abs/2102.08373v1

    • [cs.LG]COMBO: Conservative Offline Model-Based Policy Optimization
    Tianhe Yu, Aviral Kumar, Rafael Rafailov, Aravind Rajeswaran, Sergey Levine, Chelsea Finn
    http://arxiv.org/abs/2102.08363v1

    • [cs.LG]CTAB-GAN: Effective Table Data Synthesizing
    Zilong Zhao, Aditya Kunar, Hiek Van der Scheer, Robert Birke, Lydia Y. Chen
    http://arxiv.org/abs/2102.08369v1

    • [cs.LG]Certified Robustness to Programmable Transformations in LSTMs
    Yuhao Zhang, Aws Albarghouthi, Loris D’Antoni
    http://arxiv.org/abs/2102.07818v1

    • [cs.LG]Chickenpox Cases in Hungary: a Benchmark Dataset for Spatiotemporal Signal Processing with Graph Neural Networks
    Benedek Rozemberczki, Paul Scherer, Oliver Kiss, Rik Sarkar, Tamas Ferenci
    http://arxiv.org/abs/2102.08100v1

    • [cs.LG]Classification of multivariate weakly-labelled time-series with attention
    Surayez Rahman, Chang Wei Tan
    http://arxiv.org/abs/2102.08245v1

    • [cs.LG]Communication-Efficient Distributed Cooperative Learning with Compressed Beliefs
    Mohammad Taha Toghani, Cesar A. Uribe
    http://arxiv.org/abs/2102.07767v1

    • [cs.LG]Constructing Multiclass Classifiers using Binary Classifiers Under Log-Loss
    Assaf Ben-Yishai, Or Ordentlich
    http://arxiv.org/abs/2102.08184v1

    • [cs.LG]Dataset Condensation with Differentiable Siamese Augmentation
    Bo Zhao, Hakan Bilen
    http://arxiv.org/abs/2102.08259v1

    • [cs.LG]Differential Privacy and Byzantine Resilience in SGD: Do They Add Up?
    Rachid Guerraoui, Nirupam Gupta, Rafaël Pinot, Sébastien Rouault, John Stephan
    http://arxiv.org/abs/2102.08166v1

    • [cs.LG]Differentially Private Quantiles
    Jennifer Gillenwater, Matthew Joseph, Alex Kulesza
    http://arxiv.org/abs/2102.08244v1

    • [cs.LG]Distributionally-Constrained Policy Optimization via Unbalanced Optimal Transport
    Arash Givchi, Pei Wang, Junqi Wang, Patrick Shafto
    http://arxiv.org/abs/2102.07889v1

    • [cs.LG]Don’t Just Blame Over-parametrization for Over-confidence: Theoretical Analysis of Calibration in Binary Classification
    Yu Bai, Song Mei, Huan Wang, Caiming Xiong
    http://arxiv.org/abs/2102.07856v1

    • [cs.LG]EDITH :ECG biometrics aided by Deep learning for reliable Individual auTHentication
    Nabil Ibtehaz, Muhammad E. H. Chowdhury, Amith Khandakar, Serkan Kiranyaz, M. Sohel Rahman, Anas Tahir, Yazan Qiblawey, Tawsifur Rahman
    http://arxiv.org/abs/2102.08026v1

    • [cs.LG]EPE-NAS: Efficient Performance Estimation Without Training for Neural Architecture Search
    Vasco Lopes, Saeid Alirezazadeh, Luís A. Alexandre
    http://arxiv.org/abs/2102.08099v1

    • [cs.LG]Efficient Competitions and Online Learning with Strategic Forecasters
    Rafael Frongillo, Robert Gomez, Anish Thilagar, Bo Waggoner
    http://arxiv.org/abs/2102.08358v1

    • [cs.LG]Efficient Learning with Arbitrary Covariate Shift
    Adam Kalai, Varun Kanade
    http://arxiv.org/abs/2102.07802v1

