astro-ph.IM - 仪器仪表和天体物理学方法
cond-mat.mtrl-sci - 材料科学 cond-mat.quant-gas - 量子气体 cs.AI - 人工智能 cs.CL - 计算与语言 cs.CR - 加密与安全 cs.CV - 机器视觉与模式识别 cs.CY - 计算与社会 cs.DC - 分布式、并行与集群计算 cs.DL - 数字图书馆 cs.HC - 人机接口 cs.IR - 信息检索 cs.IT - 信息论 cs.LG - 自动学习 cs.LO - 计算逻辑 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.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.SY - 系统和控制 math.NA - 数值分析 math.OC - 优化与控制 math.PR - 概率 math.ST - 统计理论 q-bio.GN - 基因组学 stat.AP - 应用统计 stat.ME - 统计方法论 stat.ML - (统计)机器学习
• [astro-ph.IM]Checkpoint, Restore, and Live Migration for Science Platforms
• [cond-mat.mtrl-sci]Accelerating the screening of amorphous polymer electrolytes by learning to reduce random and systematic errors in molecular dynamics simulations
• [cond-mat.quant-gas]Machine-learning enhanced dark soliton detection in Bose-Einstein condensates
• [cs.AI]Understanding the Effect of Out-of-distribution Examples and Interactive Explanations on Human-AI Decision Making
• [cs.CL]Better Together — An Ensemble Learner for Combining the Results of Ready-made Entity Linking Systems
• [cs.CL]ECOL: Early Detection of COVID Lies Using Content, Prior Knowledge and Source Information
• [cs.CL]Experimental Evaluation of Deep Learning models for Marathi Text Classification
• [cs.CL]Hostility Detection in Hindi leveraging Pre-Trained Language Models
• [cs.CL]Machine-Assisted Script Curation
• [cs.CL]Of Non-Linearity and Commutativity in BERT
• [cs.CL]On Informative Tweet Identification For Tracking Mass Events
• [cs.CL]On the Temporality of Priors in Entity Linking
• [cs.CL]Persistent Anti-Muslim Bias in Large Language Models
• [cs.CL]Persuasive Natural Language Generation — A Literature Review
• [cs.CL]SICKNL: A Dataset for Dutch Natural Language Inference
• [cs.CL]TUDublin team at Constraint@AAAI2021 — COVID19 Fake News Detection
• [cs.CL]Text Augmentation in a Multi-Task View
• [cs.CL]Transformer-based Language Model Fine-tuning Methods for COVID-19 Fake News Detection
• [cs.CL]WER-BERT: Automatic WER Estimation with BERT in a Balanced Ordinal Classification Paradigm
• [cs.CR]Cyber Taxi: A Taxonomy of Interactive Cyber Training and Education Systems
• [cs.CR]Malicious Code Detection: Run Trace Output Analysis by LSTM
• [cs.CR]Privacy Analysis in Language Models via Training Data Leakage Report
• [cs.CR]Selective Deletion in a Blockchain
• [cs.CR]Time-Based CAN Intrusion Detection Benchmark
• [cs.CV]AVGCN: Trajectory Prediction using Graph Convolutional Networks Guided by Human Attention
• [cs.CV]CityFlow-NL: Tracking and Retrieval of Vehicles at City Scale by Natural Language Descriptions
• [cs.CV]DAIL: Dataset-Aware and Invariant Learning for Face Recognition
• [cs.CV]DeFlow: Learning Complex Image Degradations from Unpaired Data with Conditional Flows
• [cs.CV]EEC: Learning to Encode and Regenerate Images for Continual Learning
• [cs.CV]Explainability of vision-based autonomous driving systems: Review and challenges
• [cs.CV]FabricNet: A Fiber Recognition Architecture Using Ensemble ConvNets
• [cs.CV]GAN Inversion: A Survey
• [cs.CV]Image deblurring based on lightweight multi-information fusion network
• [cs.CV]OrigamiSet1.0: Two New Datasets for Origami Classification and Difficulty Estimation
• [cs.CV]Practical Face Reconstruction via Differentiable Ray Tracing
• [cs.CV]Random Shadows and Highlights: A new data augmentation method for Extreme Lighting Conditions
• [cs.CV]Rescaling CNN through Learnable Repetition of Network Parameters
• [cs.CV]Road Surface Translation Under Snow-covered and Semantic Segmentation for Snow Hazard Index
• [cs.CV]Self-Supervised Learning for Segmentation
• [cs.CV]Stereo camera system calibration: the need of two sets of parameters
• [cs.CV]Towards Accurate Camouflaged Object Detection with Mixture Convolution and Interactive Fusion
• [cs.CV]TypeNet: Deep Learning Keystroke Biometrics
• [cs.CV]U-Noise: Learnable Noise Masks for Interpretable Image Segmentation
• [cs.CV]Understanding the Role of Scene Graphs in Visual Question Answering
• [cs.CY]Capturing social media expressions during the COVID-19 pandemic in Argentina and forecasting mental health and emotions
• [cs.CY]Exploring the Smart City Adoption Process: Evidence from the Belgian urban context
• [cs.CY]Exploring the socio-technical interplay of Industry 4.0: a single case study of an Italian manufacturing organisation
• [cs.DC]Asynchronous Gathering in a Torus
• [cs.DC]EDSC: An Event-Driven Smart Contract Platform
• [cs.DC]On the Synchronization Power of Token Smart Contracts
• [cs.DL]Quantitative View of the Structure of Institutional Scientific Collaborations Using the Examples of Halle, Jena and Leipzig
• [cs.DL]To what extent is researchers’ data-sharing motivated by formal mechanisms of recognition and credit?
