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.DL - 数字图书馆 cs.DS - 数据结构与算法 cs.GR - 计算机图形学 cs.GT - 计算机科学与博弈论 cs.HC - 人机接口 cs.IR - 信息检索 cs.IT - 信息论 cs.LG - 自动学习 cs.LO - 计算逻辑 cs.MA - 多代理系统 cs.NE - 神经与进化计算 cs.RO - 机器人学 cs.SE - 软件工程 cs.SI - 社交网络与信息网络 econ.EM - 计量经济学 eess.IV - 图像与视频处理 eess.SP - 信号处理 math.FA - 泛函演算 math.NA - 数值分析 math.OC - 优化与控制 math.ST - 统计理论 physics.comp-ph - 计算物理学 physics.data-an - 数据分析、 统计和概率 physics.ins-det - 仪器和探测器 physics.soc-ph - 物理学与社会 q-bio.QM - 定量方法 quant-ph - 量子物理 stat.AP - 应用统计 stat.CO - 统计计算 stat.ME - 统计方法论 stat.ML - (统计)机器学习
• [cs.AI]A Taxonomy of Explainable Bayesian Networks
• [cs.AI]Causality and independence in perfectly adapted dynamical systems
• [cs.AI]Disembodied Machine Learning: On the Illusion of Objectivity in NLP
• [cs.AI]Embedding Symbolic Temporal Knowledge into Deep Sequential Models
• [cs.AI]Strategic Argumentation Dialogues for Persuasion: Framework and Experiments Based on Modelling the Beliefs and Concerns of the Persuadee
• [cs.AR]Rethinking Floating Point Overheads for Mixed Precision DNN Accelerators
• [cs.CL]”This item is a glaxefw, and this is a glaxuzb”: Compositionality Through Language Transmission, using Artificial Neural Networks
• [cs.CL]A Neural Few-Shot Text Classification Reality Check
• [cs.CL]A transformer based approach for fighting COVID-19 fake news
• [cs.CL]Attention Guided Dialogue State Tracking with Sparse Supervision
• [cs.CL]BERTaú: Itaú BERT for digital customer service
• [cs.CL]BOLD: Dataset and Metrics for Measuring Biases in Open-Ended Language Generation
• [cs.CL]DRAG: Director-Generator Language Modelling Framework for Non-Parallel Author Stylized Rewriting
• [cs.CL]Does Typological Blinding Impede Cross-Lingual Sharing?
• [cs.CL]El Volumen Louder Por Favor: Code-switching in Task-oriented Semantic Parsing
• [cs.CL]Enhancing Sequence-to-Sequence Neural Lemmatization with External Resources
• [cs.CL]Explaining Natural Language Processing Classifiers with Occlusion and Language Modeling
• [cs.CL]Identifying COVID-19 Fake News in Social Media
• [cs.CL]Joint Coreference Resolution and Character Linking for Multiparty Conversation
• [cs.CL]Knowledge-driven Natural Language Understanding of English Text and its Applications
• [cs.CL]LESA: Linguistic Encapsulation and Semantic Amalgamation Based Generalised Claim Detection from Online Content
• [cs.CL]LOME: Large Ontology Multilingual Extraction
• [cs.CL]Mining Large-Scale Low-Resource Pronunciation Data From Wikipedia
• [cs.CL]Modeling Context in Answer Sentence Selection Systems on a Latency Budget
• [cs.CL]Neural Sentence Ordering Based on Constraint Graphs
• [cs.CL]ProtoDA: Efficient Transfer Learning for Few-Shot Intent Classification
• [cs.CL]Semi-automatic Generation of Multilingual Datasets for Stance Detection in Twitter
• [cs.CL]Syntactic Nuclei in Dependency Parsing — A Multilingual Exploration
• [cs.CL]The Role of Syntactic Planning in Compositional Image Captioning
• [cs.CL]Us vs. Them: A Dataset of Populist Attitudes, News Bias and Emotions
• [cs.CL]Weakly Supervised Neuro-Symbolic Module Networks for Numerical Reasoning
• [cs.CR]Detecting Malicious Accounts showing Adversarial Behavior in Permissionless Blockchains
• [cs.CR]Robust Android Malware Detection System against Adversarial Attacks using Q-Learning
• [cs.CR]S++: A Fast and Deployable Secure-Computation Framework for Privacy-Preserving Neural Network Training
• [cs.CR]Security, Fault Tolerance, and Communication Complexity in Distributed Systems
• [cs.CR]Website Fingerprinting on Early QUIC Traffic
• [cs.CV]Assessing the applicability of Deep Learning-based visible-infrared fusion methods for fire imagery
• [cs.CV]Augmenting Proposals by the Detector Itself
• [cs.CV]CNN with large memory layers
• [cs.CV]COMPAS: Representation Learning with Compositional Part Sharing for Few-Shot Classification
• [cs.CV]DOC2PPT: Automatic Presentation Slides Generation from Scientific Docum
4c0f
ents
• [cs.CV]Discriminative Appearance Modeling with Multi-track Pooling for Real-time Multi-object Tracking
• [cs.CV]Domain Adaptation by Topology Regularization
• [cs.CV]Exploring Cross-Image Pixel Contrast for Semantic Segmentation
• [cs.CV]Fusion Moves for Graph Matching
• [cs.CV]Generative Multi-Label Zero-Shot Learning
• [cs.CV]HDIB1M — Handwritten Document Image Binarization 1 Million Dataset
• [cs.CV]Multi-Modal Aesthetic Assessment for MObile Gaming Image
• [cs.CV]Neural Architecture Search with Random Labels
• [cs.CV]Object Detection Made Simpler by Eliminating Heuristic NMS
• [cs.CV]PIG-Net: Inception based Deep Learning Architecture for 3D Point Cloud Segmentation
• [cs.CV]Playable Video Generation
• [cs.CV]Puzzle-CAM: Improved localization via matching partial and full features
• [cs.CV]Reducing ReLU Count for Privacy-Preserving CNN Speedup
• [cs.CV]Rethinking Rotated Object Detection with Gaussian Wasserstein Distance Loss
• [cs.CV]Tokens-to-Token ViT: Training Vision Transformers from Scratch on ImageNet
• [cs.CV]VAE^2: Preventing Posterior Collapse of Variational Video Predictions in the Wild
• [cs.CV]Vx2Text: End-to-End Learning of Video-Based Text Generation From Multimodal Inputs
• [cs.CY]Eye: Program Visualizer for CS2
• [cs.CY]Large Scale Analysis of Multitasking Behavior During Remote Meetings
• [cs.CY]Making Responsible AI the Norm rather than the Exception
• [cs.CY]Moral and Social Ramifications of Autonomous Vehicles
• [cs.DC]Formal Definitions of Memory Consistency Models
• [cs.DL]BIP! DB: A Dataset of Impact Measures for Scientific Publications
• [cs.DS]A New Approach to Capacity Scaling Augmented With Unreliable Machine Learning Predictions
• [cs.DS]Random Graph Matching with Improved Noise Robustness
• [cs.GR]GPU Optimization for High-Quality Kinetic Fluid Simulation
• [cs.GT]Equilibrium Learning in Combinatorial Auctions: Computing Approximate Bayesian Nash Equilibria via Pseudogradient Dynamics
