astro-ph.EP - 地球与行星天体

    astro-ph.IM - 仪器仪表和天体物理学方法 cond-mat.mtrl-sci - 材料科学 cs.AI - 人工智能 cs.CL - 计算与语言 cs.CR - 加密与安全 cs.CV - 机器视觉与模式识别 cs.CY - 计算与社会 cs.DB - 数据库 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.NE - 神经与进化计算 cs.RO - 机器人学 cs.SI - 社交网络与信息网络 eess.AS - 语音处理 eess.IV - 图像与视频处理 eess.SP - 信号处理 math.OC - 优化与控制 math.PR - 概率 math.ST - 统计理论 nlin.PS -模式形成与孤子 physics.med-ph - 医学物理学 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

    • [astro-ph.EP]A Bayesian neural network predicts the dissolution of compact planetary systems
    • [astro-ph.IM]Estimating Galactic Distances From Images Using Self-supervised Representation Learning
    • [cond-mat.mtrl-sci]Interpretable discovery of new semiconductors with machine learning
    • [cs.AI]A Brief Survey of Associations Between Meta-Learning and General AI
    • [cs.AI]Dimensions of Commonsense Knowledge
    • [cs.AI]First-Order Problem Solving through Neural MCTS based Reinforcement Learning
    • [cs.AI]Quantum Mathematics in Artificial Intelligence
    • [cs.AI]Solving Common-Payoff Games with Approximate Policy Iteration
    • [cs.CL]A character representation enhanced on-device Intent Classification
    • [cs.CL]AI- and HPC-enabled Lead Generation for SARS-CoV-2: Models and Processes to Extract Druglike Molecules Contained in Natural Language Text
    • [cs.CL]AT-BERT: Adversarial Training BERT for Acronym Identification Winning Solution for SDU@AAAI-21
    • [cs.CL]BERT-GT: Cross-sentence n-ary relation extraction with BERT and Graph Transformer
    • [cs.CL]Clustering Word Embeddings with Self-Organizing Maps. Application on LaRoSeDa — A Large Romanian Sentiment Data Set
    • [cs.CL]Evaluation of Deep Learning Models for Hostility Detection in Hindi Text
    • [cs.CL]Explain and Predict, and then Predict again
    • [cs.CL]Implicit Unlikelihood Training: Improving Neural Text Generation with Reinforcement Learning
    • [cs.CL]Neural Contract Element Extraction Revisited
    • [cs.CL]Of Non-Linearity and Commutativity in BERT
    • [cs.CL]Quantum Cognitively Motivated Decision Fusion for Video Sentiment Analysis
    • [cs.CL]Toward Effective Automated Content Analysis via Crowdsourcing
    • [cs.CL]Transforming Multi-Conditioned Generation from Meaning Representation
    • [cs.CR]DeepiSign: Invisible Fragile Watermark to Protect the Integrityand Authenticity of CNN
    • [cs.CR]On the Practicality of Differential Privacy in Federated Learning by Tuning Iteration Times
    • [cs.CR]Online rating system development using blockchain-based distributed ledger technology
    • [cs.CR]Sharing pandemic vaccination certificates through blockchain: Case study and performance evaluation
    • [cs.CV]3D-ANAS: 3D Asymmetric Neural Architecture Search for Fast Hyperspectral Image Classification
    • [cs.CV]A Multimodal Eye Movement Dataset and a Multimodal Eye Movement Segmentation Analysis
    • [cs.CV]Automated Detection of Patellofemoral Osteoarthritis from Knee Lateral View Radiographs Using Deep Learning: Data from the Multicenter Osteoarthritis Study (MOST)
    • [cs.CV]CleftNet: Augmented Deep Learning for Synaptic Cleft Detection from Brain Electron Microscopy
    • [cs.CV]Context Matters: Self-Attention for Sign Language Recognition
    • [cs.CV]Enhanced Information Fusion Network for Crowd Counting
    • [cs.CV]Explaining the Black-box Smoothly- A Counterfactual Approach
    • [cs.CV]FaceX-Zoo: A PyTorh Toolbox for Face Recognition
    • [cs.CV]Fine-grained Semantic Constraint in Image Synthesis
    • [cs.CV]Joint Demosaicking and Denoising in the Wild: The Case of Training Under Ground Truth Uncertainty
    • [cs.CV]LLA: Loss-aware Label Assignment for Dense Pedestrian Detection
    • [cs.CV]Lesion2Vec: Deep Metric Learning for Few Shot Multiple Lesions Recognition in Wireless Capsule Endoscopy
    • [cs.CV]Mixup Without Hesitation
    • [cs.CV]Multimodal Engagement Analysis from Facial Videos in the Classroom
    • [cs.CV]Pneumonia Detection on Chest X-ray using Radiomic Features and Contrastive Learning
    • [cs.CV]Predicting Relative Depth between Objects from Semantic Features
    • [cs.CV]PvDeConv: Point-Voxel Deconvolution for Autoencoding CAD Construction in 3D
    • [cs.CV]Random Transformation of Image Brightness for Adversarial Attack
    • [cs.CV]Resolution invariant person reid based on feature transformation and self-weighted attention
    • [cs.CV]Rethinking Interactive Image Segmentation: Feature Space Annotation
    • [cs.CV]Superpixel-based Refinement for Object Proposal Generation
    • [cs.CV]Take More Positives: A Contrastive Learning Framework for Unsupervised Person Re-Identification
    • [cs.CV]Temporally Guided Articulated Hand Pose Tracking in Surgical Videos
    • [cs.CV]The Vulnerability of Semantic Segmentation Networks to Adversarial Attacks in Autonomous Driving: Enhancing Extensive Environment Sensing
    • [cs.CV]TrackMPNN: A Message Passing Graph Neural Architecture for Multi-Object Tracking
    • [cs.CV]Two-stage CNN-based wood log recognition
    • [cs.CV]UFA-FUSE: A novel deep supervised and hybrid model for multi-focus image fusion
    • [cs.CV]Unchain the Search Space with Hierarchical Differentiable Architecture Search
    • [cs.CV]Where you live matters: a spatial analysis of COVID-19 mortality
    • [cs.CY]Capturing social media expressions during the COVID-19 pandemic in Argentina and forecasting mental health and emotions
    • [cs.CY]The audiovisual resource as a pedagogical tools in times of covid 19. An empirical analysis of its efficiency
    • [cs.DB]DBTagger: Multi-Task Learning for Keyword Mapping in NLIDBs Using Bi-Directional Recurrent Neural Networks
    • [cs.DC]Panorama: A Framework to Support Collaborative Context Monitoring on Co-Located Mobile Devices
    • [cs.DC]Symbolic Loop Compilation for Tightly Coupled Processor Arrays
    • [cs.DC]Time and Communication Complexity of Leader Election in Anonymous Networks
    • [cs.DC]Towards a Performance Model for Byzantine Fault Tolerant (Storage) Services
    • [cs.DS]Locality Sensitive Hashing for Efficient Similar Polygon Retrieval
    • [cs.GT]From Learning with Partial Information to Bandits: Only Strict Nash Equilibria are Stable
    • [cs.HC]Fits and Starts: Enterprise Use of AutoML and the Role of Humans in the Loop
    • [cs.HC]The Hidden Cost of Using Amazon Mechanical Turk for Research
    • [cs.IR]Neural News Recommendation with Negative Feedback
    • [cs.IR]On the Calibration and Uncertainty of Neural Learning to Rank Models
    • [cs.IT]An Early-Stopping Mechanism for DSCF Decoding of Polar Codes
    • [cs.IT]CAnet: Uplink-aided Downlink Channel Acquisition in FDD Massive MIMO using Deep Learning
    • [cs.IT]Dynamic Spectrum Access using Stochastic Multi-User Bandits