• [cs.HC]Automating Gamification Personalization: To the User and Beyond
• [cs.HC]EventAnchor: Reducing Human Interactions in Event Annotation of Racket Sports Videos
• [cs.IR]: Cloud-Client Cooperative Deep Learning for Temporal Recommendation in the Post-GDPR Era
• [cs.IR]Eating Garlic Prevents COVID-19 Infection: Detecting Misinformation on the Arabic Content of Twitter
• [cs.IR]Learning Student Interest Trajectory for MOOCThread Recommendation
• [cs.IR]The Expando-Mono-Duo Design Pattern for Text Ranking with Pretrained Sequence-to-Sequence Models
• [cs.IR]TrNews: Heterogeneous User-Interest Transfer Learning for News Recommendation
• [cs.IT]Adaptive Private Distributed Matrix Multiplication
• [cs.IT]Decoding of Interleaved Linearized Reed-Solomon Codes with Applications to Network Coding
• [cs.IT]Group Testing with a Graph Infection Spread Model
• [cs.IT]Intelligent Reflecting Surfaces for Compute-and-Forward
• [cs.IT]Noise Is Useful: Exploiting Data Diversity for Edge Intelligence
• [cs.IT]Off-grid Channel Estimation with Sparse Bayesian Learning for OTFS Systems
• [cs.IT]On a Class of Time-Varying Gaussian ISI Channels
• [cs.LG]PF: Oversampling via Optimum-Path Forest for Breast Cancer Detection
• [cs.LG]4D Attention-based Neural Network for EEG Emotion Recognition
• [cs.LG]A Framework for Assurance of Medication Safety using Machine Learning
• [cs.LG]A Metaheuristic-Driven Approach to Fine-Tune Deep Boltzmann Machines
• [cs.LG]A Pipeline for Vision-Based On-Orbit Proximity Operations Using Deep Learning and Synthetic Imagery
• [cs.LG]Analysis of hidden feedback loops in continuous machine learning systems
• [cs.LG]Anomaly Detection Support Using Process Classification
• [cs.LG]BiGCN: A Bi-directional Low-Pass Filtering Graph Neural Network
• [cs.LG]Continuous Deep Q-Learning with Simulator for Stabilization of Uncertain Discrete-Time Systems
• [cs.LG]DICE: Diversity in Deep Ensembles via Conditional Redundancy Adversarial Estimation
• [cs.LG]Deep Cellular Recurrent Network for Efficient Analysis of Time-Series Data with Spatial Information
• [cs.LG]Entangled Kernels — Beyond Separability
• [cs.LG]Evaluating Deep Learning Approaches for Covid19 Fake News Detection
• [cs.LG]Evaluating Soccer Player: from Live Camera to Deep Reinforcement Learning
• [cs.LG]Evaluating the Robustness of Collaborative Agents
• [cs.LG]Federated Learning: Opportunities and Challenges
• [cs.LG]Gaussian Mixture Graphical Lasso with Application to Edge Detection in Brain Networks
• [cs.LG]Generating coherent spontaneous speech and gesture from text
• [cs.LG]Hyperbolic Deep Neural Networks: A Survey
• [cs.LG]Joint Dimensionality Reduction for Separable Embedding Estimation
• [cs.LG]Label Contrastive Coding based Graph Neural Network for Graph Classification
• [cs.LG]Learning and Fast Adaptation for Grid Emergency Control via Deep Meta Reinforcement Learning
• [cs.LG]NetCut: Real-Time DNN Inference Using Layer Removal
• [cs.LG]Neural networks behave as hash encoders: An empirical study
• [cs.LG]On the quantization of recurrent neural networks
• [cs.LG]Preferential Mixture-of-Experts: Interpretable Models that Rely on Human Expertise as much as Possible
• [cs.LG]Quantitative Rates and Fundamental Obstructions to Non-Euclidean Universal Approximation with Deep Narrow Feed-Forward Networks
• [cs.LG]Reliability Check via Weight Similarity in Privacy-Preserving Multi-Party Machine Learning
• [cs.LG]Scaling Equilibrium Propagation to Deep ConvNets by Drastically Reducing its Gradient Estimator Bias
• [cs.LG]Should Ensemble Members Be Calibrated?
• [cs.LG]Structured Prediction as Translation between Augmented Natural Languages
• [cs.LG]Tackling Instance-Dependent Label Noise via a Universal Probabilistic Model
• [cs.LG]Topological Deep Learning
• [cs.LG]Towards Creating a Deployable Grasp Type Probability Estimator for a Prosthetic Hand
• [cs.LG]Towards Practical Adam: Non-Convexity, Convergence Theory, and Mini-Batch Acceleration
• [cs.LG]Uniform Error and Posterior Variance Bounds for Gaussian Process Regression with Application to Safe Control
• [cs.LG]X-CAL: Explicit Calibration for Survival Analysis
• [cs.LO]Analysis of E-commerce Ranking Signals via Signal Temporal Logic
• [cs.MA]Learning Safe Multi-Agent Control with Decentralized Neural Barrier Certificates
• [cs.NE]A Multiple Classifier Approach for Concatenate-Designed Neural Networks
• [cs.NE]A Nature-Inspired Feature Selection Approach based on Hypercomplex Information
• [cs.NI]Leader Confirmation Replication for Millisecond Consensus in Geo-distributed Private Chains
• [cs.RO]A Survey on Simulators for Testing Self-Driving Cars
• [cs.RO]Enclosing the Sliding Surfaces of a Controlled Swing
• [cs.RO]Ensemble of LSTMs and feature selection for human action prediction
• [cs.RO]Interpreting and Predicting Tactile Signals for the SynTouch BioTac
• [cs.RO]Learning Kinematic Feasibility for Mobile Manipulation through Deep Reinforcement Learning
• [cs.RO]Multi-robot Symmetric Rendezvous Searchon the Line
• [cs.RO]Rule-based Optimal Control for Autonomous Driving
• [cs.RO]Spillover Algorithm: A Decentralized Coordination Approach for Multi-Robot Production Planning in Open Shared Factories
• [cs.RO]Temporal Logic Task Allocation in Heterogeneous Multi-Robot Systems
• [cs.SI]Publishing patterns reflect political polarization in news media
• [cs.SI]Signal Processing on Higher-Order Networks: Livin’ on the Edge … and Beyond
• [cs.SI]Towards Understanding and Evaluating Structural Node Embeddings
• [econ.GN]Scared into Action: How Partisanship and Fear are Associated with Reactions to Public Health Directives
• [eess.AS]An evaluation of word-level confidence estimation for end-to-end automatic speech recognition
• [eess.AS]Whispered and Lombard Neural Speech Synthesis
• [eess.IV]A Multi-Stage Attentive Transfer Learning Framework for Improving COVID-19 Diagnosis
• [eess.IV]A Unified Conditional Disentanglement Framework for Multimodal Brain MR Image Translation