• [cs.HC]AHMoSe: A Knowledge-Based Visual Support System for Selecting Regression Machine Learning Models
• [cs.HC]WallStreetBets: Positions or Ban
• [cs.IR]A Graph-based Relevance Matching Model for Ad-hoc Retrieval
• [cs.IR]A Survey on Personality-Aware Recommendation Systems
• [cs.IR]Perceptions of Diversity in Electronic Music: the Impact of Listener, Artist, and Track Characteristics
• [cs.IT]A note on tight projective 2-designs
• [cs.IT]Adversarial Attacks on Deep Learning Based Power Allocation in a Massive MIMO Network
• [cs.IT]Construction of binary LCD codes, ternary LCD codes and quaternary Hermitian LCD codes
• [cs.IT]Edge Federated Learning Via Unit-Modulus Over-The-Air Computation (Extended Version)
• [cs.IT]Improved Rate-Energy Trade-off For SWIPT Using Chordal Distance Decomposition In Interference Alignment Networks
• [cs.IT]Information contraction in noisy binary neural networks and its implications
• [cs.IT]Joint Transmission Scheme and Coded Content Placement in Cluster-centric UAV-aided Cellular Networks
• [cs.IT]List-Decodable Coded Computing: Breaking the Adversarial Toleration Barrier
• [cs.IT]Modeling Ground-to-Air Path Loss for Millimeter Wave UAV Networks
• [cs.IT]Non-Asymptotic Converse Bounds Via Auxiliary Channels
• [cs.IT]On Mutual Information Analysis of Infectious Disease Transmission via Particle Propagation
• [cs.IT]On the Performance of Large-Scale Wireless Networks in the Finite Block-Length Regime
• [cs.IT]Performance Comparison between Reconfigurable Intelligent Surface and Relays: Theoretical Methods and a Perspective from Operator
• [cs.IT]Predictive Control and Communication Co-Design via Two-Way Gaussian Process Regression and AoI-Aware Scheduling
• [cs.IT]Private DNA Sequencing: Hiding Information in Discrete Noise
• [cs.IT]Random-Mode Frank-Wolfe Algorithm for Tensor Completion in Wireless Edge Caching
• [cs.IT]Rate-Energy Balanced Precoding Design for SWIPT based Two-Way Relay Systems
• [cs.IT]Reinforcement Learning based Per-antenna Discrete Power Control for Massive MIMO Systems
• [cs.IT]The Most Informative Order Statistic and its Application to Image Denoising
• [cs.LG]A Hybrid 2-stage Neural Optimization for Pareto Front Extraction
• [cs.LG]A Machine Learning Challenge for Prognostic Modelling in Head and Neck Cancer Using Multi-modal Data
• [cs.LG]Acting in Delayed Environments with Non-Stationary Markov Policies
• [cs.LG]AdaSpring: Context-adaptive and Runtime-evolutionary Deep Model Compression for Mobile Applications
• [cs.LG]Adaptive Decision Forest: An Incremental Machine Learning Framework
• [cs.LG]Adversarial Machine Learning Attacks on Condition-Based Maintenance Capabilities
• [cs.LG]An Analysis Of Protected Health Information Leakage In Deep-Learning Based De-Identification Algorithms
• [cs.LG]Automatic design of novel potential 3CL and PL
inhibitors
• [cs.LG]Autoregressive Denoising Diffusion Models for Multivariate Probabilistic Time Series Forecasting
• [cs.LG]BENDR: using transformers and a contrastive self-supervised learning task to learn from massive amounts of EEG data
• [cs.LG]Better sampling in explanation methods can prevent dieselgate-like deception
• [cs.LG]Copula-based conformal prediction for Multi-Target Regression
• [cs.LG]Covert Model Poisoning Against Federated Learning: Algorithm Design and Optimization
• [cs.LG]Deep learning via LSTM models for COVID-19 infection forecasting in India
• [cs.LG]Dopamine: Differentially Private Secure Federated Learning on Medical Data
• [cs.LG]Faster Kernel Interpolation for Gaussian Processes
• [cs.LG]Federated Multi-Armed Bandits
• [cs.LG]Generalising via Meta-Examples for Continual Learning in the Wild
• [cs.LG]Hadamard Powers and the Identification of Mixtures of Products
• [cs.LG]Improving Neural Network Robustness through Neighborhood Preserving Layers
• [cs.LG]Increasing the Confidence of Deep Neural Networks by Coverage Analysis
• [cs.LG]Interpreting and Unifying Graph Neural Networks with An Optimization Framework
• [cs.LG]Learning Structural Edits via Incremental Tree Transformations
• [cs.LG]Low Complexity Approximate Bayesian Logistic Regression for Sparse Online Learning
• [cs.LG]Machine learning for cloud resources management — An overview
• [cs.LG]Model-Based Policy Search Using Monte Carlo Gradient Estimation with Real Systems Application
• [cs.LG]On the Origin of Implicit Regularization in Stochastic Gradient Descent
• [cs.LG]On the mapping between Hopfield networks and Restricted Boltzmann Machines
• [cs.LG]Overestimation learning with guarantees
• [cs.LG]PSpan:Mining Frequent Subnets of Petri Nets
• [cs.LG]Self-Attention Meta-Learner for Continual Learning
• [cs.LG]Self-supervised Cross-silo Federated Neural Architecture Search
• [cs.LG]The Hidden Tasks of Generative Adversarial Networks: An Alternative Perspective on GAN Training
• [cs.LG]tf.data: A Machine Learning Data Processing Framework
• [cs.LO]Efficiency of Query Evaluation Under Guarded TGDs: The Unbounded Arity Case
• [cs.MA]Exploring the Impact of Tunable Agents in Sequential Social Dilemmas
• [cs.NE]Coefficients’ Settings in Particle Swarm Optimization: Insight and Guidelines
• [cs.NE]Evolutionary Neural Architecture Search Supporting Approximate Multipliers
• [cs.NE]Reconstructing Perceptive Images from Brain Activity by Shape-Semantic GAN
• [cs.NE]Stagnation Detection with Randomized Local Search
• [cs.RO]Acoustic Communication and Sensing for Inflatable Modular Soft Robots
• [cs.RO]Evolutionary Co-Design of Morphology and Control of Soft Tensegrity Modular Robots with Programmable Stiffness
• [cs.RO]SwingBot: Learning Physical Features from In-hand Tactile Exploration for Dynamic Swing-up Manipulation
• [cs.RO]Visualization of Nonlinear Programming for Robot Motion Planning