    • [cs.IT]Event-Driven Source Traffic Prediction in Machine-Type Communications Using LSTM Networks
    • [cs.LG]A Unified Framework for Online Trip Destination Prediction
    • [cs.LG]A deep learning modeling framework to capture mixing patterns in reactive-transport systems
    • [cs.LG]Activation Density based Mixed-Precision Quantization for Energy Efficient Neural Networks
    • [cs.LG]Adversary Instantiation: Lower Bounds for Differentially Private Machine Learning
    • [cs.LG]Automated Respiratory Event Detection Using Deep Neural Networks
    • [cs.LG]Blind Modulation Classification via Combined Machine Learning and Signal Feature Extraction
    • [cs.LG]Challenges and approaches to time-series forecasting in data center telemetry: A Survey
    • [cs.LG]Continental-scale streamflow modeling of basins with reservoirs: a demonstration of effectiveness and a delineation of challenges
    • [cs.LG]Developing an OpenAI Gym-compatible framework and simulation environment for testing Deep Reinforcement Learning agents solving the Ambulance Location Problem
    • [cs.LG]Double-Adversarial Activation Anomaly Detection: Adversarial Autoencoders are Anomaly Generators
    • [cs.LG]Explainable De
    1000
    ep Behavioral Sequence Clustering for Transaction Fraud Detection
    • [cs.LG]Exploiting Multiple Timescales in Hierarchical Echo State Networks
    • [cs.LG]From Tinkering to Engineering: Measurements in Tensorflow Playground
    • [cs.LG]HighAir: A Hierarchical Graph Neural Network-Based Air Quality Forecasting Method
    • [cs.LG]Hyperbolic Deep Neural Networks: A Survey
    • [cs.LG]Independent Policy Gradient Methods for Competitive Reinforcement Learning
    • [cs.LG]Learning Intuitive Physics with Multimodal Generative Models
    • [cs.LG]Learning with Comparison Feedback: Online Estimation of Sample Statistics
    • [cs.LG]Machine Learning for Initial Value Problems of Parameter-Dependent Dynamical Systems
    • [cs.LG]Measuring Recommender System Effects with Simulated Users
    • [cs.LG]On the Convergence of Deep Networks with Sample Quadratic Overparameterization
    • [cs.LG]PEng4NN: An Accurate Performance Estimation Engine for Efficient Automated Neural Network Architecture Search
    • [cs.LG]Phase Retrieval using Expectation Consistent Signal Recovery Algorithm based on Hypernetwork
    • [cs.LG]Proceedings of the NeurIPS 2020 Workshop on Machine Learning for the Developing World: Improving Resilience
    • [cs.LG]PyHealth: A Python Library for Health Predictive Models
    • [cs.LG]Queue-Learning: A Reinforcement Learning Approach for Providing Quality of Service
    • [cs.LG]Reliable Fleet Analytics for Edge IoT Solutions
    • [cs.LG]Robustness of on-device Models: Adversarial Attack to Deep Learning Models on Android Apps
    • [cs.LG]Seed Stocking Via Multi-Task Learning
    • [cs.LG]Sound Event Detection with Binary Neural Networks on Tightly Power-Constrained IoT Devices
    • [cs.LG]Type4Py: Deep Similarity Learning-Based Type Inference for Python
    • [cs.NE]An Evolutionary Game Model for Understanding Fraud in Consumption Taxes
    • [cs.RO]A Robotic System for Implant Modification in Single-stage Cranioplasty
    • [cs.RO]Action Priors for Large Action Spaces in Robotics
    • [cs.RO]Automatic Extrinsic Calibration Method for LiDAR and Camera Sensor Setups
    • [cs.RO]Clutter Slices Approach for Identification-on-the-fly of Indoor Spaces
    • [cs.RO]Ergodic Exploration using Tensor Train: Applications in Insertion Tasks
    • [cs.RO]Regret Analysis of Distributed Gaussian Process Estimation and Coverage
    • [cs.SI]Locating highly connected clusters in large networks with HyperLogLog counters
    • [eess.AS]Neural Network-based Virtual Microphone Estimator
    • [eess.IV]Deep Gaussian Denoiser Epistemic Uncertainty and Decoupled Dual-Attention Fusion
    • [eess.IV]Resolution-Based Distillation for Efficient Histology Image Classification
    • [eess.IV]Using uncertainty estimation to reduce false positives in liver lesion detection
    • [eess.SP]Fast Randomized-MUSIC for mm-Wave Massive MIMO Radars
    • [eess.SP]Implementation of OpenAirInterface-based real-world channel measurement for evaluating wireless transmission algorithms
    • [math.OC]Trace Ratio Optimization with an Application to Multi-view Learning
    • [math.PR]A note on a confidence bound of Kuzborskij and Szepesvári
    • [math.PR]Estimating the probability that a given vector is in the convex hull of a random sample
    • [math.PR]Perturbations of copulas and Mixing properties
    • [math.ST]A new method for constructing continuous distributions on the unit interval
    • [math.ST]Bayesian inference in high-dimensional models
    • [math.ST]General Hannan and Quinn Criterion for Common Time Series
    • [math.ST]Sharp detection boundaries on testing dense subhypergraph
    • [math.ST]The Beta-Mixture Shrinkage Prior for Sparse Covariances with Posterior Minimax Rates
    • [nlin.PS]Data-driven peakon and periodic peakon travelling wave solutions of some nonlinear dispersive equations via deep learning
    • [physics.med-ph]A patient-specific approach for quantitative and automatic analysis of computed tomography images in lung disease: application to COVID-19 patients
    • [quant-ph]Quantum Consensus: an overview
    • [stat.AP]Change-point detection using spectral PCA for multivariate time series
    • [stat.AP]The Study of Urban Residential’s Public Space Activeness using Space-centric Approach
    • [stat.ME]BDNNSurv: Bayesian deep neural networks for survival analysis using pseudo values
    • [stat.ME]Bayesian equation selection on sparse data for discovery of stochastic dynamical systems
    • [stat.ME]Evaluation of Logistic Regression Applied to Respondent-Driven Samples: Simulated and Real Data
    • [stat.ME]Flexible Validity Conditions for the Multivariate Matérn Covariance in any Spatial Dimension and for any Number of Components
    • [stat.ME]High-Dimensional Low-Rank Tensor Autoregressive Time Series Modeling
    • [stat.ME]Mode Hunting Using Pettiest Components Analysis
    • [stat.ME]Moving sum data segmentation for stochastics processes based on invariance
    • [stat.ME]New Bias Calibration for Robust Estimation in Small Areas
    • [stat.ME]Penalized regression calibration: a method for the prediction of survival outcomes using complex longitudinal and high-dimensional data
    • [stat.ME]Statistical analysis of periodic data in neuroscience
    • [stat.ME]Weighted Approach for Estimating Effects in Principal Strata with Missing Data for a Categorical Post-Baseline Variable in Randomized Controlled Trials
    • [stat.ML]Benchmarking Simulation-Based Inference
    • [stat.ML]Data augmentation and feature selection for automatic model recommendation in computational physics