• [eess.IV]Advancing Eosinophilic Esophagitis Diagnosis and Phenotype Assessment with Deep Learning Computer Vision
• [eess.IV]Automated Model Design and Benchmarking of 3D Deep Learning Models for COVID-19 Detection with Chest CT Scans
• [eess.IV]Dual-cycle Constrained Bijective VAE-GAN For Tagged-to-Cine Magnetic Resonance Image Synthesis
• [eess.IV]MRI Images, Brain Lesions and Deep Learning
• [eess.SY]Optimal Energy Shaping via Neural Approximators
• [math.NA]Non-intrusive surrogate modeling for parametrized time-dependent PDEs using convolutional autoencoders
• [math.OC]No-go Theorem for Acceleration in the Hyperbolic Plane
• [math.PR]Explicit non-asymptotic bounds for the distance to the first-order Edgeworth expansion
• [math.ST]Breaking the curse: a dimension free computational upper-bound for smooth optimal transport estimation
• [math.ST]From Smooth Wasserstein Distance to Dual Sobolev Norm: Empirical Approximation and Statistical Applications
• [math.ST]Hidden Markov chains and fields with observations in Riemannian manifolds
• [math.ST]Kernel-based ANOVA decomposition and Shapley effects — Application to global sensitivity analysis
• [math.ST]New bounds for -means and information
-means
• [math.ST]Optimal Clustering in Anisotropic Gaussian Mixture Models
• [math.ST]Optimal designs for comparing regression curves — dependence within and between groups
• [math.ST]Optimal network online change point localisation
• [q-bio.GN]Feature reduction for machine learning on molecular features: The GeneScore
• [stat.AP]Assortativity measures for weighted and directed networks
• [stat.AP]Estimating functional parameters for understanding the impact of weather and government interventions on COVID-19 outbreak
• [stat.ME]A new volatility model: GQARCH-Itô model
• [stat.ME]Adaptive shrinkage of smooth functional effects towards a predefined functional subspace
• [stat.ME]Agglomerative Hierarchical Clustering for Selecting Valid Instrumental Variables
• [stat.ME]Bayesian Multiple Index Models for Environmental Mixtures
• [stat.ME]Bayesian inference with tmbstan for a state-space model with VAR(1) state equation
• [stat.ME]Enhanced Cube Implementation For Highly Stratified Population
• [stat.ME]Integrative Learning for Population of Dynamic Networks with Covariates
• [stat.ME]P-spline smoothed functional ICA of EEG data
• [stat.ME]The -family of covariance functions: A Matérn analogue for modeling random fields on spheres
• [stat.ML]Convex Smoothed Autoencoder-Optimal Transport model
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• [astro-ph.IM]Checkpoint, Restore, and Live Migration for Science Platforms
Mario Juric, Steven Stetzler, Colin T. Slater
http://arxiv.org/abs/2101.05782v1
• [cond-mat.mtrl-sci]Accelerating the screening of amorphous polymer electrolytes by learning to reduce random and systematic errors in molecular dynamics simulations
Tian Xie, Arthur France-Lanord, Yanming Wang, Jeffrey Lopez, Michael Austin Stolberg, Megan Hill, Graham Michael Leverick, Rafael Gomez-Bombarelli, Jeremiah A. Johnson, Yang Shao-Horn, Jeffrey C. Grossman
http://arxiv.org/abs/2101.05339v1
• [cond-mat.quant-gas]Machine-learning enhanced dark soliton detection in Bose-Einstein condensates
Shangjie Guo, Amilson R. Fritsch, Craig Greenberg, I. B. Spielman, Justyna P. Zwolak
http://arxiv.org/abs/2101.05404v1
• [cs.AI]Understanding the Effect of Out-of-distribution Examples and Interactive Explanations on Human-AI Decision Making
Han Liu, Vivian Lai, Chenhao Tan
http://arxiv.org/abs/2101.05303v1
• [cs.CL]Better Together — An Ensemble Learner for Combining the Results of Ready-made Entity Linking Systems
Renato Stoffalette João, Pavlos Fafalios, Stefan Dietze
http://arxiv.org/abs/2101.05634v1
• [cs.CL]ECOL: Early Detection of COVID Lies Using Content, Prior Knowledge and Source Information
Ipek Baris, Zeyd Boukhers
http://arxiv.org/abs/2101.05499v1
• [cs.CL]Experimental Evaluation of Deep Learning models for Marathi Text Classification
Atharva Kulkarni, Meet Mandhane, Manali Likhitkar, Gayatri Kshirsagar, Jayashree Jagdale, Raviraj Joshi
http://arxiv.org/abs/2101.04899v2
• [cs.CL]Hostility Detection in Hindi leveraging Pre-Trained Language Models
Ojasv Kamal, Adarsh Kumar, Tejas Vaidhya
http://arxiv.org/abs/2101.05494v1
• [cs.CL]Machine-Assisted Script Curation
Manuel R. Ciosici, Joseph Cummings, Mitchell DeHaven, Alex Hedges, Yash Kankanampati, Dong-Ho Lee, Ralph Weischedel, Marjorie Freedman
http://arxiv.org/abs/2101.05400v1
• [cs.CL]Of Non-Linearity and Commutativity in BERT
Sumu Zhao, Damian Pascual, Gino Brunner, Roger Wattenhofer
http://arxiv.org/abs/2101.04547v3
• [cs.CL]On Informative Tweet Identification For Tracking Mass Events
Renato Stoffalette João
http://arxiv.org/abs/2101.05656v1
• [cs.CL]On the Temporality of Priors in Entity Linking
Renato Stoffalette Joao
http://arxiv.org/abs/2101.05593v1
• [cs.CL]Persistent Anti-Muslim Bias in Large Language Models
Abubakar Abid, Maheen Farooqi, James Zou
http://arxiv.org/abs/2101.05783v1
• [cs.CL]Persuasive Natural Language Generation — A Literature Review
Sebastian Duerr, Peter A. Gloor
http://arxiv.org/abs/2101.05786v1
• [cs.CL]SICKNL: A Dataset for Dutch Natural Language Inference
Gijs Wijnholds, Michael Moortgat
http://arxiv.org/abs/2101.05716v1
• [cs.CL]TUDublin team at Constraint@AAAI2021 — COVID19 Fake News Detection
Elena Shushkevich, John Cardiff
http://arxiv.org/abs/2101.05701v1
• [cs.CL]Text Augmentation in a Multi-Task View
Jason Wei, Chengyu Huang, Shiqi Xu, Soroush Vosoughi
http://arxiv.org/abs/2101.05469v1
• [cs.CL]Transformer-based Language Model Fine-tuning Methods for COVID-19 Fake News Detection
Ben Chen, Bin Chen, Dehong Gao, Qijin Chen, Chengfu Huo, Xiaonan Meng, Weijun Ren, Yang Zhou
http://arxiv.org/abs/2101.05509v1
• [cs.CL]WER-BERT: Automatic WER Estimation with BERT in a Balanced Ordinal Classification Paradigm
Akshay Krishna Sheshadri, Anvesh Rao Vijjini, Sukhdeep Kharbanda