    • [cs.LG]Few-Shot Graph Learning for Molecular Property Prediction
    Zhichun Guo, Chuxu Zhang, Wenhao Yu, John Herr, Olaf Wiest, Meng Jiang, Nitesh V. Chawla
    http://arxiv.org/abs/2102.07916v1

    • [cs.LG]GP-Tree: A Gaussian Process Classifier for Few-Shot Incremental Learning
    Idan Achituve, Aviv Navon, Yochai Yemini, Gal Chechik, Ethan Fetaya
    http://arxiv.org/abs/2102.07868v1

    • [cs.LG]GradInit: Learning to Initialize Neural Networks for Stable and Efficient Training
    Chen Zhu, Renkun Ni, Zheng Xu, Kezhi Kong, W. Ronny Huang, Tom Goldstein
    http://arxiv.org/abs/2102.08098v1

    • [cs.LG]HDMI: High-order Deep Multiplex Infomax
    Baoyu Jing, Chanyoung Park, Hanghang Tong
    http://arxiv.org/abs/2102.07810v1

    • [cs.LG]Hierarchical VAEs Know What They Don’t Know
    Jakob D. Havtorn, Jes Frellsen, Søren Hauberg, Lars Maaløe
    http://arxiv.org/abs/2102.08248v1

    • [cs.LG]How to Learn when Data Reacts to Your Model: Performative Gradient Descent
    Zachary Izzo, Lexing Ying, James Zou
    http://arxiv.org/abs/2102.07698v2

    • [cs.LG]Identifying Misinformation from Website Screenshots
    Sara Abdali, Rutuja Gurav, Siddharth Menon, Daniel Fonseca, Negin Entezari, Neil Shah, Evangelos E. Papalexakis
    http://arxiv.org/abs/2102.07849v1

    • [cs.LG]Improper Learning with Gradient-based Policy Optimization
    Mohammadi Zaki, Avinash Mohan, Aditya Gopalan, Shie Mannor
    http://arxiv.org/abs/2102.08201v1

    • [cs.LG]Improving Bayesian Inference in Deep Neural Networks with Variational Structured Dropout
    Son Nguyen, Duong Nguyen, Khai Nguyen, Nhat Ho, Khoat Than, Hung Bui
    http://arxiv.org/abs/2102.07927v1

    • [cs.LG]Improving the Accuracy Of MEPDG Climate Modeling Using Radial Basis Function
    Amirehsan Ghasemi, Kelvin J Msechu, Arash Ghasemi, Mbakisya A. Onyango, Ignatius Fomunung, Joseph Owino
    http://arxiv.org/abs/2102.07890v1

    • [cs.LG]IntSGD: Floatless Compression of Stochastic Gradients
    Konstantin Mishchenko, Bokun Wang, Dmitry Kovalev, Peter Richtárik
    http://arxiv.org/abs/2102.08374v1

    • [cs.LG]Integrating Floor Plans into Hedonic Models for Rent Price Appraisal
    Kirill Solovev, Nicolas Pröllochs
    http://arxiv.org/abs/2102.08162v1

    • [cs.LG]Inverse Reinforcement Learning in the Continuous Setting with Formal Guarantees
    Gregory Dexter, Kevin Bello, Jean Honorio
    http://arxiv.org/abs/2102.07937v1

    • [cs.LG]Joint self-supervised blind denoising and noise estimation
    Jean Ollion, Charles Ollion, Elisabeth Gassiat, Luc Lehéricy, Sylvain Le Corff
    http://arxiv.org/abs/2102.08023v1

    • [cs.LG]KNH: Multi-View Modeling with K-Nearest Hyperplanes Graph for Misinformation Detection
    Sara Abdali, Neil Shah, Evangelos E. Papalexakis
    http://arxiv.org/abs/2102.07857v1

    • [cs.LG]Learning Invariant Representations using Inverse Contrastive Loss
    Aditya Kumar Akash, Vishnu Suresh Lokhande, Sathya N. Ravi, Vikas Singh
    http://arxiv.org/abs/2102.08343v1

    • [cs.LG]Local Hyper-flow Diffusion
    Kimon Fountoulakis, Pan Li, Shenghao Yang
    http://arxiv.org/abs/2102.07945v1