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    • [astro-ph.EP]A Bayesian neural network predicts the dissolution of compact planetary systems
    Miles Cranmer, Daniel Tamayo, Hanno Rein, Peter Battaglia, Samuel Hadden, Philip J. Armitage, Shirley Ho, David N. Spergel
    http://arxiv.org/abs/2101.04117v1

    • [astro-ph.IM]Estimating Galactic Distances From Images Using Self-supervised Representation Learning
    Md Abul Hayat, Peter Harrington, George Stein, Zarija Lukić, Mustafa Mustafa
    http://arxiv.org/abs/2101.04293v1

    • [cond-mat.mtrl-sci]Interpretable discovery of new semiconductors with machine learning
    Hitarth Choubisa, Petar Todorović, Joao M. Pina, Darshan H. Parmar, Ziliang Li, Oleksandr Voznyy, Isaac Tamblyn, Edward Sargent
    http://arxiv.org/abs/2101.04383v1

    • [cs.AI]A Brief Survey of Associations Between Meta-Learning and General AI
    Huimin Peng
    http://arxiv.org/abs/2101.04283v1

    • [cs.AI]Dimensions of Commonsense Knowledge
    Filip Ilievski, Alessandro Oltramari, Kaixin Ma, Bin Zhang, Deborah L. McGuinness, Pedro Szekely
    http://arxiv.org/abs/2101.04640v1

    • [cs.AI]First-Order Problem Solving through Neural MCTS based Reinforcement Learning
    Ruiyang Xu, Prashank Kadam, Karl Lieberherr
    http://arxiv.org/abs/2101.04167v1

    • [cs.AI]Quantum Mathematics in Artificial Intelligence
    Dominic Widdows, Kirsty Kitto, Trevor Cohen
    http://arxiv.org/abs/2101.04255v1

    • [cs.AI]Solving Common-Payoff Games with Approximate Policy Iteration
    Samuel Sokota, Edward Lockhart, Finbarr Timbers, Elnaz Davoodi, Ryan D’Orazio, Neil Burch, Martin Schmid, Michael Bowling, Marc Lanctot
    http://arxiv.org/abs/2101.04237v1

    • [cs.CL]A character representation enhanced on-device Intent Classification
    Sudeep Deepak Shivnikar, Himanshu Arora, Harichandana B S S
    http://arxiv.org/abs/2101.04456v1

    • [cs.CL]AI- and HPC-enabled Lead Generation for SARS-CoV-2: Models and Processes to Extract Druglike Molecules Contained in Natural Language Text
    Zhi Hong, J. Gregory Pauloski, Logan Ward, Kyle Chard, Ben Blaiszik, Ian Foster
    http://arxiv.org/abs/2101.04617v1

    • [cs.CL]AT-BERT: Adversarial Training BERT for Acronym Identification Winning Solution for SDU@AAAI-21
    Danqing Zhu, Wangli Lin, Yang Zhang, Qiwei Zhong, Guanxiong Zeng, Weilin Wu, Jiayu Tang
    http://arxiv.org/abs/2101.03700v2

    • [cs.CL]BERT-GT: Cross-sentence n-ary relation extraction with BERT and Graph Transformer
    Po-Ting Lai, Zhiyong Lu
    http://arxiv.org/abs/2101.04158v1

    • [cs.CL]Clustering Word Embeddings with Self-Organizing Maps. Application on LaRoSeDa — A Large Romanian Sentiment Data Set
    Anca Maria Tache, Mihaela Gaman, Radu Tudor Ionescu
    http://arxiv.org/abs/2101.04197v1

    • [cs.CL]Evaluation of Deep Learning Models for Hostility Detection in Hindi Text
    Ramchandra Joshi, Rushabh Karnavat, Kaustubh Jirapure, Raviraj Joshi
    http://arxiv.org/abs/2101.04144v1

    • [cs.CL]Explain and Predict, and then Predict again
    Zijian Zhang, Koustav Rudra, Avishek Anand
    http://arxiv.org/abs/2101.04109v1