http://arxiv.org/abs/2101.05478v1
• [cs.CR]Cyber Taxi: A Taxonomy of Interactive Cyber Training and Education Systems
Marcus Knüpfer, Tore Bierwirth, Lars Stiemert, Matthias Schopp, Sebastian Seeber, Daniela Pöhn, Peter Hillmann
http://arxiv.org/abs/2101.05538v1
• [cs.CR]Malicious Code Detection: Run Trace Output Analysis by LSTM
Cengiz Acarturk, Melih Sirlanci, Pinar Gurkan Balikcioglu, Deniz Demirci, Nazenin Sahin, Ozge Acar Kucuk
http://arxiv.org/abs/2101.05646v1
• [cs.CR]Privacy Analysis in Language Models via Training Data Leakage Report
Huseyin A. Inan, Osman Ramadan, Lukas Wutschitz, Daniel Jones, Victor Rühle, James Withers, Robert Sim
http://arxiv.org/abs/2101.05405v1
• [cs.CR]Selective Deletion in a Blockchain
Peter Hillmann, Marcus Knüpfer, Erik Heiland, Andreas Karcher
http://arxiv.org/abs/2101.05495v1
• [cs.CR]Time-Based CAN Intrusion Detection Benchmark
Deborah H. Blevins, Pablo Moriano, Robert A. Bridges, Miki E. Verma, Michael D. Iannacone, Samuel C Hollifield
http://arxiv.org/abs/2101.05781v1
• [cs.CV]AVGCN: Trajectory Prediction using Graph Convolutional Networks Guided by Human Attention
Congcong Liu, Yuying Chen, Ming Liu, Bertram E. Shi
http://arxiv.org/abs/2101.05682v1
• [cs.CV]CityFlow-NL: Tracking and Retrieval of Vehicles at City Scale by Natural Language Descriptions
Qi Feng, Vitaly Ablavsky, Stan Sclaroff
http://arxiv.org/abs/2101.04741v2
• [cs.CV]DAIL: Dataset-Aware and Invariant Learning for Face Recognition
Gaoang Wang, Lin Chen, Tianqiang Liu, Mingwei He, Jiebo Luo
http://arxiv.org/abs/2101.05419v1
• [cs.CV]DeFlow: Learning Complex Image Degradations from Unpaired Data with Conditional Flows
Valentin Wolf, Andreas Lugmayr, Martin Danelljan, Luc Van Gool, Radu Timofte
http://arxiv.org/abs/2101.05796v1
• [cs.CV]EEC: Learning to Encode and Regenerate Images for Continual Learning
Ali Ayub, Alan R. Wagner
http://arxiv.org/abs/2101.04904v2
• [cs.CV]Explainability of vision-based autonomous driving systems: Review and challenges
Éloi Zablocki, Hédi Ben-Younes, Patrick Pérez, Matthieu Cord
http://arxiv.org/abs/2101.05307v1
• [cs.CV]FabricNet: A Fiber Recognition Architecture Using Ensemble ConvNets
Abu Quwsar Ohi, M. F. Mridha, Md. Abdul Hamid, Muhammad Mostafa Monowar, Faris A Kateb
http://arxiv.org/abs/2101.05564v1
• [cs.CV]GAN Inversion: A Survey
Weihao Xia, Yulun Zhang, Yujiu Yang, Jing-Hao Xue, Bolei Zhou, Ming-Hsuan Yang
http://arxiv.org/abs/2101.05278v1
• [cs.CV]Image deblurring based on lightweight multi-information fusion network
Yanni Zhang, Yiming Liu, Qiang Li, Miao Qi, Dahong Xu, Jun Kong, Jianzhong Wang
http://arxiv.org/abs/2101.05403v1
• [cs.CV]OrigamiSet1.0: Two New Datasets for Origami Classification and Difficulty Estimation
Daniel Ma, Gerald Friedland, Mario Michael Krell
http://arxiv.org/abs/2101.05470v1
• [cs.CV]Practical Face Reconstruction via Differentiable Ray Tracing
Abdallah Dib, Gaurav Bharaj, Junghyun Ahn, Cédric Thébault, Philippe-Henri Gosselin, Marco Romeo, Louis Chevallier
http://arxiv.org/abs/2101.05356v1
• [cs.CV]Random Shadows and Highlights: A new data augmentation method for Extreme Lighting Conditions
Osama Mazhar, Jens Kober
http://arxiv.org/abs/2101.05361v1
• [cs.CV]Rescaling CNN through Learnable Repetition of Network Parameters
Arnav Chavan, Udbhav Bamba, Rishabh Tiwari, Deepak Gupta
http://arxiv.org/abs/2101.05650v1
• [cs.CV]Road Surface Translation Under Snow-covered and Semantic Segmentation for Snow Hazard Index
Yasuno Takato, Fujii Junichiro, Sugawara Hiroaki, Amakata Masazumi
http://arxiv.org/abs/2101.05616v1
• [cs.CV]Self-Supervised Learning for Segmentation
Abhinav Dhere, Jayanthi Sivaswamy
http://arxiv.org/abs/2101.05456v1
• [cs.CV]Stereo camera system calibration: the need of two sets of parameters
Riccardo Beschi, Xiao Feng, Stefania Melillo, Lorena Postiglione
http://arxiv.org/abs/2101.05725v1
• [cs.CV]Towards Accurate Camouflaged Object Detection with Mixture Convolution and Interactive Fusion
Bo Dong, Mingchen Zhuge, Yongxiong Wang, Hongbo Bi, Geng Chen
http://arxiv.org/abs/2101.05687v1
• [cs.CV]TypeNet: Deep Learning Keystroke Biometrics
Alejandro Acien, Aythami Morales, John V. Monaco, Ruben Vera-Rodriguez, Julian Fierrez
http://arxiv.org/abs/2101.05570v1
• [cs.CV]U-Noise: Learnable Noise Masks for Interpretable Image Segmentation
Teddy Koker, Fatemehsadat Mireshghallah, Tom Titcombe, Georgios Kaissis
http://arxiv.org/abs/2101.05791v1
• [cs.CV]Understanding the Role of Scene Graphs in Visual Question Answering
Vinay Damodaran, Sharanya Chakravarthy, Akshay Kumar, Anjana Umapathy, Teruko Mitamura, Yuta Nakashima, Noa Garcia, Chenhui Chu
http://arxiv.org/abs/2101.05479v1
• [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/2101.04540v2
• [cs.CY]Exploring the Smart City Adoption Process: Evidence from the Belgian urban context
Emanuele Gabriel Margherita, Giovanni Esposito, Stefania Denise Escobar, Nathalie Crutzen
http://arxiv.org/abs/2101.05670v1
• [cs.CY]Exploring the socio-technical interplay of Industry 4.0: a single case study of an Italian manufacturing organisation
Emanuele Gabriel Margherita, Alessio Maria Braccini
http://arxiv.org/abs/2101.05665v1
• [cs.DC]Asynchronous Gathering in a Torus
Sayaka Kamei, Anissa Lamani, Fukuhito Ooshita, Sebastien Tixeuil, Koichi Wada
http://arxiv.org/abs/2101.05421v1
• [cs.DC]EDSC: An Event-Driven Smart Contract Platform
Mudabbir Kaleem, Keshav Kasichainula, Rabimba Karanjai, Lei Xu, Zhimin Gao, Lin Chen, Weidong Shi
http://arxiv.org/abs/2101.05475v1
• [cs.DC]On the Synchronization Power of Token Smart Contracts
Orestis Alpos, Christian Cachin, Giorgia Azzurra Marson, Luca Zanolini
http://arxiv.org/abs/2101.05543v1
• [cs.DL]Quantitative View of the Structure of Institutional Scientific Collaborations Using the Examples of Halle, Jena and Leipzig
Aliakbar Akbaritabar
http://arxiv.org/abs/2101.05784v1
• [cs.DL]To what extent is researchers’ data-sharing motivated by formal mechanisms of recognition and credit?