    • [cs.LG]Low Curvature Activations Reduce Overfitting in Adversarial Training
    Vasu Singla, Sahil Singla, David Jacobs, Soheil Feizi
    http://arxiv.org/abs/2102.07861v1

    • [cs.LG]MARINA: Faster Non-Convex Distributed Learning with Compression
    Eduard Gorbunov, Konstantin Burlachenko, Zhize Li, Peter Richtárik
    http://arxiv.org/abs/2102.07845v1

    • [cs.LG]Maximizing Conditional Entropy for Batch-Mode Active Learning of Perceptual Metrics
    Priyadarshini Kumari, Sidhdhartha Chaudhuri, Vivek Borkar, Subhasis Chaudhuri
    http://arxiv.org/abs/2102.07365v2

    • [cs.LG]Message Passing Descent for Efficient Machine Learning
    Francesco Concetti, Michael Chertkov
    http://arxiv.org/abs/2102.08110v1

    • [cs.LG]Model-based Meta Reinforcement Learning using Graph Structured Surrogate Models
    Qi Wang, Herke van Hoof
    http://arxiv.org/abs/2102.08291v1

    • [cs.LG]Momentum Residual Neural Networks
    Michael E. Sander, Pierre Ablin, Mathieu Blondel, Gabriel Peyré
    http://arxiv.org/abs/2102.07870v1

    • [cs.LG]Offline Model-Based Optimization via Normalized Maximum Likelihood Estimation
    Justin Fu, Sergey Levine
    http://arxiv.org/abs/2102.07970v1

    • [cs.LG]One Line To Rule Them All: Generating LO-Shot Soft-Label Prototypes
    Ilia Sucholutsky, Nam-Hwui Kim, Ryan P. Browne, Matthias Schonlau
    http://arxiv.org/abs/2102.07834v1

    • [cs.LG]Online hyperparameter optimization by real-time recurrent learning
    Daniel Jiwoong Im, Cristina Savin, Kyunghyun Cho
    http://arxiv.org/abs/2102.07813v1

    • [cs.LG]Online learning of Riemannian hidden Markov models in homogeneous Hadamard spaces
    Quinten Tupker, Salem Said, Cyrus Mostajeran
    http://arxiv.org/abs/2102.07771v1

    • [cs.LG]Optimal Algorithms for Private Online Learning in a Stochastic Environment
    Bingshan Hu, Zhiming Huang, Nishant A. Mehta
    http://arxiv.org/abs/2102.07929v1

    • [cs.LG]Orthogonal Features-based EEG Signal Denoising using Fractionally Compressed AutoEncoder
    Subham Nagar, Ahlad Kumar, M. N. S. Swamy
    http://arxiv.org/abs/2102.08083v1

    • [cs.LG]Phase-Modulated Radar Waveform Classification Using Deep Networks
    Michael Wharton, Anne M. Pavy, Philip Schniter
    http://arxiv.org/abs/2102.07827v1

    • [cs.LG]Posterior-Aided Regularization for Likelihood-Free Inference
    Dongjun Kim, Kyungwoo Song, Seungjae Shin, Wanmo Kang, Il-Chul Moon
    http://arxiv.org/abs/2102.07770v1

    • [cs.LG]RMIX: Learning Risk-Sensitive Policies for Cooperative Reinforcement Learning Agents
    Wei Qiu, Xinrun Wang, Runsheng Yu, Xu He, Rundong Wang, Bo An, Svetlana Obraztsova, Zinovi Rabinovich
    http://arxiv.org/abs/2102.08159v1

    • [cs.LG]Scaling Up Exact Neural Network Compression by ReLU Stability
    Thiago Serra, Abhinav Kumar, Srikumar Ramalingam
    http://arxiv.org/abs/2102.07804v1

    • [cs.LG]Steadily Learn to Drive with Virtual Memory
    Yuhang Zhang, Yao Mu, Yujie Yang, Yang Guan, Shengbo Eben Li, Qi Sun, Jianyu Chen
    http://arxiv.org/abs/2102.08072v1