• [cs.SE]A Spatial-Temporal Graph Neural Network Framework for Automated Software Bug Triaging
• [cs.SI]CML-COVID: A Large-Scale COVID-19 Twitter Dataset with Latent Topics, Sentiment and Location Information
• [cs.SI]Contagion-Preserving Network Sparsifiers: Exploring Epidemic Edge Importance Utilizing Effective Resistance
• [econ.EM]Choice modelling in the age of machine learning
• [eess.IV]A Multi-Scale Conditional Deep Model for Tumor Cell Ratio Counting
• [eess.IV]An Explainable AI System for Automated COVID-19 Assessment and Lesion Categorization from CT-scans
• [eess.IV]Automated femur segmentation from computed tomography images using a deep neural network
• [eess.IV]Chronological age estimation of lateral cephalometric radiographs with deep learning
• [eess.IV]Easy-GT: Open-Source Software to Facilitate Making the Ground Truth for White Blood Cells Nucleus
• [eess.IV]Neural Particle Image Velocimetry
• [eess.SP]An Overview of Machine Learning Techniques for Radiowave Propagation Modeling
• [eess.SP]Multi-Antenna Joint Radar and Communications: Precoder Optimization and Weighted Sum-Rate vs Probing Power Tradeoff
• [math.FA]Approximation with Tensor Networks. Part III: Multivariate Approximation
• [math.NA]Non-intrusive reduced order modeling of poroelasticity of heterogeneous media based on a discontinuous Galerkin approximation
• [math.NA]POD-DL-ROM: enhancing deep learning-based reduced order models for nonlinear parametrized PDEs by proper orthogonal decomposition
• [math.OC]Potential Function-based Framework for Making the Gradients Small in Convex and Min-Max Optimization
• [math.ST]Ellipse Combining with Unknown Cross Ellipse Correlations
• [math.ST]Reproducing kernel Hilbert spaces, polynomials and the classical moment problems
• [physics.comp-ph]Porting WarpX to GPU-accelerated platforms
• [physics.data-an]Development of a Vertex Finding Algorithm using Recurrent Neural Network
• [physics.data-an]Inference of stochastic time series with missing data
• [physics.ins-det]Tackling the muon identification in water Cherenkov detectors problem for the future Southern Wide-field Gamma-ray Observatory by means of Machine Learning
• [physics.soc-ph]Detecting Hidden Layers from Spreading Dynamics on Complex Networks
• [q-bio.QM]G-MIND: An End-to-End Multimodal Imaging-Genetics Framework for Biomarker Identification and Disease Classification
• [quant-ph]Advantages and Bottlenecks of Quantum Machine Learning for Remote Sensing
• [quant-ph]Entanglement-assisted multiple-access channels: capacity regions and protocol designs
• [quant-ph]LOCCNet: a machine learning framework for distributed quantum information processing
• [stat.AP]Seroprevalence of SARS-CoV-2 antibodies in South Korea
• [stat.AP]The fraud loss for selecting the model complexity in fraud detection
• [stat.CO]Sequential Monte Carlo algorithms for agent-based models of disease transmission
• [stat.ME]A Kernel-Based Neural Network for High-dimensional Genetic Risk Prediction Analysis
• [stat.ME]Lévy Adaptive B-spline Regression via Overcomplete Systems
• [stat.ME]Robust Extrinsic Regression Analysis for Manifold Valued Data
• [stat.ME]The AL-Gaussian Distribution as the Descriptive Model for the Internal Proactive Inhibition in the Standard Stop Signal Task
• [stat.ML]Interpolating Classifiers Make Few Mistakes
• [stat.ML]Learning Representations for Individualized Organ Transplantation Allocation
• [stat.ML]On Statistical Bias In Active Learning: How and When To Fix It
·····································
• [cs.AI]A Taxonomy of Explainable Bayesian Networks
Iena Petronella Derks, Alta de Waal
http://arxiv.org/abs/2101.11844v1
• [cs.AI]Causality and independence in perfectly adapted dynamical systems
Tineke Blom, Joris M. Mooij
http://arxiv.org/abs/2101.11885v1
• [cs.AI]Disembodied Machine Learning: On the Illusion of Objectivity in NLP
Zeerak Waseem, Smarika Lulz, Joachim Bingel, Isabelle Augenstein
http://arxiv.org/abs/2101.11974v1
• [cs.AI]Embedding Symbolic Temporal Knowledge into Deep Sequential Models
Yaqi Xie, Fan Zhou, Harold Soh
http://arxiv.org/abs/2101.11981v1
• [cs.AI]Strategic Argumentation Dialogues for Persuasion: Framework and Experiments Based on Modelling the Beliefs and Concerns of the Persuadee
Emmanuel Hadoux, Anthony Hunter, Sylwia Polberg
http://arxiv.org/abs/2101.11870v1
• [cs.AR]Rethinking Floating Point Overheads for Mixed Precision DNN Accelerators
Hamzah Abdel-Aziz, Ali Shafiee, Jong Hoon Shin, Ardavan Pedram, Joseph H. Hassoun
http://arxiv.org/abs/2101.11748v1
• [cs.CL]“This item is a glaxefw, and this is a glaxuzb”: Compositionality Through Language Transmission, using Artificial Neural Networks
Hugh Perkins
http://arxiv.org/abs/2101.11739v1
• [cs.CL]A Neural Few-Shot Text Classification Reality Check
Thomas Dopierre, Christophe Gravier, Wilfried Logerais
http://arxiv.org/abs/2101.12073v1
• [cs.CL]A transformer based approach for fighting COVID-19 fake news
S. M. Sadiq-Ur-Rahman Shifath, Mohammad Faiyaz Khan, Md. Saiful Islam
http://arxiv.org/abs/2101.12027v1
• [cs.CL]Attention Guided Dialogue State Tracking with Sparse Supervision
Shuailong Liang, Lahari Poddar, Gyuri Szarvas
http://arxiv.org/abs/2101.11958v1
• [cs.CL]BERTaú: Itaú BERT for digital customer service
Paulo Finardi, José Dié Viegas, Gustavo T. Ferreira, Alex F. Mansano, Vinicius F. Caridá
http://arxiv.org/abs/2101.12015v1
• [cs.CL]BOLD: Dataset and Metrics for Measuring Biases in Open-Ended Language Generation
Jwala Dhamala, Tony Sun, Varun Kumar, Satyapriya Krishna, Yada Pruksachatkun, Kai-Wei Chang, Rahul Gupta
http://arxiv.org/abs/2101.11718v1
• [cs.CL]DRAG: Director-Generator Language Modelling Framework for Non-Parallel Author Stylized Rewriting
Hrituraj Singh, Gaurav Verma, Aparna Garimella, Balaji Vasan Srinivasan
http://arxiv.org/abs/2101.11836v1
• [cs.CL]Does Typological Blinding Impede Cross-Lingual Sharing?