Pablo Dorta-González, Sara M. González-Betancor, María Isabel Dorta-González
http://arxiv.org/abs/2101.05636v1
• [cs.HC]Automating Gamification Personalization: To the User and Beyond
Luiz Rodrigues, Armando M. Toda, Wilk Oliveira, Paula T. Palomino, Julita Vassileva, Seiji Isotani
http://arxiv.org/abs/2101.05718v1
• [cs.HC]EventAnchor: Reducing Human Interactions in Event Annotation of Racket Sports Videos
Dazhen Deng, Jiang Wu, Jiachen Wang, Yihong Wu, Xiao Xie, Zheng Zhou, Hui Zhang, Xiaolong Zhang, Yingcai Wu
http://arxiv.org/abs/2101.04954v2
• [cs.IR]: Cloud-Client Cooperative Deep Learning for Temporal Recommendation in the Post-GDPR Era
Jialiang Han, Yun Ma
http://arxiv.org/abs/2101.05641v1
• [cs.IR]Eating Garlic Prevents COVID-19 Infection: Detecting Misinformation on the Arabic Content of Twitter
Sarah Alqurashi, Btool Hamoui, Abdulaziz Alashaikh, Ahmad Alhindi, Eisa Alanazi
http://arxiv.org/abs/2101.05626v1
• [cs.IR]Learning Student Interest Trajectory for MOOCThread Recommendation
Shalini Pandey, Andrew Lan, George Karypis, Jaideep Srivastava
http://arxiv.org/abs/2101.05625v1
• [cs.IR]The Expando-Mono-Duo Design Pattern for Text Ranking with Pretrained Sequence-to-Sequence Models
Ronak Pradeep, Rodrigo Nogueira, Jimmy Lin
http://arxiv.org/abs/2101.05667v1
• [cs.IR]TrNews: Heterogeneous User-Interest Transfer Learning for News Recommendation
Guangneng Hu, Qiang Yang
http://arxiv.org/abs/2101.05611v1
• [cs.IT]Adaptive Private Distributed Matrix Multiplication
Rawad Bitar, Marvin Xhemrishi, Antonia Wachter-Zeh
http://arxiv.org/abs/2101.05681v1
• [cs.IT]Decoding of Interleaved Linearized Reed-Solomon Codes with Applications to Network Coding
Hannes Bartz, Sven Puchinger
http://arxiv.org/abs/2101.05604v1
• [cs.IT]Group Testing with a Graph Infection Spread Model
Batuhan Arasli, Sennur Ulukus
http://arxiv.org/abs/2101.05792v1
• [cs.IT]Intelligent Reflecting Surfaces for Compute-and-Forward
Mahdi Jafari Siavoshani, Seyed Pooya Shariatpanahi
http://arxiv.org/abs/2101.05607v1
• [cs.IT]Noise Is Useful: Exploiting Data Diversity for Edge Intelligence
Zhi Zeng, Yuan Liu, Weijun Tang, Fangjiong Chen
http://arxiv.org/abs/2101.05465v1
• [cs.IT]Off-grid Channel Estimation with Sparse Bayesian Learning for OTFS Systems
Zhiqiang Wei, Weijie Yuan, Shuangyang Li, Jinhong Yuan, Derrick Wing Kwan Ng
http://arxiv.org/abs/2101.05629v1
• [cs.IT]On a Class of Time-Varying Gaussian ISI Channels
Kamyar Moshksar
http://arxiv.org/abs/2101.05373v1
• [cs.LG]PF: Oversampling via Optimum-Path Forest for Breast Cancer Detection
Leandro Aparecido Passos, Danilo Samuel Jodas, Luiz C. F. Ribeiro, Thierry Pinheiro, João P. Papa
http://arxiv.org/abs/2101.05775v1
• [cs.LG]4D Attention-based Neural Network for EEG Emotion Recognition
Guowen Xiao, Mengwen Ye, Bowen Xu, Zhendi Chen, Quansheng Ren
http://arxiv.org/abs/2101.05484v1
• [cs.LG]A Framework for Assurance of Medication Safety using Machine Learning
Yan Jia, Tom Lawton, John McDermid, Eric Rojas, Ibrahim Habli
http://arxiv.org/abs/2101.05620v1
• [cs.LG]A Metaheuristic-Driven Approach to Fine-Tune Deep Boltzmann Machines
Leandro Aparecido Passos, João Paulo Papa
http://arxiv.org/abs/2101.05795v1
• [cs.LG]A Pipeline for Vision-Based On-Orbit Proximity Operations Using Deep Learning and Synthetic Imagery
Carson Schubert, Kevin Black, Daniel Fonseka, Abhimanyu Dhir, Jacob Deutsch, Nihal Dhamani, Gavin Martin, Maruthi Akella
http://arxiv.org/abs/2101.05661v1
• [cs.LG]Analysis of hidden feedback loops in continuous machine learning systems
Anton Khritankov
http://arxiv.org/abs/2101.05673v1
• [cs.LG]Anomaly Detection Support Using Process Classification
Sebastian Eresheim, Lukas Daniel Klausner, Patrick Kochberger
http://arxiv.org/abs/2101.05371v1
• [cs.LG]BiGCN: A Bi-directional Low-Pass Filtering Graph Neural Network