    • [cs.LG]Structured Graph Learning for Scalable Subspace Clustering: From Single-view to Multi-view
    Zhao Kang, Zhiping Lin, Xiaofeng Zhu, Wenbo Xu
    http://arxiv.org/abs/2102.07943v1

    • [cs.LG]Successive Pruning for Model Compression via Rate Distortion Theory
    Berivan Isik, Albert No, Tsachy Weissman
    http://arxiv.org/abs/2102.08329v1

    • [cs.LG]TeraPipe: Token-Level Pipeline Parallelism for Training Large-Scale Language Models
    Zhuohan Li, Shiyuan Guo, Danyang Zhuo, Hao Zhang, Dawn Song, Ion Stoica
    http://arxiv.org/abs/2102.07988v1

    • [cs.LG]Topological Deep Learning: Classification Neural Networks
    Mustafa Hajij, Kyle Istvan
    http://arxiv.org/abs/2102.08354v1

    • [cs.LG]Topological Graph Neural Networks
    Max Horn, Edward De Brouwer, Michael Moor, Yves Moreau, Bastian Rieck, Karsten Borgwardt
    http://arxiv.org/abs/2102.07835v1

    • [cs.LG]Training Larger Networks for Deep Reinforcement Learning
    Kei Ota, Devesh K. Jha, Asako Kanezaki
    http://arxiv.org/abs/2102.07920v1

    • [cs.LG]Training Stacked Denoising Autoencoders for Representation Learning
    Jason Liang, Keith Kelly
    http://arxiv.org/abs/2102.08012v1

    • [cs.LG]Unified Shapley Framework to Explain Prediction Drift
    Aalok Shanbhag, Avijit Ghosh, Josh Rubin
    http://arxiv.org/abs/2102.07862v1

    • [cs.LG]Unifying Lower Bounds on Prediction Dimension of Consistent Convex Surrogates
    Jessie Finocchiaro, Rafael Frongillo, Bo Waggoner
    http://arxiv.org/abs/2102.08218v1

    • [cs.LG]Using Data Assimilation to Train a Hybrid Forecast System that Combines Machine-Learning and Knowledge-Based Components
    Alexander Wikner, Jaideep Pathak, Brian R. Hunt, Istvan Szunyogh, Michelle Girvan, Edward Ott
    http://arxiv.org/abs/2102.07819v1

    • [cs.LG]Zero-Shot Adaptation for mmWave Beam-Tracking on Overhead Messenger Wires through Robust Adversarial Reinforcement Learning
    Masao Shinzaki, Yusuke Koda, Koji Yamamoto, Takayuki Nishio, Masahiro Morikura, Yushi Shirato, Daisei Uchida, Naoki Kita
    http://arxiv.org/abs/2102.08055v1

    • [cs.MA]DFAC Framework: Factorizing the Value Function via Quantile Mixture for Multi-Agent Distributional Q-Learning
    Wei-Fang Sun, Cheng-Kuang Lee, Chun-Yi Lee
    http://arxiv.org/abs/2102.07936v1

    • [cs.MA]Quantifying environment and population diversity in multi-agent reinforcement learning
    Kevin R. McKee, Joel Z. Leibo, Charlie Beattie, Richard Everett
    http://arxiv.org/abs/2102.08370v1

    • [cs.NE]A Federated Data-Driven Evolutionary Algorithm
    Jinjin Xu, Yaochu Jin, Wenli Du, Sai Gu
    http://arxiv.org/abs/2102.08288v1

    • [cs.NI]Automated Identification of Vulnerable Devices in Networks using Traffic Data and Deep Learning
    Jakob Greis, Artem Yushchenko, Daniel Vogel, Michael Meier, Volker Steinhage
    http://arxiv.org/abs/2102.08199v1

    • [cs.NI]Federated Learning over Wireless Networks: A Band-limited Coordinated Descent Approach
    Junshan Zhang, Na Li, Mehmet Dedeoglu
    http://arxiv.org/abs/2102.07972v1