    • [cs.CL]Implicit Unlikelihood Training: Improving Neural Text Generation with Reinforcement Learning
    Evgeny Lagutin, Daniil Gavrilov, Pavel Kalaidin
    http://arxiv.org/abs/2101.04229v1

    • [cs.CL]Neural Contract Element Extraction Revisited
    Ilias Chalkidis, Manos Fergadiotis, Prodromos Malakasiotis, Ion Androutsopoulos
    http://arxiv.org/abs/2101.04355v1

    • [cs.CL]Of Non-Linearity and Commutativity in BERT
    Sumu Zhao, Damian Pascual, Gino Brunner, Roger Wattenhofer
    http://arxiv.org/abs/2101.04547v1

    • [cs.CL]Quantum Cognitively Motivated Decision Fusion for Video Sentiment Analysis
    Dimitris Gkoumas, Qiuchi Li, Shahram Dehdashti, Massimo Melucci, Yijun Yu, Dawei Song
    http://arxiv.org/abs/2101.04406v1

    • [cs.CL]Toward Effective Automated Content Analysis via Crowdsourcing
    Jiele Wu, Chau-Wai Wong, Xinyan Zhao, Xianpeng Liu
    http://arxiv.org/abs/2101.04615v1

    • [cs.CL]Transforming Multi-Conditioned Generation from Meaning Representation
    Joosung Lee
    http://arxiv.org/abs/2101.04257v1

    • [cs.CR]DeepiSign: Invisible Fragile Watermark to Protect the Integrityand Authenticity of CNN
    Alsharif Abuadbba, Hyoungshick Kim, Surya Nepal
    http://arxiv.org/abs/2101.04319v1

    • [cs.CR]On the Practicality of Differential Privacy in Federated Learning by Tuning Iteration Times
    Yao Fu, Yipeng Zhou, Di Wu, Shui Yu, Yonggang Wen, Chao Li
    http://arxiv.org/abs/2101.04163v1

    • [cs.CR]Online rating system development using blockchain-based distributed ledger technology
    Monir Shaker, Fereidoon Shams Aliee, Reza Fotohi
    http://arxiv.org/abs/2101.04173v1

    • [cs.CR]Sharing pandemic vaccination certificates through blockchain: Case study and performance evaluation
    José Luis Hernández-Ramos, Georgios Karopoulos, Dimitris Geneiatakis, Tania Martin, Georgios Kambourakis, Igor Nai Fovino
    http://arxiv.org/abs/2101.04575v1

    • [cs.CV]3D-ANAS: 3D Asymmetric Neural Architecture Search for Fast Hyperspectral Image Classification
    Haokui Zhang, Chengrong Gong, Yunpeng Bai, Zongwen Bai, Ying Li
    http://arxiv.org/abs/2101.04287v1

    • [cs.CV]A Multimodal Eye Movement Dataset and a Multimodal Eye Movement Segmentation Analysis
    Wolfgang Fuhl, Enkelejda Kasneci
    http://arxiv.org/abs/2101.04318v1

    • [cs.CV]Automated Detection of Patellofemoral Osteoarthritis from Knee Lateral View Radiographs Using Deep Learning: Data from the Multicenter Osteoarthritis Study (MOST)
    Neslihan Bayramoglu, Miika T. Nieminen, Simo Saarakkala
    http://arxiv.org/abs/2101.04350v1

    • [cs.CV]CleftNet: Augmented Deep Learning for Synaptic Cleft Detection from Brain Electron Microscopy
    Yi Liu, Shuiwang Ji
    http://arxiv.org/abs/2101.04266v1

    • [cs.CV]Context Matters: Self-Attention for Sign Language Recognition
    Fares Ben Slimane, Mohamed Bouguessa
    http://arxiv.org/abs/2101.04632v1

    • [cs.CV]Enhanced Information Fusion Network for Crowd Counting
    Geng Chen, Peirong Guo
    http://arxiv.org/abs/2101.04279v1

    • [cs.CV]Explaining the Black-box Smoothly- A Counterfactual Approach
    Sumedha Singla, Brian Pollack, Stephen Wallace, Kayhan Batmanghelich
    http://arxiv.org/abs/2101.04230v1

    • [cs.CV]FaceX-Zoo: A PyTorh Toolbox for Face Recognition
    Jun Wang, Yinglu Liu, Yibo Hu, Hailin Shi, Tao Mei
    http://arxiv.org/abs/2101.04407v1

    • [cs.CV]Fine-grained Semantic Constraint in Image Synthesis
    Pengyang Li, Donghui Wang
    http://arxiv.org/abs/2101.04558v1

    • [cs.CV]Joint Demosaicking and Denoising in the Wild: The Case of Training Under Ground Truth Uncertainty
    Jierun Chen, Song Wen, S. -H. Gary Chan
    http://arxiv.org/abs/2101.04442v1

    • [cs.CV]LLA: Loss-aware Label Assignment for Dense Pedestrian Detection
    Zheng Ge, Jianfeng Wang, Xin Huang, Songtao Liu, Osamu Yoshie
    http://arxiv.org/abs/2101.04307v1

    • [cs.CV]Lesion2Vec: Deep Metric Learning for Few Shot Multiple Lesions Recognition in Wireless Capsule Endoscopy
    Sodiq Adewole, Philip Fernandez, James Jablonski, Sana Syed, Andrew Copland, Michael Porter, Donald Brown
    http://arxiv.org/abs/2101.04240v1

    • [cs.CV]Mixup Without Hesitation
    Hao Yu, Huanyu Wang, Jianxin Wu
    http://arxiv.org/abs/2101.04342v1

    • [cs.CV]Multimodal Engagement Analysis from Facial Videos in the Classroom
    Ömer Sümer, Patricia Goldberg, Sidney D’Mello, Peter Gerjets, Ulrich Trautwein, Enkelejda Kasneci
    http://arxiv.org/abs/2101.04215v1

    • [cs.CV]Pneumonia Detection on Chest X-ray using Radiomic Features and Contrastive Learning
    Yan Han, Chongyan Chen, Ahmed H Tewfik, Ying Ding, Yifan Peng
    http://arxiv.org/abs/2101.04269v1

    • [cs.CV]Predicting Relative Depth between Objects from Semantic Features
    Stefan Cassar, Adrian Muscat, Dylan Seychell
    http://arxiv.org/abs/2101.04626v1