Zhixian Chen, Tengfei Ma, Zhihua Jin, Yangqiu Song, Yang Wang
http://arxiv.org/abs/2101.05519v1
• [cs.LG]Continuous Deep Q-Learning with Simulator for Stabilization of Uncertain Discrete-Time Systems
Junya Ikemoto, Toshimitsu Ushio
http://arxiv.org/abs/2101.05640v1
• [cs.LG]DICE: Diversity in Deep Ensembles via Conditional Redundancy Adversarial Estimation
Alexandre Rame, Matthieu Cord
http://arxiv.org/abs/2101.05544v1
• [cs.LG]Deep Cellular Recurrent Network for Efficient Analysis of Time-Series Data with Spatial Information
Lasitha Vidyaratne, Mahbubul Alam, Alexander Glandon, Anna Shabalina, Christopher Tennant, Khan Iftekharuddin
http://arxiv.org/abs/2101.05608v1
• [cs.LG]Entangled Kernels — Beyond Separability
Riikka Huusari, Hachem Kadri
http://arxiv.org/abs/2101.05514v1
• [cs.LG]Evaluating Deep Learning Approaches for Covid19 Fake News Detection
Apurva Wani, Isha Joshi, Snehal Khandve, Vedangi Wagh, Raviraj Joshi
http://arxiv.org/abs/2101.04012v2
• [cs.LG]Evaluating Soccer Player: from Live Camera to Deep Reinforcement Learning
Paul Garnier, Théophane Gregoir
http://arxiv.org/abs/2101.05388v1
• [cs.LG]Evaluating the Robustness of Collaborative Agents
Paul Knott, Micah Carroll, Sam Devlin, Kamil Ciosek, Katja Hofmann, A. D. Dragan, Rohin Shah
http://arxiv.org/abs/2101.05507v1
• [cs.LG]Federated Learning: Opportunities and Challenges
Priyanka Mary Mammen
http://arxiv.org/abs/2101.05428v1
• [cs.LG]Gaussian Mixture Graphical Lasso with Application to Edge Detection in Brain Networks
Hang Yin, Xinyue Liu, Xiangnan Kong
http://arxiv.org/abs/2101.05348v1
• [cs.LG]Generating coherent spontaneous speech and gesture from text
Simon Alexanderson, Éva Székely, Gustav Eje Henter, Taras Kucherenko, Jonas Beskow
http://arxiv.org/abs/2101.05684v1
• [cs.LG]Hyperbolic Deep Neural Networks: A Survey
Wei Peng, Tuomas Varanka, Abdelrahman Mostafa, Henglin Shi, Guoying Zhao
http://arxiv.org/abs/2101.04562v2
• [cs.LG]Joint Dimensionality Reduction for Separable Embedding Estimation
Yanjun Li, Bihan Wen, Hao Cheng, Yoram Bresler
http://arxiv.org/abs/2101.05500v1
• [cs.LG]Label Contrastive Coding based Graph Neural Network for Graph Classification
Yuxiang Ren, Jiyang Bai, Jiawei Zhang
http://arxiv.org/abs/2101.05486v1
• [cs.LG]Learning and Fast Adaptation for Grid Emergency Control via Deep Meta Reinforcement Learning
Renke Huang, Yujiao Chen, Tianzhixi Yin, Qiuhua Huang, Jie Tan, Wenhao Yu, Xinya Li, Ang Li, Yan Du
http://arxiv.org/abs/2101.05317v1
• [cs.LG]NetCut: Real-Time DNN Inference Using Layer Removal
Mehrshad Zandigohar, Deniz Erdogmus, Gunar Schirner
http://arxiv.org/abs/2101.05363v1
• [cs.LG]Neural networks behave as hash encoders: An empirical study
Fengxiang He, Shiye Lei, Jianmin Ji, Dacheng Tao
http://arxiv.org/abs/2101.05490v1
• [cs.LG]On the quantization of recurrent neural networks
Jian Li, Raziel Alvarez
http://arxiv.org/abs/2101.05453v1
• [cs.LG]Preferential Mixture-of-Experts: Interpretable Models that Rely on Human Expertise as much as Possible
Melanie F. Pradier, Javier Zazo, Sonali Parbhoo, Roy H. Perlis, Maurizio Zazzi, Finale Doshi-Velez
http://arxiv.org/abs/2101.05360v1
• [cs.LG]Quantitative Rates and Fundamental Obstructions to Non-Euclidean Universal Approximation with Deep Narrow Feed-Forward Networks
Anastasis Kratsios, Leonie Papon
http://arxiv.org/abs/2101.05390v1
• [cs.LG]Reliability Check via Weight Similarity in Privacy-Preserving Multi-Party Machine Learning
Kennedy Edemacu, Beakcheol Jang, Jong Wook Kim
http://arxiv.org/abs/2101.05504v1
• [cs.LG]Scaling Equilibrium Propagation to Deep ConvNets by Drastically Reducing its Gradient Estimator Bias
Axel Laborieux, Maxence Ernoult, Benjamin Scellier, Yoshua Bengio, Julie Grollier, Damien Querlioz
http://arxiv.org/abs/2101.05536v1
• [cs.LG]Should Ensemble Members Be Calibrated?