    • [cs.RO]Composing Pick-and-Place Tasks By Grounding Language
    Oier Mees, Wolfram Burgard
    http://arxiv.org/abs/2102.08094v1

    • [cs.RO]Darboux-Frame-Based Parametrization for a Spin-Rolling Sphere on a Plane: A Nonlinear Transformation of Underactuated System to Fully-Actuated Model
    Seyed Amir Tafrishi, Mikhail Svinin, Motoji Yamamoto
    http://arxiv.org/abs/2102.07923v1

    • [cs.RO]Design Iterations for Passive Aerial Manipulator
    Vidyadhara
    1000
    B V, Lima Agnel Tony, Mohitvishnu S. Gadde, Shuvrangshu Jana, Varun V. P., Aashay Anil Bhise, Suresh Sundaram, Debasish Ghose
    http://arxiv.org/abs/2102.08306v1

    • [cs.RO]Graph-based Motion Planning for Automated Vehicles using Multi-model Branching and Admissible Heuristics
    Oliver Speidel, Jona Ruof, Klaus Dietmayer
    http://arxiv.org/abs/2102.07812v1

    • [cs.RO]Hough2Map — Iterative Event-based Hough Transform for High-Speed Railway Mapping
    Florian Tschopp, Cornelius von Einem, Andrei Cramariuc, David Hug, Andrew William Palmer, Roland Siegwart, Margarita Chli, Juan Nieto
    http://arxiv.org/abs/2102.08145v1

    • [cs.RO]Learning the Noise of Failure: Intelligent System Tests for Robots
    Felix Sygulla, Daniel Rixen
    http://arxiv.org/abs/2102.08080v1

    • [cs.RO]Optimal Mixed Discrete-Continuous Planningfor Linear Hybrid Systems
    Jingkai Chen, Brian Williams, Chuchu Fan
    http://arxiv.org/abs/2102.08261v1

    • [cs.RO]Probabilistic Localization of Insect-Scale Drones on Floating-Gate Inverter Arrays
    Priyesh Shukla, Ankith Muralidhar, Nick Iliev, Theja Tulabandhula, Sawyer B. Fuller, Amit Ranjan Trivedi
    http://arxiv.org/abs/2102.08247v1

    • [cs.RO]Supervised Training of Dense Object Nets using Optimal Descriptors for Industrial Robotic Applications
    Andras Kupcsik, Markus Spies, Alexander Klein, Marco Todescato, Nicolai Waniek, Philipp Schillinger, Mathias Buerger
    http://arxiv.org/abs/2102.08096v1

    • [cs.SD]Anomalous Sound Detection with Machine Learning: A Systematic Review
    Eduardo C. Nunes
    http://arxiv.org/abs/2102.07820v1

    • [cs.SD]Improving Deep-learning-based Semi-supervised Audio Tagging with Mixup
    Léo Cances, Etienne Labbé, Thomas Pellegrini
    http://arxiv.org/abs/2102.08183v1

    • [cs.SD]Improving speech recognition models with small samples for air traffic control systems
    Yi Lin, Qin Li, Bo Yang, Zhen Yan, Huachun Tan, Zhengmao Chen
    http://arxiv.org/abs/2102.08015v1

    • [cs.SD]Semi Supervised Learning For Few-shot Audio Classification By Episodic Triplet Mining
    Swapnil Bhosale, Rupayan Chakraborty, Sunil Kumar Kopparapu
    http://arxiv.org/abs/2102.08074v1

    • [cs.SE]D2A: A Dataset Built for AI-Based Vulnerability Detection Methods Using Differential Analysis
    Yunhui Zheng, Saurabh Pujar, Burn Lewis, Luca Buratti, Edward Epstein, Bo Yang, Jim Laredo, Alessandro Morari, Zhong Su
    http://arxiv.org/abs/2102.07995v1

    • [cs.SI]A Hidden Challenge of Link Prediction: Which Pairs to Check?
    Caleb Belth, Alican Büyükçakır, Danai Koutra
    http://arxiv.org/abs/2102.07878v1