    • [cs.CV]PvDeConv: Point-Voxel Deconvolution for Autoencoding CAD Construction in 3D
    Kseniya Cherenkova, Djamila Aouada, Gleb Gusev
    http://arxiv.org/abs/2101.04493v1

    • [cs.CV]Random Transformation of Image Brightness for Adversarial Attack
    Bo Yang, Kaiyong Xu, Hengjun Wang, Hengwei Zhang
    http://arxiv.org/abs/2101.04321v1

    • [cs.CV]Resolution invariant person reid based on feature transformation and self-weighted attention
    Ziyue Zhang, Shuai Jiang, Congzhentao Huang, Richard Yi Da Xu
    http://arxiv.org/abs/2101.04544v1

    • [cs.CV]Rethinking Interactive Image Segmentation: Feature Space Annotation
    Jordão Bragantini, Alexandre Falcão, Laurent Najman
    http://arxiv.org/abs/2101.04378v1

    • [cs.CV]Superpixel-based Refinement for Object Proposal Generation
    Christian Wilms, Simone Frintrop
    http://arxiv.org/abs/2101.04574v1

    • [cs.CV]Take More Positives: A Contrastive Learning Framework for Unsupervised Person Re-Identification
    Xuanyu He, Wei Zhang, Ran Song, Xiangyuan Lan
    http://arxiv.org/abs/2101.04340v1

    • [cs.CV]Temporally Guided Articulated Hand Pose Tracking in Surgical Videos
    Nathan Louis, Luowei Zhou, Steven J. Yule, Roger D. Dias, Milisa Manojlovich, Francis D. Pagani, Donald S. Likosky, Jason J. Corso
    http://arxiv.org/abs/2101.04281v1

    • [cs.CV]The Vulnerability of Semantic Segmentation Networks to Adversarial Attacks in Autonomous Driving: Enhancing Extensive Environment Sensing
    Andreas Bär, Jonas Löhdefink, Nikhil Kapoor, Serin J. Varghese, Fabian Hüger, Peter Schlicht, Tim Fingscheidt
    http://arxiv.org/abs/2101.039
    1266
    24v1
    1266
    24v1)

    • [cs.CV]TrackMPNN: A Message Passing Graph Neural Architecture for Multi-Object Tracking
    Akshay Rangesh, Pranav Maheshwari, Mez Gebre, Siddhesh Mhatre, Vahid Ramezani, Mohan M. Trivedi
    http://arxiv.org/abs/2101.04206v1

    • [cs.CV]Two-stage CNN-based wood log recognition
    Georg Wimmer, Rudolf Schraml, Heinz Hofbauer, Alexander Petutschnigg, Andreas Uhl
    http://arxiv.org/abs/2101.04450v1

    • [cs.CV]UFA-FUSE: A novel deep supervised and hybrid model for multi-focus image fusion
    Yongsheng Zang, Dongming Zhou, Changcheng Wang, Rencan Nie, Yanbu Guo
    http://arxiv.org/abs/2101.04506v1

    • [cs.CV]Unchain the Search Space with Hierarchical Differentiable Architecture Search
    Guanting Liu, Yujie Zhong, Sheng Guo, Matthew R. Scott, Weilin Huang
    http://arxiv.org/abs/2101.04028v2

    • [cs.CV]Where you live matters: a spatial analysis of COVID-19 mortality
    Behzad Javaheri
    http://arxiv.org/abs/2101.04199v1

    • [cs.CY]Capturing social media expressions during the COVID-19 pandemic in Argentina and forecasting mental health and emotions
    Antonela Tommasel, Andres Diaz-Pace, Juan Manuel Rodriguez, Daniela Godoy
    http://arxiv.org/abs/2101.04540v1

    • [cs.CY]The audiovisual resource as a pedagogical tools in times of covid 19. An empirical analysis of its efficiency
    Juan Rodriguez Basignana, Carolina Asuaga
    http://arxiv.org/abs/2101.04569v1

    • [cs.DB]DBTagger: Multi-Task Learning for Keyword Mapping in NLIDBs Using Bi-Directional Recurrent Neural Networks
    Arif Usta, Akifhan Karakayali, Özgür Ulusoy
    http://arxiv.org/abs/2101.04226v1

    • [cs.DC]Panorama: A Framework to Support Collaborative Context Monitoring on Co-Located Mobile Devices
    Khaled Alanezi, Xinyang Zhou, Lijun Chen, Shivakant Mishra
    http://arxiv.org/abs/2101.04335v1

    • [cs.DC]Symbolic Loop Compilation for Tightly Coupled Processor Arrays
    Michael Witterauf, Dominik Walter, Frank Hannig, Jürgen Teich
    http://arxiv.org/abs/2101.04395v1

    • [cs.DC]Time and Communication Complexity of Leader Election in Anonymous Networks
    Dariusz R. Kowalski, Miguel A. Mosteiro
    http://arxiv.org/abs/2101.04400v1

    • [cs.DC]Towards a Performance Model for Byzantine Fault Tolerant (Storage) Services
    Thomas Loruenser, Benjamin Rainer, Florian Wohner
    http://arxiv.org/abs/2101.04489v1

    • [cs.DS]Locality Sensitive Hashing for Efficient Similar Polygon Retrieval
    Haim Kaplan, Jay Tenenbaum
    http://arxiv.org/abs/2101.04339v1

    • [cs.GT]From Learning with Partial Information to Bandits: Only Strict Nash Equilibria are Stable
    Angeliki Giannou, Emmanouil-Vasileios Vlatakis-Gkaragkounis, Panayotis Mertikopoulos
    http://arxiv.org/abs/2101.04667v1

    • [cs.HC]Fits and Starts: Enterprise Use of AutoML and the Role of Humans in the Loop
    Anamaria Crisan, Brittany Fiore-Gartland
    http://arxiv.org/abs/2101.04296v1

    • [cs.HC]The Hidden Cost of Using Amazon Mechanical Turk for Research
    Antonios Saravanos, Stavros Zervoudakis, Dongnanzi Zheng, Neil Stott, Bohdan Hawryluk, Donatella Delfino
    http://arxiv.org/abs/2101.04459v1

    • [cs.IR]Neural News Recommendation with Negative Feedback
    Chuhan Wu, Fangzhao Wu, Yongfeng Huang, Xing Xie
    http://arxiv.org/abs/2101.04328v1