Xixin Wu, Mark Gales
http://arxiv.org/abs/2101.05397v1
• [cs.LG]Structured Prediction as Translation between Augmented Natural Languages
Giovanni Paolini, Ben Athiwaratkun, Jason Krone, Jie Ma, Alessandro Achille, Rishita Anubhai, Cicero Nogueira dos Santos, Bing Xiang, Stefano Soatto
http://arxiv.org/abs/2101.05779v1
• [cs.LG]Tackling Instance-Dependent Label Noise via a Universal Probabilistic Model
Qizhou Wang, Bo Han, Tongliang Liu, Gang Niu, Jian Yang, Chen Gong
http://arxiv.org/abs/2101.05467v1
• [cs.LG]Topological Deep Learning
Ephy R. Love, Benjamin Filippenko, Vasileios Maroulas, Gunnar Carlsson
http://arxiv.org/abs/2101.05778v1
• [cs.LG]Towards Creating a Deployable Grasp Type Probability Estimator for a Prosthetic Hand
Mehrshad Zandigohar, Mo Han, Deniz Erdogmus, Gunar Schirner
http://arxiv.org/abs/2101.05357v1
• [cs.LG]Towards Practical Adam: Non-Convexity, Convergence Theory, and Mini-Batch Acceleration
Congliang Chen, Li Shen, Fangyu Zou, Wei Liu
http://arxiv.org/abs/2101.05471v1
• [cs.LG]Uniform Error and Posterior Variance Bounds for Gaussian Process Regression with Application to Safe Control
Armin Lederer, Jonas Umlauft, Sandra Hirche
http://arxiv.org/abs/2101.05328v1
• [cs.LG]X-CAL: Explicit Calibration for Survival Analysis
Mark Goldstein, Xintian Han, Aahlad Puli, Adler J. Perotte, Rajesh Ranganath
http://arxiv.org/abs/2101.05346v1
• [cs.LO]Analysis of E-commerce Ranking Signals via Signal Temporal Logic
Tommaso Dreossi, Giorgio Ballardin, Parth Gupta, Jan Bakus, Yu-Hsiang Lin, Vamsi Salaka
http://arxiv.org/abs/2101.05415v1
• [cs.MA]Learning Safe Multi-Agent Control with Decentralized Neural Barrier Certificates
Zengyi Qin, Kaiqing Zhang, Yuxiao Chen, Jingkai Chen, Chuchu Fan
http://arxiv.org/abs/2101.05436v1
• [cs.NE]A Multiple Classifier Approach for Concatenate-Designed Neural Networks
Ka-Hou Chan, Sio-Kei Im, Wei Ke
http://arxiv.org/abs/2101.05457v1
• [cs.NE]A Nature-Inspired Feature Selection Approach based on Hypercomplex Information
Gustavo H. de Rosa, João Paulo Papa, Xin-She Yang
http://arxiv.org/abs/2101.05652v1
• [cs.NI]Leader Confirmation Replication for Millisecond Consensus in Geo-distributed Private Chains
Dongjie Zhu, Haiwen Du, Yundong Sun, Zhaoshuo Tian
http://arxiv.org/abs/2101.05462v1
• [cs.RO]A Survey on Simulators for Testing Self-Driving Cars
Prabhjot Kaur, Samira Taghavi, Zhaofeng Tian, Weisong Shi
http://arxiv.org/abs/2101.05337v1
• [cs.RO]Enclosing the Sliding Surfaces of a Controlled Swing
Luc Jaulin, Benoît Desrochers
http://arxiv.org/abs/2101.05418v1
• [cs.RO]Ensemble of LSTMs and feature selection for human action prediction
Tomislav Petković, Luka Petrović, Ivan Marković, Ivan Petrović
http://arxiv.org/abs/2101.05645v1
• [cs.RO]Interpreting and Predicting Tactile Signals for the SynTouch BioTac
Yashraj S. Narang, Balakumar Sundaralingam, Karl Van Wyk, Arsalan Mousavian, Dieter Fox
http://arxiv.org/abs/2101.05452v1
• [cs.RO]Learning Kinematic Feasibility for Mobile Manipulation through Deep Reinforcement Learning
Daniel Honerkamp, Tim Welschehold, Abhinav Valada
http://arxiv.org/abs/2101.05325v1
• [cs.RO]Multi-robot Symmetric Rendezvous Searchon the Line
Deniz Ozsoyeller, Pratap Tokekar
http://arxiv.org/abs/2101.05324v1
• [cs.RO]Rule-based Optimal Control for Autonomous Driving
Wei Xiao, Noushin Mehdipour, Anne Collin, Amitai Bin-Nun, Emilio Frazzoli, Radboud Duintjer Tebbens, Calin Belta
http://arxiv.org/abs/2101.05709v1
• [cs.RO]Spillover Algorithm: A Decentralized Coordination Approach for Multi-Robot Production Planning in Open Shared Factories
Marin Lujak, Alberto Fernández, Eva Onaindia
http://arxiv.org/abs/2101.05700v1
• [cs.RO]Temporal Logic Task Allocation in Heterogeneous Multi-Robot Systems
Xusheng Luo, Michael M. Zavlanos
http://arxiv.org/abs/2101.05694v1
• [cs.SI]Publishing patterns reflect political polarization in news media
Nick Hagar, Johannes Wachs, Emőke-Ágnes Horvát
http://arxiv.org/abs/2101.05044v2
• [cs.SI]Signal Processing on Higher-Order Networks: Livin’ on the Edge … and Beyond
Michael T. Schaub, Yu Zhu, Jean-Baptiste Seby, T. Mitchell Roddenberry, Santiago Segarra
http://arxiv.org/abs/2101.05510v1
• [cs.SI]Towards Understanding and Evaluating Structural Node Embeddings
Junchen Jin, Mark Heimann, Di Jin, Danai Koutra
http://arxiv.org/abs/2101.05730v1
• [econ.GN]Scared into Action: How Partisanship and Fear are Associated with Reactions to Public Health Directives
Mike Lindow, David DeFranza, Arul Mishra, Himanshu Mishra
http://arxiv.org/abs/2101.05365v1
• [eess.AS]An evaluation of word-level confidence estimation for end-to-end automatic speech recognition
Dan Oneata, Alexandru Caranica, Adriana Stan, Horia Cucu
http://arxiv.org/abs/2101.05525v1
• [eess.AS]Whispered and Lombard Neural Speech Synthesis
Qiong Hu, Tobias Bleisch, Petko Petkov, Tuomo Raitio, Erik Marchi, Varun Lakshminarasimhan
http://arxiv.org/abs/2101.05313v1
• [eess.IV]A Multi-Stage Attentive Transfer Learning Framework for Improving COVID-19 Diagnosis
Yi Liu, Shuiwang Ji
http://arxiv.org/abs/2101.05410v1
• [eess.IV]A Unified Conditional Disentanglement Framework for Multimodal Brain MR Image Translation