    • [cs.SI]Deciphering Social Opinion Polarization Towards Political Event Based on Content and Structural Analysis
    Andry Alamsyah, Wachda Yuniar Rochmah, Arina Nahya Nurnafia
    http://arxiv.org/abs/2102.08249v1

    • [cs.SI]Empirical Characterization of Graph Sampling Algorithms
    Muhammad Irfan Yousuf, Izza Anwer, Raheel Anwar
    http://arxiv.org/abs/2102.07980v1

    • [cs.SI]Evaluating Node Embeddings of Complex Networks
    Arash Dehghan-Kooshkghazi, Bogumił Kamiński, Łukasz Kraiński, Paweł Prałat, François Théberge
    http://arxiv.org/abs/2102.08275v1

    • [cs.SI]Meta-Path-Free Representation Learning on Heterogeneous Networks
    Jie Zhang, Jinru Ding, Suyuan Liu, Hongyan Wu
    http://arxiv.org/abs/2102.08120v1

    • [cs.SI]User Characteristics of Olympic Gold Medallists on Instagram: A Quantitative Analysis of Rio2016
    Amirhosein Bodaghi
    http://arxiv.org/abs/2102.08271v1

    • [econ.GN]An Effort to Measure Customer Relationship Performance in Indonesia’s Fintech Industry
    Alisya Putri Rabbani, Andry Alamsyah, Sri Widiyanesti
    http://arxiv.org/abs/2102.08262v1

    • [econ.GN]Interdependencies between Mining Costs, Mining Rewards and Blockchain Security
    Pavel Ciaian, d’Artis Kancs, Miroslava Rajcaniova
    http://arxiv.org/abs/2102.08107v1

    • [econ.GN]Supportive 5G infrastructure policies are essential for universal 6G: Evidence from an open-source techno-economic simulation model using remote sensing
    Edward J. Oughton, Ashutosh Jha
    http://arxiv.org/abs/2102.08086v1

    • [eess.AS]Axial Residual Networks for CycleGAN-based Voice Conversion
    Jaeseong You, Gyuhyeon Nam, Dalhyun Kim, Gyeongsu Chae
    http://arxiv.org/abs/2102.08075v1

    • [eess.AS]Context-Aware Prosody Correction for Text-Based Speech Editing
    Max Morrison, Lucas Rencker, Zeyu Jin, Nicholas J. Bryan, Juan-Pablo Caceres, Bryan Pardo
    http://arxiv.org/abs/2102.08328v1

    • [eess.AS]PeriodNet: A non-autoregressive waveform generation model with a structure separating periodic and aperiodic components
    Yukiya Hono, Shinji Takaki, Kei Hashimoto, Keiichiro Oura, Yoshihiko Nankaku, Keiichi Tokuda
    http://arxiv.org/abs/2102.07786v1

    • [eess.IV]A Deep-Learning Approach For Direct Whole-Heart Mesh Reconstruction
    Fanwei Kong, Nathan Wilson, Shawn C. Shadden
    http://arxiv.org/abs/2102.07899v1

    • [eess.IV]Deep Equilibrium Architectures for Inverse Problems in Imaging
    Davis Gilton, Gregory Ongie, Rebecca Willett
    http://arxiv.org/abs/2102.07944v1

    • [eess.IV]Going Beyond Saliency Maps: Training Deep Models to Interpret Deep Models
    Zixuan Liu, Ehsan Adeli, Kilian M. Pohl, Qingyu Zhao
    http://arxiv.org/abs/2102.08239v1

    • [eess.IV]On Mathews Correlation Coefficient and Improved Distance Map Loss for Automatic Glacier Calving Front Segmentation in SAR Imagery
    Amirabbas Davari, Saahil Islam, Thorsten Seehaus, Matthias Braun, Andreas Maier, Vincent Christlein
    http://arxiv.org/abs/2102.08312v1

    • [eess.SP]Enhancing the Spatio-temporal Observability of Grid-Edge Resources in Distribution Grids
    Shanny Lin, Hao Zhu
    http://arxiv.org/abs/2102.07801v1