Johannes Bjerva, Isabelle Augenstein
http://arxiv.org/abs/2101.11888v1
• [cs.CL]El Volumen Louder Por Favor: Code-switching in Task-oriented Semantic Parsing
Arash Einolghozati, Abhinav Arora, Lorena Sainz-Maza Lecanda, Anuj Kumar, Sonal Gupta
http://arxiv.org/abs/2101.10524v3
• [cs.CL]Enhancing Sequence-to-Sequence Neural Lemmatization with External Resources
Kirill Milintsevich, Kairit Sirts
http://arxiv.org/abs/2101.12056v1
• [cs.CL]Explaining Natural Language Processing Classifiers with Occlusion and Language Modeling
David Harbecke
http://arxiv.org/abs/2101.11889v1
• [cs.CL]Identifying COVID-19 Fake News in Social Media
Tathagata Raha, Vijayasaradhi Indurthi, Aayush Upadhyaya, Jeevesh Kataria, Pramud Bommakanti, Vikram Keswani, Vasudeva Varma
http://arxiv.org/abs/2101.11954v1
• [cs.CL]Joint Coreference Resolution and Character Linking for Multiparty Conversation
Jiaxin Bai, Hongming Zhang, Yangqiu Song, Kun Xu
http://arxiv.org/abs/2101.11204v2
• [cs.CL]Knowledge-driven Natural Language Understanding of English Text and its Applications
Kinjal Basu, Sarat Varanasi, Farhad Shakerin, Joaquin Arias, Gopal Gupta
http://arxiv.org/abs/2101.11707v1
• [cs.CL]LESA: Linguistic Encapsulation and Semantic Amalgamation Based Generalised Claim Detection from Online Content
Shreya Gupta, Parantak Singh, Megha Sundriyal, Md Shad Akhtar, Tanmoy Chakraborty
http://arxiv.org/abs/2101.11891v1
• [cs.CL]LOME: Large Ontology Multilingual Extraction
Patrick Xia, Guanghui Qin, Siddharth Vashishtha, Yunmo Chen, Tongfei Chen, Chandler May, Craig Harman, Kyle Rawlins, Aaron Steven White, Benjamin Van Durme
http://arxiv.org/abs/2101.12175v1
• [cs.CL]Mining Large-Scale Low-Resource Pronunciation Data From Wikipedia
Tania Chakraborty, Manasa Prasad, Theresa Breiner, Sandy Ritchie, Daan van Esch
http://arxiv.org/abs/2101.11575v1
• [cs.CL]Modeling Context in Answer Sentence Selection Systems on a Latency Budget
Rujun Han, Luca Soldaini, Alessandro Moschitti
http://arxiv.org/abs/2101.12093v1
• [cs.CL]Neural Sentence Ordering Based on Constraint Graphs
Yutao Zhu, Kun Zhou, Jian-Yun Nie, Shengchao Liu, Zhicheng Dou
http://arxiv.org/abs/2101.11178v2
• [cs.CL]ProtoDA: Efficient Transfer Learning for Few-Shot Intent Classification
Manoj Kumar, Varun Kumar, Hadrien Glaude, Cyprien delichy, Aman Alok, Rahul Gupta
http://arxiv.org/abs/2101.11753v1
• [cs.CL]Semi-automatic Generation of Multilingual Datasets for Stance Detection in Twitter
Elena Zotova, Rodrigo Agerri, German Rigau
http://arxiv.org/abs/2101.11978v1
• [cs.CL]Syntactic Nuclei in Dependency Parsing — A Multilingual Exploration
Ali Basirat, Joakim Nivre
http://arxiv.org/abs/2101.11959v1
• [cs.CL]The Role of Syntactic Planning in Compositional Image Captioning
Emanuele Bugliarello, Desmond Elliott
http://arxiv.org/abs/2101.11911v1
• [cs.CL]Us vs. Them: A Dataset of Populist Attitudes, News Bias and Emotions
Pere-Lluís Huguet Cabot, David Abadi, Agneta Fischer, Ekaterina Shutova
http://arxiv.org/abs/2101.11956v1
• [cs.CL]Weakly Supervised Neuro-Symbolic Module Networks for Numerical Reasoning
Amrita Saha, Shafiq Joty, Steven C. H. Hoi
http://arxiv.org/abs/2101.11802v1
• [cs.CR]Detecting Malicious Accounts showing Adversarial Behavior in Permissionless Blockchains
Rachit Agarwal, Tanmay Thapliyal, Sandeep K. Shukla
http://arxiv.org/abs/2101.11915v1
• [cs.CR]Robust Android Malware Detection System against Adversarial Attacks using Q-Learning
Hemant Rathore, Sanjay K. Sahay, Piyush Nikam, Mohit Sewak
http://arxiv.org/abs/2101.12031v1
• [cs.CR]S++: A Fast and Deployable Secure-Computation Framework for Privacy-Preserving Neural Network Training
Prashanthi Ramachandran, Shivam Agarwal, Arup Mondal, Aastha Shah, Debayan Gupta
http://arxiv.org/abs/2101.12078v1
• [cs.CR]Security, Fault Tolerance, and Communication Complexity in Distributed Systems
Donald Rozinak Beaver
http://arxiv.org/abs/2101.12143v1
• [cs.CR]Website Fingerprinting on Early QUIC Traffic
Pengwei Zhan, Liming Wang, Yi Tang
http://arxiv.org/abs/2101.11871v1
• [cs.CV]Assessing the applicability of Deep Learning-based visible-infrared fusion methods for fire imagery
J. F. Ciprián-Sánchez, G. Ochoa-Ruiz, M. Gonzalez-Mendoza, L. Rossi
http://arxiv.org/abs/2101.11745v1
• [cs.CV]Augmenting Proposals by the Detector Itself
Xiaopei Wan, Zhenhua Guo, Chao He, Yujiu Yang, Fangbo Tao
http://arxiv.org/abs/2101.11789v1
• [cs.CV]CNN with large memory layers
Rasul Karimov, Victor Lempitsky
http://arxiv.org/abs/2101.11685v1
• [cs.CV]COMPAS: Representation Learning with Compositional Part Sharing for Few-Shot Classification
Ju He, Adam Kortylewski, Alan Yuille
http://arxiv.org/abs/2101.11878v1
• [cs.CV]DOC2PPT: Automatic Presentation Slides Generation from Scientific Docum
4c0f
ents
Tsu-Jui Fu, William Yang Wang, Daniel McDuff, Yale Song
http://arxiv.org/abs/2101.11796v1
• [cs.CV]Discriminative Appearance Modeling with Multi-track Pooling for Real-time Multi-object Tracking
Chanho Kim, Li Fuxin, Mazen Alotaibi, James M. Rehg
http://arxiv.org/abs/2101.12159v1
• [cs.CV]Domain Adaptation by Topology Regularization
Deborah Weeks, Samuel Rivera
http://arxiv.org/abs/2101.12102v1
• [cs.CV]Exploring Cross-Image Pixel Contrast for Semantic Segmentation
Wenguan Wang, Tianfei Zhou, Fisher Yu, Jifeng Dai, Ender Konukoglu, Luc Van Gool
http://arxiv.org/abs/2101.11939v1
• [cs.CV]Fusion Moves for Graph Matching
Lisa Hutschenreiter, Stefan Haller, Lorenz Feineis, Carsten Rother, Dagmar Kainmüller, Bogdan Savchynskyy
http://arxiv.org/abs/2101.12085v1
• [cs.CV]Generative Multi-Label Zero-Shot Learning
Akshita Gupta, Sanath Narayan, Salman Khan, Fahad Shahbaz Khan, Ling Shao, Joost van de Weijer
http://arxiv.org/abs/2101.11606v2
• [cs.CV]HDIB1M — Handwritten Document Image Binarization 1 Million Dataset