    • [cs.IR]On the Calibration and Uncertainty of Neural Learning to Rank Models
    Gustavo Penha, Claudia Hauff
    http://arxiv.org/abs/2101.04356v1

    • [cs.IT]An Early-Stopping Mechanism for DSCF Decoding of Polar Codes
    Ilshat Sagitov, Pascal Giard
    http://arxiv.org/abs/2101.04586v1

    • [cs.IT]CAnet: Uplink-aided Downlink Channel Acquisition in FDD Massive MIMO using Deep Learning
    Jiajia Guo, Chao-Kai Wen, Shi Jin
    http://arxiv.org/abs/2101.04377v1

    • [cs.IT]Dynamic Spectrum Access using Stochastic Multi-User Bandits
    Meghana Bande, Akshayaa Magesh, Venugopal V. Veeravalli
    http://arxiv.org/abs/2101.04388v1

    • [cs.IT]Event-Driven Source Traffic Prediction in Machine-Type Communications Using LSTM Networks
    Thulitha Senevirathna, Bathiya Thennakoon, Tharindu Sankalpa, Chatura Seneviratne, Samad Ali, Nandana Rajatheva
    http://arxiv.org/abs/2101.04365v1

    • [cs.LG]A Unified Framework for Online Trip Destination Prediction
    Victor Eberstein, Jonas Sjöblom, Nikolce Murgovski, Morteza Haghir Chehreghani
    http://arxiv.org/abs/2101.04520v1

    • [cs.LG]A deep learning modeling framework to capture mixing patterns in reactive-transport systems
    N. V. Jagtap, M. K. Mudunuru, K. B. Nakshatrala
    http://arxiv.org/abs/2101.04227v1

    • [cs.LG]Activation Density based Mixed-Precision Quantization for Energy Efficient Neural Networks
    Karina Vasquez, Yeshwanth Venkatesha, Abhiroop Bhattacharjee, Abhishek Moitra, Priyadarshini Panda
    http://arxiv.org/abs/2101.04354v1

    • [cs.LG]Adversary Instantiation: Lower Bounds for Differentially Private Machine Learning
    Milad Nasr, Shuang Song, Abhradeep Thakurta, Nicolas Papernot, Nicholas Carlini
    http://arxiv.org/abs/2101.04535v1

    • [cs.LG]Automated Respiratory Event Detection Using Deep Neural Networks
    Thijs E Nassi, Wolfgang Ganglberger, Haoqi Sun, Abigail A Bucklin, Siddharth Biswal, Michel J A M van Putten, Robert J Thomas, M Brandon Westover
    http://arxiv.org/abs/2101.04635v1

    • [cs.LG]Blind Modulation Classification via Combined Machine Learning and Signal Feature Extraction
    Jafar Norolahi, Paeiz Azmi
    http://arxiv.org/abs/2101.04337v1

    • [cs.LG]Challenges and approaches to time-series forecasting in data center telemetry: A Survey
    Shruti Jadon, Jan Kanty Milczek, Ajit Patnakar
    http://arxiv.org/abs/2101.04224v1

    • [cs.LG]Continental-scale streamflow modeling of basins with reservoirs: a demonstration of effectiveness and a delineation of challenges
    Wenyu Ouyang, Kathryn Lawson, Dapeng Feng, Lei Ye, Chi Zhang, Chaopeng Shen
    http://arxiv.org/abs/2101.04423v1

    • [cs.LG]Developing an OpenAI Gym-compatible framework and simulation environment for testing Deep Reinforcement Learning agents solving the Ambulance Location Problem
    Michael Allen, Kerry Pearn, Tom Monks
    http://arxiv.org/abs/2101.04434v1

    • [cs.LG]Double-Adversarial Activation Anomaly Detection: Adversarial Autoencoders are Anomaly Generators
    J. -P. Schulze, P. Sperl, K. Böttinger
    http://arxiv.org/abs/2101.04645v1

    • [cs.LG]Explainable De
    1000
    ep Behavioral Sequence Clustering for Transaction Fraud Detection
    Wei Min, Weiming Liang, Hang Yin, Zhurong Wang, Mei Li, Alok Lal
    http://arxiv.org/abs/2101.04285v1

    • [cs.LG]Exploiting Multiple Timescales in Hierarchical Echo State Networks
    Luca Manneschi, Matt O. A. Ellis, Guido Gigante, Andrew C. Lin, Paolo Del Giudice, Eleni Vasilaki
    http://arxiv.org/abs/2101.04223v1

    • [cs.LG]From Tinkering to Engineering: Measurements in Tensorflow Playground
    Henrik Hoeiness, Axel Harstad, Gerald Friedland
    http://arxiv.org/abs/2101.04141v1

    • [cs.LG]HighAir: A Hierarchical Graph Neural Network-Based Air Quality Forecasting Method
    Jiahui Xu, Ling Chen, Mingqi Lv, Chaoqun Zhan, Sanjian Chen, Jian Chang
    http://arxiv.org/abs/2101.04264v1

    • [cs.LG]Hyperbolic Deep Neural Networks: A Survey
    Wei Peng, Tuomas Varanka, Abdelrahman Mostafa, Henglin Shi, Guoying Zhao
    http://arxiv.org/abs/2101.04562v1

    • [cs.LG]Independent Policy Gradient Methods for Competitive Reinforcement Learning
    Constantinos Daskalakis, Dylan J. Foster, Noah Golowich
    http://arxiv.org/abs/2101.04233v1

    • [cs.LG]Learning Intuitive Physics with Multimodal Generative Models
    Sahand Rezaei-Shoshtari, Francois Robert Hogan, Michael Jenkin, David Meger, Gregory Dudek
    http://arxiv.org/abs/2101.04454v1

    • [cs.LG]Learning with Comparison Feedback: Online Estimation of Sample Statistics
    Michela Meister, Sloan Nietert
    http://arxiv.org/abs/2101.04176v1

    • [cs.LG]Machine Learning for Initial Value Problems of Parameter-Dependent Dynamical Systems
    Roland Pulch, Maha Youssef
    http://arxiv.org/abs/2101.04595v1