    • [eess.SP]On the use of generative deep neural networks to synthesize artificial multichannel EEG signals
    Ozan Ozdenizci, Deniz Erdogmus
    http://arxiv.org/abs/2102.08061v1

    • [math.CO]Designs in finite metric spaces: a probabilistic approach
    Minjia Shi, Olivier Rioul, Patrick Solé
    http://arxiv.org/abs/2102.08276v1

    • [math.OC]Decentralized Distributed Optimization for Saddle Point Problems
    Alexander Rogozin, Alexander Beznosikov, Darina Dvinskikh, Dmitry Kovalev, Pavel Dvurechensky, Alexander Gasnikov
    http://arxiv.org/abs/2102.07758v2

    • [math.OC]Efficient Discretizations of Optimal Transport
    Junqi Wang, Pei Wang, Patrick Shafto
    http://arxiv.org/abs/2102.07956v1

    • [math.OC]Learning Symbolic Expressions: Mixed-Integer Formulations, Cuts, and Heuristics
    Jongeun Kim, Sven Leyffer, Prasanna Balaprakash
    http://arxiv.org/abs/2102.08351v1

    • [math.OC]Stochastic Variance Reduction for Variational Inequality Methods
    Ahmet Alacaoglu, Yura Malitsky
    http://arxiv.org/abs/2102.08352v1

    • [math.PR]Concentration of measure and generalized product ofrandom vectors with an application to Hanson-Wright-like inequalities
    Cosme Louart, Romain Couillet
    http://arxiv.org/abs/2102.08020v1

    • [math.ST]Distribution-Free Conditional Median Inference
    Dhruv Medarametla, Emmanuel J. Candès
    http://arxiv.org/abs/2102.07967v1

    • [math.ST]Exponential confidence interval based on the recursive Wolverton-Wagner density estimation
    M. R. Formica, E. Ostrovsky, L. Sirota
    http://arxiv.org/abs/2102.07867v1

    • [math.ST]Signed variable optimal kernel for non-parametric density estimation
    M. R. Formica, E. Ostrovsky, L. Sirota
    http://arxiv.org/abs/2102.07858v1

    • [math.ST]Tight Risk Bound for High Dimensional Time Series Completion
    Pierre Alquier, Nicolas Marie, Amélie Rosier
    http://arxiv.org/abs/2102.08178v1

    • [math.ST]Unbiased simulation of rare events in continuous time
    James Hodgson, Adam M. Johansen, Murray Pollock
    http://arxiv.org/abs/2102.08057v1

    • [physics.med-ph]Corneal Pachymetry by AS-OCT after Descemet’s Membrane Endothelial Keratoplasty
    Friso G. Heslinga, Ruben T. Lucassen, Myrthe A. van den Berg, Luuk van der Hoek, Josien P. W. Pluim, Javier Cabrerizo, Mark Alberti, Mitko Veta
    http://arxiv.org/abs/2102.07846v1

    • [physics.med-ph]DAN-Net: Dual-Domain Adaptive-Scaling Non-local Network for CT Metal Artifact Reduction
    Tao Wang, Wenjun Xia, Yongqiang Huang, Huaiqiang Sun, Yan Liu, Hu Chen, Jiliu Zhou, Yi Zhang
    http://arxiv.org/abs/2102.08003v1

    • [q-bio.PE]A stochastic SIR model for the analysis of the COVID-19 Italian epidemic
    Sara Pasquali, Antonio Pievatolo, Antonella Bodini, Fabrizio Ruggeri
    http://arxiv.org/abs/2102.07566v2

    • [q-fin.ST]On Technical Trading and Social Media Indicators in Cryptocurrencies’ Price Classification Through Deep Learning
    Marco Ortu, Nicola Uras, Claudio Conversano, Giuseppe Destefanis, Silvia Bartolucci
    http://arxiv.org/abs/2102.08189v1

    • [quant-ph]Universal Adversarial Examples and Perturbations for Quantum Classifiers
    Weiyuan Gong, Dong-Ling Deng
    http://arxiv.org/abs/2102.07788v1