Kaustubh Sadekar, Prajwal Singh, Shanmuganathan Raman
http://arxiv.org/abs/2101.11674v1
• [cs.CV]Multi-Modal Aesthetic Assessment for MObile Gaming Image
Zhenyu Lei, Yejing Xie, Suiyi Ling, Andreas Pastor, Junle Wang, Patrick Le Callet
http://arxiv.org/abs/2101.11700v1
• [cs.CV]Neural Architecture Search with Random Labels
Xuanyang Zhang, Pengfei Hou, Xiangyu Zhang, Jian Sun
http://arxiv.org/abs/2101.11834v1
• [cs.CV]Object Detection Made Simpler by Eliminating Heuristic NMS
Qiang Zhou, Chaohui Yu, Chunhua Shen, Zhibin Wang, Hao Li
http://arxiv.org/abs/2101.11782v1
• [cs.CV]PIG-Net: Inception based Deep Learning Architecture for 3D Point Cloud Segmentation
Sindhu Hegde, Shankar Gangisetty
http://arxiv.org/abs/2101.11987v1
• [cs.CV]Playable Video Generation
Willi Menapace, Stéphane Lathuilière, Sergey Tulyakov, Aliaksandr Siarohin, Elisa Ricci
http://arxiv.org/abs/2101.12195v1
• [cs.CV]Puzzle-CAM: Improved localization via matching partial and full features
Sanghyun Jo, In-Jae Yu
http://arxiv.org/abs/2101.11253v2
• [cs.CV]Reducing ReLU Count for Privacy-Preserving CNN Speedup
Inbar Helbitz, Shai Avidan
http://arxiv.org/abs/2101.11835v1
• [cs.CV]Rethinking Rotated Object Detection with Gaussian Wasserstein Distance Loss
Xue Yang, Junchi Yan, Qi Ming, Wentao Wang, Xiaopeng Zhang, Qi Tian
http://arxiv.org/abs/2101.11952v1
• [cs.CV]Tokens-to-Token ViT: Training Vision Transformers from Scratch on ImageNet
Li Yuan, Yunpeng Chen, Tao Wang, Weihao Yu, Yujun Shi, Francis EH Tay, Jiashi Feng, Shuicheng Yan
http://arxiv.org/abs/2101.11986v1
• [cs.CV]VAE^2: Preventing Posterior Collapse of Variational Video Predictions in the Wild
Yizhou Zhou, Chong Luo, Xiaoyan Sun, Zheng-Jun Zha, Wenjun Zeng
http://arxiv.org/abs/2101.12050v1
• [cs.CV]Vx2Text: End-to-End Learning of Video-Based Text Generation From Multimodal Inputs
Xudong Lin, Gedas Bertasius, Jue Wang, Shih-Fu Chang, Devi Parikh, Lorenzo Torresani
http://arxiv.org/abs/2101.12059v1
• [cs.CY]Eye: Program Visualizer for CS2
Aman Bansal, Preey Shah, Sahil Shah
http://arxiv.org/abs/2101.12089v1
• [cs.CY]Large Scale Analysis of Multitasking Behavior During Remote Meetings
Hancheng Cao, Chia-Jung Lee, Shamsi Iqbal, Mary Czerwinski, Priscilla Wong, Sean Rintel, Brent Hecht, Jaime Teevan, Longqi Yang
http://arxiv.org/abs/2101.11865v1
• [cs.CY]Making Responsible AI the Norm rather than the Exception
Abhishek Gupta
http://arxiv.org/abs/2101.11832v1
• [cs.CY]Moral and Social Ramifications of Autonomous Vehicles
Veljko Dubljević, Sean Douglas, Jovan Milojevich, Nirav Ajmeri, William A. Bauer, George F. List, Munindar P. Singh
http://arxiv.org/abs/2101.11775v1
• [cs.DC]Formal Definitions of Memory Consistency Models
Jordi Bataller Mascarell
http://arxiv.org/abs/rg/abs/2101.09527v1
• [cs.DL]BIP! DB: A Dataset of Impact Measures for Scientific Publications
Thanasis Vergoulis, Ilias Kanellos, Claudio Atzori, Andrea Mannocci, Serafeim Chatzopoulos, Sandro La Bruzzo, Natalia Manola, Paolo Manghi
http://arxiv.org/abs/2101.12001v1
• [cs.DS]A New Approach to Capacity Scaling Augmented With Unreliable Machine Learning Predictions
Daan Rutten, Debankur Mukherjee
http://arxiv.org/abs/2101.12160v1
• [cs.DS]Random Graph Matching with Improved Noise Robustness
Cheng Mao, Mark Rudelson, Konstantin Tikhomirov
http://arxiv.org/abs/2101.11783v1
• [cs.GR]GPU Optimization for High-Quality Kinetic Fluid Simulation
Yixin Chen, Wei Li, Rui Fan, Xiaopei Liu
http://arxiv.org/abs/2101.11856v1
• [cs.GT]Equilibrium Learning in Combinatorial Auctions: Computing Approximate Bayesian Nash Equilibria via Pseudogradient Dynamics
Stefan Heidekrüger, Paul Sutterer, Nils Kohring, Maximilian Fichtl, Martin Bichler
http://arxiv.org/abs/2101.11946v1
• [cs.HC]AHMoSe: A Knowledge-Based Visual Support System for Selecting Regression Machine Learning Models
Diego Rojo, Nyi Nyi Htun, Denis Parra, Robin De Croon, Katrien Verbert
http://arxiv.org/abs/2101.11970v1
• [cs.HC]WallStreetBets: Positions or Ban
Christian Boylston, Beatriz Palacios, Plamen Tassev, Amy Bruckman
http://arxiv.org/abs/2101.12110v1
• [cs.IR]A Graph-based Relevance Matching Model for Ad-hoc Retrieval
Yufeng Zhang, Jinghao Zhang, Zeyu Cui, Shu Wu, Liang Wang
http://arxiv.org/abs/2101.11873v1
• [cs.IR]A Survey on Personality-Aware Recommendation Systems
Sahraoui Dhelim, Nyothiri Aung, Mohammed Amine Bouras, Huansheng Ning, Erik Cambria
http://arxiv.org/abs/2101.12153v1
• [cs.IR]Perceptions of Diversity in Electronic Music: the Impact of Listener, Artist, and Track Characteristics
Lorenzo Porcaro, Emilia Gómez, Carlos Castillo
http://arxiv.org/abs/2101.11916v1
• [cs.IT]A note on tight projective 2-designs
Joseph W. Iverson, Emily J. King, Dustin G. Mixon
http://arxiv.org/abs/2101.11756v1
• [cs.IT]Adversarial Attacks on Deep Learning Based Power Allocation in a Massive MIMO Network
B. R. Manoj, Meysam Sadeghi, Erik G. Larsson
http://arxiv.org/abs/2101.12090v1
• [cs.IT]Construction of binary LCD codes, ternary LCD codes and quaternary Hermitian LCD codes
Masaaki Harada
http://arxiv.org/abs/2101.11821v1
• [cs.IT]Edge Federated Learning Via Unit-Modulus Over-The-Air Computation (Extended Version)
Shuai Wang, Yuncong Hong, Rui Wang, Qi Hao, Yik-Chung Wu, Derrick Wing Kwan Ng
http://arxiv.org/abs/2101.12051v1
• [cs.IT]Improved Rate-Energy Trade-off For SWIPT Using Chordal Distance Decomposition In Interference Alignment Networks
Navneet Garg, Avinash Rudraksh, Govind Sharma, Tharmalingam Ratnarajah
http://arxiv.org/abs/2101.12161v1
• [cs.IT]Information contraction in noisy binary neural networks and its implications
Chuteng Zhou, Quntao Zhuang, Matthew Mattina, Paul N. Whatmough
http://arxiv.org/abs/2101.11750v1