Xiaofeng Liu, Fangxu Xing, Georges El Fakhri, Jonghye Woo
http://arxiv.org/abs/2101.05434v1
• [eess.IV]Advancing Eosinophilic Esophagitis Diagnosis and Phenotype Assessment with Deep Learning Computer Vision
William Adorno III, Alexis Catalano, Lubaina Ehsan, Hans Vitzhum von Eckstaedt, Barrett Barnes, Emily McGowan, Sana Syed, Donald E. Brown
http://arxiv.org/abs/2101.05326v1
• [eess.IV]Automated Model Design and Benchmarking of 3D Deep Learning Models for COVID-19 Detection with Chest CT Scans
Xin He, Shihao Wang, Xiaowen Chu, Shaohuai Shi, Jiangping Tang, Xin Liu, Chenggang Yan, Jiyong Zhang, Guiguang Ding
http://arxiv.org/abs/2101.05442v1
• [eess.IV]Dual-cycle Constrained Bijective VAE-GAN For Tagged-to-Cine Magnetic Resonance Image Synthesis
Xiaofeng Liu, Fangxu Xing, Jerry L. Prince, Aaron Carass, Maureen Stone, Georges El Fakhri, Jonghye Woo
http://arxiv.org/abs/2101.05439v1
• [eess.IV]MRI Images, Brain Lesions and Deep Learning
Darwin Castillo, Vasudevan Lakshminarayanan, Maria J. Rodriguez-Alvarez
http://arxiv.org/abs/2101.05091v2
• [eess.SY]Optimal Energy Shaping via Neural Approximators
Stefano Massaroli, Michael Poli, Federico Califano, Jinkyoo Park, Atsushi Yamashita, Hajime Asama
http://arxiv.org/abs/2101.05537v1
• [math.NA]Non-intrusive surrogate modeling for parametrized time-dependent PDEs using convolutional autoencoders
Stefanos Nikolopoulos, Ioannis Kalogeris, Vissarion Papadopoulos
http://arxiv.org/abs/2101.05555v1
• [math.OC]No-go Theorem for Acceleration in the Hyperbolic Plane
Linus Hamilton, Ankur Moitra
http://arxiv.org/abs/2101.05657v1
• [math.PR]Explicit non-asymptotic bounds for the distance to the first-order Edgeworth expansion
Alexis Derumigny, Lucas Girard, Yannick Guyonvarch
http://arxiv.org/abs/2101.05780v1
• [math.ST]Breaking the curse: a dimension free computational upper-bound for smooth optimal transport estimation
Adrien Vacher, Francois-Xavier Vialard
http://arxiv.org/abs/2101.05380v1
• [math.ST]From Smooth Wasserstein Distance to Dual Sobolev Norm: Empirical Approximation and Statistical Applications
Sloan Nietert, Ziv Goldfeld, Kengo Kato
http://arxiv.org/abs/2101.04039v2
• [math.ST]Hidden Markov chains and fields with observations in Riemannian manifolds
Salem Said, Nicolas Le Bihan, Jonathan H. Manton
http://arxiv.org/abs/2101.03801v2
• [math.ST]Kernel-based ANOVA decomposition and Shapley effects — Application to global sensitivity analysis
Sébastien da Veiga
http://arxiv.org/abs/2101.05487v1
• [math.ST]New bounds for -means and information
-means
Gautier Appert, Olivier Catoni
http://arxiv.org/abs/2101.05728v1
• [math.ST]Optimal Clustering in Anisotropic Gaussian Mixture Models
Xin Chen, Anderson Y. Zhang
http://arxiv.org/abs/2101.05402v1
• [math.ST]Optimal designs for comparing regression curves — dependence within and between groups
Kirsten Schorning, Holger Dette
http://arxiv.org/abs/2101.05654v1
• [math.ST]Optimal network online change point localisation
Yi Yu, Oscar Hernan Madrid Padilla, Daren Wang, Alessandro Rinaldo
http://arxiv.org/abs/2101.05477v1
• [q-bio.GN]Feature reduction for machine learning on molecular features: The GeneScore
Alexander Denker, Anastasia Steshina, Theresa Grooss, Frank Ueckert, Sylvia Nürnberg
http://arxiv.org/abs/2101.05546v1
• [stat.AP]Assortativity measures for weighted and directed networks
Yelie Yuan, Jun Yan, Panpan Zhang
http://arxiv.org/abs/2101.05389v1
• [stat.AP]Estimating functional parameters for understanding the impact of weather and government interventions on COVID-19 outbreak
Chih-Li Sung
http://arxiv.org/abs/2101.05350v1
• [stat.ME]A new volatility model: GQARCH-Itô model
Huiling Yuan, Yong Zhou, Lu Xu, Yun Lei Sun, Xiang Yu Cui
http://arxiv.org/abs/2101.05644v1
• [stat.ME]Adaptive shrinkage of smooth functional effects towards a predefined functional subspace
Paul Wiemann, Thomas Kneib
http://arxiv.org/abs/2101.05630v1
• [stat.ME]Agglomerative Hierarchical Clustering for Selecting Valid Instrumental Variables
Nicolas Apfel, Xiaoran Liang
http://arxiv.org/abs/2101.05774v1
• [stat.ME]Bayesian Multiple Index Models for Environmental Mixtures
Glen McGee, Ander Wilson, Thomas F. Webster, Brent A. Coull
http://arxiv.org/abs/2101.05352v1
• [stat.ME]Bayesian inference with tmbstan for a state-space model with VAR(1) state equation
Yihan Cao, Jarle Tufto
http://arxiv.org/abs/2101.05635v1
• [stat.ME]Enhanced Cube Implementation For Highly Stratified Population
Raphaël Jauslin, Esther Eustache, Yves Tillé
http://arxiv.org/abs/2101.05568v1
• [stat.ME]Integrative Learning for Population of Dynamic Networks with Covariates
Suprateek Kundu, Jin Ming, Joe Nocera, Keith M. McGregor
http://arxiv.org/abs/2101.05539v1
• [stat.ME]P-spline smoothed functional ICA of EEG data
Marc Vidal, Mattia Rosso, Ana M. Aguilera
http://arxiv.org/abs/2101.05769v1
• [stat.ME]The -family of covariance functions: A Matérn analogue for modeling random fields on spheres
Alfredo Alegría, Francisco Cuevas-Pacheco, Peter Diggle, Emilio Porcu
http://arxiv.org/abs/2101.05394v1
• [stat.ML]Convex Smoothed Autoencoder-Optimal Transport model
Aratrika Mustafi
http://arxiv.org/abs/2101.05679v1
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