    • [stat.AP]Simple statistical models and sequential deep learning for Lithium-ion batteries degradation under dynamic conditions: Fractional Polynomials vs Neural Networks
    Clara B. Salucci, Azzeddine Bakdi, Ingrid K. Glad, Erik Vanem, Riccardo De Bin
    http://arxiv.org/abs/2102.08111v1

    • [stat.ME]A Bayesian Framework for Generation of Fully Synthetic Mixed Datasets
    Joseph Feldman, Daniel Kowal
    http://arxiv.org/abs/2102.08255v1

    • [stat.ME]A New Method to Determine the Presence of Continuous Variation in Parameters of Biological Growth Curve Models
    Md Aktar Ul Karim, Supriya Ramdas Bhagat, Amiya Ranjan Bhowmick
    http://arxiv.org/abs/2102.07992v1

    • [stat.ME]Controlling False Discovery Rates Using Null Bootstrapping
    Junpei Komiyama, Masaya Abe, Kei Nakagawa, Kenichiro McAlinn
    http://arxiv.org/abs/2102.07826v1

    • [stat.ME]The DeCAMFounder: Non-Linear Causal Discovery in the Presence of Hidden Variables
    Raj Agrawal, Chandler Squires, Neha Prasad, Caroline Uhler
    http://arxiv.org/abs/2102.07921v1

    • [stat.ME]The MELODIC family for simultaneous binary logistic regression in a reduced space
    Mark de Rooij, Patrick J. F. Groenen
    http://arxiv.org/abs/2102.08232v1

    • [stat.ME]Trees-Based Models for Correlated Data
    Assaf Rabinowicz, Saharon Rosset
    http://arxiv.org/abs/2102.08114v1

    • [stat.ML]A Law of Robustness for Weight-bounded Neural Networks
    Hisham Husain, Borja Balle
    http://arxiv.org/abs/2102.08093v1

    • [stat.ML]Capturing the learning curves of generic features maps for realistic data sets with a teacher-student model
    Bruno Loureiro, Cédric Gerbelot, Hugo Cui, Sebastian Goldt, Florent Krzakala, Marc Mézard, Lenka Zdeborová
    http://arxiv.org/abs/2102.08127v1

    • [stat.ML]Conditional Distributional Treatment Effect with Kernel Conditional Mean Embeddings and U-Statistic Regression
    Junhyung Park, Uri Shalit, Bernhard Schölkopf, Krikamol Muandet
    http://arxiv.org/abs/2102.08208v1

    • [stat.ML]Differentiable Particle Filtering via Entropy-Regularized Optimal Transport
    Adrien Corenflos, James Thornton, Arnaud Doucet, George Deligiannidis
    http://arxiv.org/abs/2102.07850v1

    • [stat.ML]Double-descent curves in neural networks: a new perspective using Gaussian processes
    Ouns El Harzli, Guillermo Valle-Pérez, Ard A. Louis
    http://arxiv.org/abs/2102.07238v2

    • [stat.ML]Making the most of your day: online learning for optimal allocation of time
    Etienne Boursier, Tristan Garrec, Vianney Perchet, Marco Scarsini
    http://arxiv.org/abs/2102.08087v1

    • [stat.ML]The Randomized Elliptical Potential Lemma with an Application to Linear Thompson Sampling
    Nima Hamidi, Mohsen Bayati
    http://arxiv.org/abs/2102.07987v1

    • [stat.ML]Tighter Bounds on the Log Marginal Likelihood of Gaussian Process Regression Using Conjugate Gradients
    Artem Artemev, David R. Burt, Mark van der Wilk
    http://arxiv.org/abs/2102.08314v1

    • [stat.ML]Top-今日学术视野(2021.2.18) - 图4 eXtreme Contextual Bandits with Arm Hierarchy
    Rajat Sen, Alexander Rakhlin, Lexing Ying, Rahul Kidambi, Dean Foster, Daniel Hill, Inderjit Dhillon
    http://arxiv.org/abs/2102.07800v1