• [cs.IT]Joint Transmission Scheme and Coded Content Placement in Cluster-centric UAV-aided Cellular Networks
Zohreh HajiAkhondi-Meybodi, Arash Mohammadi, Jamshid Abouei, Ming Hou
http://arxiv.org/abs/2101.11787v1
• [cs.IT]List-Decodable Coded Computing: Breaking the Adversarial Toleration Barrier
Mahdi Soleymani, Ramy E. Ali, Hessam Mahdavifar, A. Salman Avestimehr
http://arxiv.org/abs/2101.11653v1
• [cs.IT]Modeling Ground-to-Air Path Loss for Millimeter Wave UAV Networks
Hazim Shakhatreh, Waed Malkawi, Ahmad Sawalmeh, Muhannad Almutiry, Ali Alenezi
http://arxiv.org/abs/2101.12024v1
• [cs.IT]Non-Asymptotic Converse Bounds Via Auxiliary Channels
Ioannis Papoutsidakis, Robert J. Piechocki, Angela Doufexi
http://arxiv.org/abs/2101.11490v2
• [cs.IT]On Mutual Information Analysis of Infectious Disease Transmission via Particle Propagation
Peter Adam Hoeher, Martin Damrath, Sunasheer Bhattacharjee, Max Schurwanz
http://arxiv.org/abs/2101.12121v1
• [cs.IT]On the Performance of Large-Scale Wireless Networks in the Finite Block-Length Regime
Nourhan Hesham, Anas Chaaban
http://arxiv.org/abs/2101.11762v1
• [cs.IT]Performance Comparison between Reconfigurable Intelligent Surface and Relays: Theoretical Methods and a Perspective from Operator
Qi Gu, Dan Wu, Xin Su, Jing Jin, Yifei Yuan, Jiangzhou Wang
http://arxiv.org/abs/2101.12091v1
• [cs.IT]Predictive Control and Communication Co-Design via Two-Way Gaussian Process Regression and AoI-Aware Scheduling
Abanoub M. Girgis, Jihong Park, Mehdi Bennis, Mérouane Debbah
http://arxiv.org/abs/2101.11647v1
• [cs.IT]Private DNA Sequencing: Hiding Information in Discrete Noise
Kayvon Mazooji, Roy Dong, Ilan Shomorony
http://arxiv.org/abs/2101.12124v1
• [cs.IT]Random-Mode Frank-Wolfe Algorithm for Tensor Completion in Wireless Edge Caching
Navneet Garg, Tharmalingam Ratnarajah
http://arxiv.org/abs/2101.12146v1
• [cs.IT]Rate-Energy Balanced Precoding Design for SWIPT based Two-Way Relay Systems
Navneet Garg, Junkai Zhang, Tharmalingam Ratnarajah
http://arxiv.org/abs/2101.12169v1
• [cs.IT]Reinforcement Learning based Per-antenna Discrete Power Control for Massive MIMO Systems
Navneet Garg, Mathini Sellathurai, Tharmalingam Ratnarajah
http://arxiv.org/abs/2101.12154v1
• [cs.IT]The Most Informative Order Statistic and its Application to Image Denoising
Alex Dytso, Martina Cardone, Cynthia Rush
http://arxiv.org/abs/2101.11667v1
• [cs.LG]A Hybrid 2-stage Neural Optimization for Pareto Front Extraction
Gurpreet Singh, Soumyajit Gupta, Matthew Lease, Clint Dawson
http://arxiv.org/abs/2101.11684v1
• [cs.LG]A Machine Learning Challenge for Prognostic Modelling in Head and Neck Cancer Using Multi-modal Data
Michal Kazmierski, Mattea Welch, Sejin Kim, Chris McIntosh, Princess Margaret Head, Neck Cancer Group, Katrina Rey-McIntyre, Shao Hui Huang, Tirth Patel, Tony Tadic, Michael Milosevic, Fei-Fei Liu, Andrew Hope, Scott Bratman, Benjam
bf4
in Haibe-Kains
http://arxiv.org/abs/2101.11935v1
• [cs.LG]Acting in Delayed Environments with Non-Stationary Markov Policies
Esther Derman, Gal Dalal, Shie Mannor
http://arxiv.org/abs/2101.11992v1
• [cs.LG]AdaSpring: Context-adaptive and Runtime-evolutionary Deep Model Compression for Mobile Applications
Sicong Liu, Bin Guo, Ke Ma, Zhiwen Yu, Junzhao Du
http://arxiv.org/abs/2101.11800v1
• [cs.LG]Adaptive Decision Forest: An Incremental Machine Learning Framework
Md Geaur Rahman, Md Zahidul Islam
http://arxiv.org/abs/2101.11828v1
• [cs.LG]Adversarial Machine Learning Attacks on Condition-Based Maintenance Capabilities
Hamidreza Habibollahi Najaf Abadi
http://arxiv.org/abs/2101.12097v1
• [cs.LG]An Analysis Of Protected Health Information Leakage In Deep-Learning Based De-Identification Algorithms
Salman Seyedi, Li Xiong, Shamim Nemati, Gari D. Clifford
http://arxiv.org/abs/2101.12099v1
• [cs.LG]Automatic design of novel potential 3CL and PL
inhibitors
Timothy Atkinson, Saeed Saremi, Faustino Gomez, Jonathan Masci
http://arxiv.org/abs/2101.11890v1
• [cs.LG]Autoregressive Denoising Diffusion Models for Multivariate Probabilistic Time Series Forecasting
Kashif Rasul, Calvin Seward, Ingmar Schuster, Roland Vollgraf
http://arxiv.org/abs/2101.12072v1
• [cs.LG]BENDR: using transformers and a contrastive self-supervised learning task to learn from massive amounts of EEG data
Demetres Kostas, Stephane Aroca-Ouellette, Frank Rudzicz
http://arxiv.org/abs/2101.12037v1
• [cs.LG]Better sampling in explanation methods can prevent dieselgate-like deception
Domen Vreš, Marko Robnik Šikonja
http://arxiv.org/abs/2101.11702v1
• [cs.LG]Copula-based conformal prediction for Multi-Target Regression
Soundouss Messoudi, Sébastien Destercke, Sylvain Rousseau
http://arxiv.org/abs/2101.12002v1
• [cs.LG]Covert Model Poisoning Against Federated Learning: Algorithm Design and Optimization
Kang Wei, Jun Li, Ming Ding, Chuan Ma, Yo-Seb Jeon, H. Vincent Poor
http://arxiv.org/abs/2101.11799v1
• [cs.LG]Deep learning via LSTM models for COVID-19 infection forecasting in India
Rohitash Chandra, Ayush Jain, Divyanshu Singh Chauhan
http://arxiv.org/abs/2101.11881v1
• [cs.LG]Dopamine: Differentially Private Secure Federated Learning on Medical Data
Mohammad Malekzadeh, Burak Hasircioglu, Nitish Mital, Kunal Katarya, Mehmet Emre Ozfatura, Deniz Gündüz
http://arxiv.org/abs/2101.11693v1
• [cs.LG]Faster Kernel Interpolation for Gaussian Processes
Mohit Yadav, Daniel Sheldon, Cameron Musco
http://arxiv.org/abs/2101.11751v1
• [cs.LG]Federated Multi-Armed Bandits
Chengshuai Shi, Cong Shen
http://arxiv.org/abs/2101.12204v1
• [cs.LG]Generalising via Meta-Examples for Continual Learning in the Wild
Alessia Bertugli, Stefano Vincenzi, Simone Calderara, Andrea Passerini
http://arxiv.org/abs/2101.12081v1
• [cs.LG]Hadamard Powers and the Identification of Mixtures of Products