    • [cs.LG]Measuring Recommender System Effects with Simulated Users
    Sirui Yao, Yoni Halpern, Nithum Thain, Xuezhi Wang, Kang Lee, Flavien Prost, Ed H. Chi, Jilin Chen, Alex Beutel
    http://arxiv.org/abs/2101.04526v1

    • [cs.LG]On the Convergence of Deep Networks with Sample Quadratic Overparameterization
    Asaf Noy, Yi Xu, Yonathan Aflalo, Rong Jin
    http://arxiv.org/abs/2101.04243v1

    • [cs.LG]PEng4NN: An Accurate Performance Estimation Engine for Efficient Automated Neural Network Architecture Search
    Ariel Keller Rorabaugh, Silvina Caíno-Lores, Michael R. Wyatt II, Travis Johnston, Michela Taufer
    http://arxiv.org/abs/2101.04185v1

    • [cs.LG]Phase Retrieval using Expectation Consistent Signal Recovery Algorithm based on Hypernetwork
    Chang-Jen Wang, Chao-Kai Wen, Shang-Ho, Tsai, Shi Jin, Geoffrey Ye Li
    http://arxiv.org/abs/2101.04348v1

    • [cs.LG]Proceedings of the NeurIPS 2020 Workshop on Machine Learning for the Developing World: Improving Resilience
    Tejumade Afonja, Konstantin Klemmer, Aya Salama, Paula Rodriguez Diaz, Niveditha Kalavakonda, Oluwafemi Azeez
    http://arxiv.org/abs/2101.04347v1

    • [cs.LG]PyHealth: A Python Library for Health Predictive Models
    Yue Zhao, Zhi Qiao, Cao Xiao, Lucas Glass, Jimeng Sun
    http://arxiv.org/abs/2101.04209v1

    • [cs.LG]Queue-Learning: A Reinforcement Learning Approach for Providing Quality of Service
    Majid Raeis, Ali Tizghadam, Alberto Leon-Garcia
    http://arxiv.org/abs/2101.04627v1

    • [cs.LG]Reliable Fleet Analytics for Edge IoT Solutions
    Emmanuel Raj, Magnus Westerlund, Leonardo Espinosa-Leal
    http://arxiv.org/abs/2101.04414v1

    • [cs.LG]Robustness of on-device Models: Adversarial Attack to Deep Learning Models on Android Apps
    Yujin Huang, Han Hu, Chunyang Chen
    http://arxiv.org/abs/2101.04401v1

    • [cs.LG]Seed Stocking Via Multi-Task Learning
    Yunhe Feng, Wenjun Zhou
    http://arxiv.org/abs/2101.04333v1

    • [cs.LG]Sound Event Detection with Binary Neural Networks on Tightly Power-Constrained IoT Devices
    Gianmarco Cerutti, Renzo Andri, Lukas Cavigelli, Michele Magno, Elisabetta Farella, Luca Benini
    http://arxiv.org/abs/2101.04446v1

    • [cs.LG]Type4Py: Deep Similarity Learning-Based Type Inference for Python
    Amir M. Mir, Evaldas Latoskinas, Sebastian Proksch, Georgios Gousios
    http://arxiv.org/abs/2101.04470v1

    • [cs.NE]An Evolutionary Game Model for Understanding Fraud in Consumption Taxes
    M. Chica, J. Hernandez, C. Manrique-de-Lara-Peñate, R. Chiong
    http://arxiv.org/abs/2101.04424v1

    • [cs.RO]A Robotic System for Implant Modification in Single-stage Cranioplasty
    Shuya Liu, Wei-Lun Huang, Chad Gordon, Mehran Armand
    http://arxiv.org/abs/2101.04303v1

    • [cs.RO]Action Priors for Large Action Spaces in Robotics
    Ondrej Biza, Dian Wang, Robert Platt, Jan-Willem van de Meent, Lawson L. S. Wong
    http://arxiv.org/abs/2101.04178v1

    • [cs.RO]Automatic Extrinsic Calibration Method for LiDAR and Camera Sensor Setups
    Jorge Beltrán, Carlos Guindel, Fernando García
    http://arxiv.org/abs/2101.04431v1

    • [cs.RO]Clutter Slices Approach for Identification-on-the-fly of Indoor Spaces
    Upinder Kaur, Praveen Abbaraju, Harrison McCarty, Richard M. Voyles
    http://arxiv.org/abs/2101.04262v1

    • [cs.RO]Ergodic Exploration using Tensor Train: Applications in Insertion Tasks
    Suhan Shetty, João Silvério, Sylvain Calinon
    http://arxiv.org/abs/2101.04428v1

    • [cs.RO]Regret Analysis of Distributed Gaussian Process Estimation and Coverage
    Lai Wei, Andrew McDonald, Vaibhav Srivastava
    http://arxiv.org/abs/2101.04306v1

    • [cs.SI]Locating highly connected clusters in large networks with HyperLogLog counters
    Lotte Weedage, Nelly Litvak, Clara Stegehuis
    http://arxiv.org/abs/2101.04610v1

    • [eess.AS]Neural Network-based Virtual Microphone Estimator
    Tsubasa Ochiai, Marc Delcroix, Tomohiro Nakatani, Rintaro Ikeshita, Keisuke Kinoshita, Shoko Araki
    http://arxiv.org/abs/2101.04315v1

    • [eess.IV]Deep Gaussian Denoiser Epistemic Uncertainty and Decoupled Dual-Attention Fusion
    Xiaoqi Ma, Xiaoyu Lin, Majed El Helou, Sabine Süsstrunk
    http://arxiv.org/abs/2101.04631v1

    • [eess.IV]Resolution-Based Distillation for Efficient Histology Image Classification
    Joseph DiPalma, Arief A. Suriawinata, Laura J. Tafe, Lorenzo Torresani, Saeed Hassanpour
    http://arxiv.org/abs/2101.04170v1

    • [eess.IV]Using uncertainty estimation to reduce false positives in liver lesion detection
    Ishaan Bhat, Hugo J. Kuif, Veronika Cheplygina, Josien P. W. Pluim
    http://arxiv.org/abs/2101.04386v1

    • [eess.SP]Fast Randomized-MUSIC for mm-Wave Massive MIMO Radars
    Li Bin, Wang Shuseng, Zhang Jun, Cao Xianbin, Zhao Chenglin
    http://arxiv.org/abs/2101.04570v1