Spencer L. Gordon, Leonard J. Schulman
http://arxiv.org/abs/2101.11688v1
• [cs.LG]Improving Neural Network Robustness through Neighborhood Preserving Layers
Bingyuan Liu, Christopher Malon, Lingzhou Xue, Erik Kruus
http://arxiv.org/abs/2101.11766v1
• [cs.LG]Increasing the Confidence of Deep Neural Networks by Coverage Analysis
Giulio Rossolini, Alessandro Biondi, Giorgio Carlo Buttazzo
http://arxiv.org/abs/2101.12100v1
• [cs.LG]Interpreting and Unifying Graph Neural Networks with An Optimization Framework
Meiqi Zhu, Xiao Wang, Chuan Shi, Houye Ji, Peng Cui
http://arxiv.org/abs/2101.11859v1
• [cs.LG]Learning Structural Edits via Incremental Tree Transformations
Ziyu Yao, Frank F. Xu, Pengcheng Yin, Huan Sun, Graham Neubig
http://arxiv.org/abs/2101.12087v1
• [cs.LG]Low Complexity Approximate Bayesian Logistic Regression for Sparse Online Learning
Gil I. Shamir, Wojciech Szpankowski
http://arxiv.org/abs/2101.12113v1
• [cs.LG]Machine learning for cloud resources management — An overview
V. N. Tsakalidou, P. Mitsou, G. A. Papakostas
http://arxiv.org/abs/2101.11984v1
• [cs.LG]Model-Based Policy Search Using Monte Carlo Gradient Estimation with Real Systems Application
Fabio Amadio, Alberto Dalla Libera, Riccardo Antonello, Daniel Nikovski, Ruggero Carli, Diego Romeres
http://arxiv.org/abs/2101.12115v1
• [cs.LG]On the Origin of Implicit Regularization in Stochastic Gradient Descent
Samuel L. Smith, Benoit Dherin, David G. T. Barrett, Soham De
http://arxiv.org/abs/2101.12176v1
• [cs.LG]On the mapping between Hopfield networks and Restricted Boltzmann Machines
Matthew Smart, Anton Zilman
http://arxiv.org/abs/2101.11744v1
• [cs.LG]Overestimation learning with guarantees
Adrien Gauffriau, François Malgouyres, Mélanie Ducoffe
http://arxiv.org/abs/2101.11717v1
• [cs.LG]PSpan:Mining Frequent Subnets of Petri Nets
Ruqian Lu, Shuhan Zhang
http://arxiv.org/abs/2101.11972v1
• [cs.LG]Self-Attention Meta-Learner for Continual Learning
Ghada Sokar, Decebal Constantin Mocanu, Mykola Pechenizkiy
http://arxiv.org/abs/2101.12136v1
• [cs.LG]Self-supervised Cross-silo Federated Neural Architecture Search
Xinle Liang, Yang Liu, Jiahuan Luo, Yuanqin He, Tianjian Chen, Qiang Yang
http://arxiv.org/abs/2101.11896v1
• [cs.LG]The Hidden Tasks of Generative Adversarial Networks: An Alternative Perspective on GAN Training
Romann M. Weber
http://arxiv.org/abs/2101.11863v1
• [cs.LG]tf.data: A Machine Learning Data Processing Framework
Derek G. Murray, Jiri Simsa, Ana Klimovic, Ihor Indyk
http://arxiv.org/abs/2101.12127v1
• [cs.LO]Efficiency of Query Evaluation Under Guarded TGDs: The Unbounded Arity Case
Cristina Feier
http://arxiv.org/abs/2101.11727v1
• [cs.MA]Exploring the Impact of Tunable Agents in Sequential Social Dilemmas
David O’Callaghan, Patrick Mannion
http://arxiv.org/abs/2101.11967v1
• [cs.NE]Coefficients’ Settings in Particle Swarm Optimization: Insight and Guidelines
Mauro S. Innocente, Johann Sienz
http://arxiv.org/abs/2101.11944v1
• [cs.NE]Evolutionary Neural Architecture Search Supporting Approximate Multipliers
Michal Pinos, Vojtech Mrazek, Lukas Sekanina
http://arxiv.org/abs/2101.11883v1
• [cs.NE]Reconstructing Perceptive Images from Brain Activity by Shape-Semantic GAN
Tao Fang, Yu Qi, Gang Pan
http://arxiv.org/abs/2101.12083v1
• [cs.NE]Stagnation Detection with Randomized Local Search
Amirhossein Rajabi, Carsten Witt
http://arxiv.org/abs/2101.12054v1
• [cs.RO]Acoustic Communication and Sensing for Inflatable Modular Soft Robots
D. S. Drew, M. Devlin, E. Hawkes, S. Follmer
http://arxiv.org/abs/2101.11817v1
• [cs.RO]Evolutionary Co-Design of Morphology and Control of Soft Tensegrity Modular Robots with Programmable Stiffness
Davide Zappetti, Jean Marc Bejjani, Dario Floreano
http://arxiv.org/abs/2101.11772v1
• [cs.RO]SwingBot: Learning Physical Features from In-hand Tactile Exploration for Dynamic Swing-up Manipulation
Chen Wang, Shaoxiong Wang, Branden Romero, Filipe Veiga, Edward Adelson
http://arxiv.org/abs/2101.11812v1
• [cs.RO]Visualization of Nonlinear Programming for Robot Motion Planning
David Hägele, Moataz Abdelaal, Ozgur S. Oguz, Marc Toussaint, Daniel Weiskopf
http://arxiv.org/abs/2101.12075v1
• [cs.SE]A Spatial-Temporal Graph Neural Network Framework for Automated Software Bug Triaging
Hongrun Wu, Yutao Ma, Zhenglong Xiang, Chen Yang, Keqing He
http://arxiv.org/abs/2101.11846v1
• [cs.SI]CML-COVID: A Large-Scale COVID-19 Twitter Dataset with Latent Topics, Sentiment and Location Information
Hassan Dashtian, Dhiraj Murthy
http://arxiv.org/abs/2101.12202v1
• [cs.SI]Contagion-Preserving Network Sparsifiers: Exploring Epidemic Edge Importance Utilizing Effective Resistance
Alexander Mercier
http://arxiv.org/abs/2101.11818v1
• [econ.EM]Choice modelling in the age of machine learning
S. Van Cranenburgh, S. Wang, A. Vij, F. Pereira, J. Walker
http://arxiv.org/abs/2101.11948v1
• [eess.IV]A Multi-Scale Conditional Deep Model for Tumor Cell Ratio Counting
Eric Cosatto, Kyle Gerard, Hans-Peter Graf, Maki Ogura, Tomoharu Kiyuna, Kanako C. Hatanaka, Yoshihiro Matsuno, Yutaka Hatanaka
http://arxiv.org/abs/2101.11731v1
• [eess.IV]An Explainable AI System for Automated COVID-19 Assessment and Lesion Categorization from CT-scans
Matteo Pennisi, Isaak Kavasidis, Concetto Spampinato, Vincenzo Schininà, Simone Palazzo, Francesco Rundo, Massimo Cristofaro, Paolo Campioni, Elisa Pianura, Federica Di Stefano, Ada Petrone, Fabrizio Albarello, Giuseppe Ippolito, Salvatore Cuzzocrea, Sabrina Conoci
http://arxiv.org/abs/2101.11943v1
• [eess.IV]Automated femur segmentation from computed tomography images using a deep neural network
P. A. Bjornsson, B. Helgason, H. Palsson, S. Sigurdsson, V. Gudnason, L. M. Ellingsen