    • [eess.SP]Implementation of OpenAirInterface-based real-world channel measurement for evaluating wireless transmission algorithms
    Qiuheng Zhou, Wei Jiang
    http://arxiv.org/abs/2101.04608v1

    • [math.OC]Trace Ratio Optimization with an Application to Multi-view Learning
    Li Wang, Lei-Hong Zhang, Ren-Cang Li
    http://arxiv.org/abs/2101.04292v1

    • [math.PR]A note on a confidence bound of Kuzborskij and Szepesvári
    Omar Rivasplata
    http://arxiv.org/abs/2101.04671v1

    • [math.PR]Estimating the probability that a given vector is in the convex hull of a random sample
    Satoshi Hayakawa, Terry Lyons, Harald Oberhauser
    http://arxiv.org/abs/2101.04250v1

    • [math.PR]Perturbations of copulas and Mixing properties
    Martial Longla, Fidel Djongreba, Patrice Takam Soh, Mathias Muia Nthiani
    http://arxiv.org/abs/2101.04573v1

    • [math.ST]A new method for constructing continuous distributions on the unit interval
    Aniket Biswas, Subrata Chakraborty
    http://arxiv.org/abs/2101.04661v1

    • [math.ST]Bayesian inference in high-dimensional models
    Sayantan Banerjee, Ismaël Castillo, Subhashis Ghosal
    http://arxiv.org/abs/2101.04491v1

    • [math.ST]General Hannan and Quinn Criterion for Common Time Series
    Kare Kamila
    http://arxiv.org/abs/2101.04210v1

    • [math.ST]Sharp detection boundaries on testing dense subhypergraph
    Mingao Yuan, Zuofeng Shang
    http://arxiv.org/abs/2101.04584v1

    • [math.ST]The Beta-Mixture Shrinkage Prior for Sparse Covariances with Posterior Minimax Rates
    Kyoungjae Lee, Seongil Jo, Jaeyong Lee
    http://arxiv.org/abs/2101.04351v1

    • [nlin.PS]Data-driven peakon and periodic peakon travelling wave solutions of some nonlinear dispersive equations via deep learning
    Li Wang, Zhenya Yan
    http://arxiv.org/abs/2101.04371v1

    • [physics.med-ph]A patient-specific approach for quantitative and automatic analysis of computed tomography images in lung disease: application to COVID-19 patients
    L. Berta, C. De Mattia, F. Rizzetto, S. Carrazza, P. E. Colombo, R. Fumagalli, T. Langer, D. Lizio, A. Vanzulli, A. Torresin
    http://arxiv.org/abs/2101.04430v1

    • [quant-ph]Quantum Consensus: an overview
    Marco Marcozzi, Leonardo Mostarda
    http://arxiv.org/abs/2101.04192v1

    • [stat.AP]Change-point detection using spectral PCA for multivariate time series
    Shuhao Jiao, Tong Shen, Zhaoxia Yu, Hernando Ombao
    http://arxiv.org/abs/2101.04334v1

    • [stat.AP]The Study of Urban Residential’s Public Space Activeness using Space-centric Approach
    Billy Pik Lik Lau, Benny Kai Kiat Ng, Chau Yuen, Bige Tuncer, Keng Hua Chong
    http://arxiv.org/abs/2101.03725v2

    • [stat.ME]BDNNSurv: Bayesian deep neural networks for survival analysis using pseudo values
    Dai Feng, Lili Zhao
    http://arxiv.org/abs/2101.03170v1

    • [stat.ME]Bayesian equation selection on sparse data for discovery of stochastic dynamical systems
    Kushagra Gupta, Dootika Vats, Snigdhansu Chatterjee
    http://arxiv.org/abs/2101.04437v1

    • [stat.ME]Evaluation of Logistic Regression Applied to Respondent-Driven Samples: Simulated and Real Data
    Sandro Sperandei, Leonardo S. Bastos, Marcelo Ribeiro-Alves, Arianne Reis, Francisco I. Bastos
    http://arxiv.org/abs/2101.04253v1

    • [stat.ME]Flexible Validity Conditions for the Multivariate Matérn Covariance in any Spatial Dimension and for any Number of Components
    Xavier Emery, Emilio Porcu, Philip White
    http://arxiv.org/abs/2101.04235v1

    • [stat.ME]High-Dimensional Low-Rank Tensor Autoregressive Time Series Modeling
    Di Wang, Yao Zheng, Guodong Li
    http://arxiv.org/abs/2101.04276v1

    • [stat.ME]Mode Hunting Using Pettiest Components Analysis
    Tianhao Liu, Daniel Andrés Díaz-Pachón, J. Sunil Rao, Jean-Eudes Dazard
    http://arxiv.org/abs/2101.04288v1

    • [stat.ME]Moving sum data segmentation for stochastics processes based on invariance
    Claudia Kirch, Philipp Klein
    http://arxiv.org/abs/2101.04651v1

    • [stat.ME]New Bias Calibration for Robust Estimation in Small Areas
    Setareh Ranjbar, Elvezio Ronchetti, Stefan Sperlich
    http://arxiv.org/abs/2101.04390v1

    • [stat.ME]Penalized regression calibration: a method for the prediction of survival outcomes using complex longitudinal and high-dimensional data
    Mirko Signorelli, Pietro Spitali, Cristina Al-Khalili Szigyarto, The MARK-MD Consortium, Roula Tsonaka
    http://arxiv.org/abs/2101.04426v1

    • [stat.ME]Statistical analysis of periodic data in neuroscience
    Daniel H. Baker
    http://arxiv.org/abs/2101.04408v1

    • [stat.ME]Weighted Approach for Estimating Effects in Principal Strata with Missing Data for a Categorical Post-Baseline Variable in Randomized Controlled Trials
    Shengchun Kong, Dominik Heinzmann, Sabine Lauer, Tian Lu
    http://arxiv.org/abs/2101.04263v1

    • [stat.ML]Benchmarking Simulation-Based Inference
    Jan-Matthis Lueckmann, Jan Boelts, David S. Greenberg, Pedro J. Gonçalves, Jakob H. Macke
    http://arxiv.org/abs/2101.04653v1

    • [stat.ML]Data augmentation and feature selection for automatic model recommendation in computational physics
    Thomas Daniel, Fabien Casenave, Nissrine Akkari, David Ryckelynck
    http://arxiv.org/abs/2101.04530v1