http://arxiv.org/abs/2101.11742v1
• [eess.IV]Chronological age estimation of lateral cephalometric radiographs with deep learning
Ningtao Liu
http://arxiv.org/abs/2101.11805v1
• [eess.IV]Easy-GT: Open-Source Software to Facilitate Making the Ground Truth for White Blood Cells Nucleus
Seyedeh-Zahra Mousavi Kouzehkanan, Islam Tavakoli, Arezoo Alipanah
http://arxiv.org/abs/2101.11654v1
• [eess.IV]Neural Particle Image Velocimetry
Nikolay Stulov, Michael Chertkov
http://arxiv.org/abs/2101.11950v1
• [eess.SP]An Overview of Machine Learning Techniques for Radiowave Propagation Modeling
Aristeidis Seretis, Costas D. Sarris
http://arxiv.org/abs/2101.11760v1
• [eess.SP]Multi-Antenna Joint Radar and Communications: Precoder Optimization and Weighted Sum-Rate vs Probing Power Tradeoff
Chengcheng Xu, Bruno Clerckx, Jianyun Zhang
http://arxiv.org/abs/2101.11957v1
• [math.FA]Approximation with Tensor Networks. Part III: Multivariate Approximation
Mazen Ali, Anthony Nouy
http://arxiv.org/abs/2101.11932v1
• [math.NA]Non-intrusive reduced order modeling of poroelasticity of heterogeneous media based on a discontinuous Galerkin approximation
T. Kadeethum, F. Ballarin, N. Bouklas
http://arxiv.org/abs/2101.11810v1
• [math.NA]POD-DL-ROM: enhancing deep learning-based reduced order models for nonlinear parametrized PDEs by proper orthogonal decomposition
Stefania Fresca, Andrea Manzoni
http://arxiv.org/abs/2101.11845v1
• [math.OC]Potential Function-based Framework for Making the Gradients Small in Convex and Min-Max Optimization
Jelena Diakonikolas, Puqian Wang
http://arxiv.org/abs/2101.12101v1
• [math.ST]Ellipse Combining with Unknown Cross Ellipse Correlations
Adam Hall
http://arxiv.org/abs/2101.12034v1
• [math.ST]Reproducing kernel Hilbert spaces, polynomials and the classical moment problems
Holger Dette, Anatoly Zhigljavsky
http://arxiv.org/abs/2101.11968v1
• [physics.comp-ph]Porting WarpX to GPU-accelerated platforms
A. Myers, A. Almgren, L. D. Amorim, J. Bell, L. Fedeli, L. Ge, K. Gott, D. P. Grote, M. Hogan, A. Huebl, R. Jambunathan, R. Lehe, C. Ng, M. Rowan, O. Shapoval, M. Thévenet, J. -L. Vay, H. Vincenti, E. Yang, N. Zaïm, W. Zhang, Y. Zhao, E. Zoni
http://arxiv.org/abs/2101.12149v1
• [physics.data-an]Development of a Vertex Finding Algorithm using Recurrent Neural Network
Kiichi Goto, Taikan Suehara, Tamaki Yoshioka, Masakazu Kurata, Hajime Nagahara, Yuta Nakashima, Noriko Takemura, Masako Iwasaki
http://arxiv.org/abs/2101.11906v1
• [physics.data-an]Inference of stochastic time series with missing data
Sangwon Lee, Vipul Periwal, Junghyo Jo
http://arxiv.org/abs/2101.11816v1
• [physics.ins-det]Tackling the muon identification in water Cherenkov detectors problem for the future Southern Wide-field Gamma-ray Observatory by means of Machine Learning
B. S. González, R. Conceição, M. Pimenta, B. Tomé, A. Guillén
http://arxiv.org/abs/2101.11924v1
• [physics.soc-ph]Detecting Hidden Layers from Spreading Dynamics on Complex Networks
Łukasz G. Gajewski, Jan Chołoniewski, Mateusz Wilinski
http://arxiv.org/abs/2101.11758v1
• [q-bio.QM]G-MIND: An End-to-End Multimodal Imaging-Genetics Framework for Biomarker Identification and Disease Classification
Sayan Ghosal, Qiang Chen, Giulio Pergola, Aaron L. Goldman, William Ulrich, Karen F. Berman, Giuseppe Blasi, Leonardo Fazio, Antonio Rampino, Alessandro Bertolino, Daniel R. Weinberger, Venkata S. Mattay, Archana Venkataraman
http://arxiv.org/abs/2101.11656v1
• [quant-ph]Advantages and Bottlenecks of Quantum Machine Learning for Remote Sensing
Daniela A. Zaidenberg, Alessandro Sebastianelli, Dario Spiller, Silvia Liberata Ullo
http://arxiv.org/abs/2101.10657v2
• [quant-ph]Entanglement-assisted multiple-access channels: capacity regions and protocol designs
Haowei Shi, Min-Hsiu Hsieh, Saikat Guha, Zheshen Zhang, Quntao Zhuang
http://arxiv.org/abs/2101.12173v1
• [quant-ph]LOCCNet: a machine learning framework for distributed quantum information processing
Xuanqiang Zhao, Benchi Zhao, Zihe Wang, Zhixin Song, Xin Wang
http://arxiv.org/abs/2101.12190v1
• [stat.AP]Seroprevalence of SARS-CoV-2 antibodies in South Korea
Kwangmin Lee, Seongil Jo, Jaeyong Lee
http://arxiv.org/abs/2101.11991v1
• [stat.AP]The fraud loss for selecting the model complexity in fraud detection
Simon Boge Brant, Ingrid Hobæk Haff
http://arxiv.org/abs/2101.11907v1
• [stat.CO]Sequential Monte Carlo algorithms for agent-based models of disease transmission
Nianqiao Ju, Jeremy Heng, Pierre E. Jacob
http://arxiv.org/abs/2101.12156v1
• [stat.ME]A Kernel-Based Neural Network for High-dimensional Genetic Risk Prediction Analysis
Xiaoxi Shen, Xiaoran Tong, Qing Lu
http://arxiv.org/abs/2101.11807v1
• [stat.ME]Lévy Adaptive B-spline Regression via Overcomplete Systems
Sewon Park, Hee-Seok Oh, Jaeyong Lee
http://arxiv.org/abs/2101.12179v1
• [stat.ME]Robust Extrinsic Regression Analysis for Manifold Valued Data
Hwiyoung Lee
http://arxiv.org/abs/2101.11872v1
• [stat.ME]The AL-Gaussian Distribution as the Descriptive Model for the Internal Proactive Inhibition in the Standard Stop Signal Task
Mohsen Soltanifar, Michael Escobar, Annie Dupuis, Andre Chevrier, Russell Schachar
http://arxiv.org/abs/2101.11682v1
• [stat.ML]Interpolating Classifiers Make Few Mistakes
Tengyuan Liang, Benjamin Recht
http://arxiv.org/abs/2101.11815v1
• [stat.ML]Learning Representations for Individualized Organ Transplantation Allocation
Can Xu, Ahmed M. Alaa, Ioana Bica, Brent D. Ershoff, Maxime Cannesson, Mihaela van der Schaar
http://arxiv.org/abs/2101.11769v1
• [stat.ML]On Statistical Bias In Active Learning: How and When To Fix It
Sebastian Farquhar, Yarin Gal, Tom Rainforth
http://arxiv.org/abs/2101.11665v1
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