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.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.LO - 计算逻辑
cs.MA - 多代理系统
cs.MM - 多媒体
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.SE - 软件工程
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
eess.SY - 系统和控制
math.OC - 优化与控制
math.ST - 统计理论
physics.chem-ph -化学物理
physics.data-an - 数据分析、 统计和概率
q-fin.TR - 贸易与市场微观结构
quant-ph - 量子物理
stat.AP - 应用统计
stat.ME - 统计方法论
stat.ML - (统计)机器学习
• [cs.AI]Answer-Set Programs for Reasoning about Counterfactual Interventions and Responsibility Scores for Classification
• [cs.AI]Learning MR-Sort Models from Non-Monotone Data
• [cs.AI]MIMO: Mutual Integration of Patient Journey and Medical Ontology for Healthcare Representation Learning
• [cs.AI]SituationCO v1.2’s Terms, Properties, Relationships and Axioms — A Core Ontology for Particular and Generic Situations
• [cs.CL]A Statistical Model of Word Rank Evolution
• [cs.CL]An artificial intelligence natural language processing pipeline for information extraction in neuroradiology
• [cs.CL]CATE: CAusality Tree Extractor from Natural Language Requirements
• [cs.CL]CausalBERT: Injecting Causal Knowledge Into Pre-trained Models with Minimal Supervision
• [cs.CL]Comparison of Czech Transformers on Text Classification Tasks
• [cs.CL]Debiasing Multilingual Word Embeddings: A Case Study of Three Indian Languages
• [cs.CL]Fine-Grained Causality Extraction From Natural Language Requirements Using Recursive Neural Tensor Networks
• [cs.CL]Guided Generation of Cause and Effect
• [cs.CL]How Do Pedophiles Tweet? Investigating the Writing Styles and Online Personas of Child Cybersex Traffickers in the Philippines
• [cs.CL]Improved Text Classification via Contrastive Adversarial Training
• [cs.CL]Soft Layer Selection with Meta-Learning for Zero-Shot Cross-Lingual Transfer
• [cs.CL]TLA: Twitter Linguistic Analysis
• [cs.CL]The Effectiveness of Intermediate-Task Training for Code-Switched Natural Language Understanding
• [cs.CL]Using Adversarial Debiasing to Remove Bias from Word Embeddings
• [cs.CL]What Do You Get When You Cross Beam Search with Nucleus Sampling?
• [cs.CR]Using Undervolting as an On-Device Defense Against Adversarial Machine Learning Attacks
• [cs.CV]An overview of mixing augmentation methods and augmentation strategies
• [cs.CV]Anomaly Detection via Self-organizing Map
• [cs.CV]Cell Detection from Imperfect Annotation by Pseudo Label Selection Using P-classification
• [cs.CV]Characterization Multimodal Connectivity of Brain Network by Hypergraph GAN for Alzheimer’s Disease Analysis
• [cs.CV]CogME: A Novel Evaluation Metric for Video Understanding Intelligence
• [cs.CV]CycleMLP: A MLP-like Architecture for Dense Prediction
• [cs.CV]DRDF: Determining the Importance of Different Multimodal Information with Dual-Router Dynamic Framework
• [cs.CV]DRIVE: Deep Reinforced Accident Anticipation with Visual Explanation
• [cs.CV]Deep Iterative 2D/3D Registration
• [cs.CV]Evidential Deep Learning for Open Set Action Recognition
• [cs.CV]Fabrication-Aware Reverse Engineering for Carpentry
• [cs.CV]Few Shots Is All You Need: A Progressive Few Shot Learning Approach for Low Resource Handwriting Recognition
• [cs.CV]From Single to Multiple: Leveraging Multi-level Prediction Spaces for Video Forecasting
• [cs.CV]Multimodal Representations Learning and Adversarial Hypergraph Fusion for Early Alzheimer’s Disease Prediction
• [cs.CV]Registration of 3D Point Sets Using Correntropy Similarity Matrix
• [cs.CV]S4T: Source-free domain adaptation for semantic segmentation via self-supervised selective self-training
• [cs.CV]Structure-Aware Long Short-Term Memory Network for 3D Cephalometric Landmark Detection
• [cs.CV]Superpixel-guided Iterative Learning from Noisy Labels for Medical Image Segmentation
• [cs.CV]TumorCP: A Simple but Effective Object-Level Data Augmentation for Tumor Segmentation
• [cs.CV]Unsupervised Domain Adaptation in LiDAR Semantic Segmentation with Self-Supervision and Gated Adapters
• [cs.CV]Weighted Intersection over Union (wIoU): A New Evaluation Metric for Image Segmentation
• [cs.CV]Window Detection In Facade Imagery: A Deep Learning Approach Using Mask R-CNN
• [cs.CV]You Better Look Twice: a new perspective for designing accurate detectors with reduced computations
• [cs.DB]Provenance, Anonymisation and Data Environments: a Unifying Construction
• [cs.DB]Understanding the Scalability of Hyperledger Fabric
• [cs.DC]Architecture-Specific Performance Optimization of Compute-Intensive FaaS Functions
• [cs.DC]Communication Lower Bounds for Nested Bilinear Algorithms
• [cs.DC]Formal method of synthesis of optimal topologies of computing systems based on projective description of graphs
• [cs.DS]Optimizing the order of actions in contact tracing
• [cs.GT]A Cooperative Optimal Mining Model for Bitcoin
• [cs.GT]Peer Selection with Noisy Assessments
• [cs.HC]Audit, Don’t Explain — Recommendations Based on a Socio-Technical Understanding of ML-Based Systems
• [cs.HC]Auditing the Biases Enacted by YouTube for Political Topics in Germany
• [cs.HC]Human Perception of Audio Deepfakes
• [cs.IR]Learned Sorted Table Search and Static Indexes in Small Space: Methodological and Practical Insights via an Experimental Study
• [cs.IT]Bidirectional Approximate Message Passing for RIS-Assisted Multi-User MISO Communications
• [cs.IT]Conjugate Beamforming with Fractional-Exponent Normalization and Scalable Power Control in Cell-Free Massive MIMO
• [cs.IT]DOA Estimation for Hybrid Massive MIMO Systems using Mixed-ADCs: Performance Loss and Energy Efficiency
• [cs.IT]Fast polar codes for terabits-per-second throughput communications
• [cs.IT]Limits of Detecting Extraterrestrial Civilizations
• [cs.IT]Maximizing the Set Cardinality of Users Scheduled for Ultra-dense uRLLC Networks
• [cs.IT]On the Generalized Covering Radii of Reed-Muller Codes
• [cs.IT]On the Modulus in Matching Vector Codes
• [cs.IT]Single-Shot Compression for Hypothesis Testing
• [cs.IT]THz Transmission meets Untrusted UAV-Relaying; Trajectory and Communication Co-design for Secrecy Energy Efficiency Maximization
• [cs.LG]A Deep Reinforcement Learning Approach for Fair Traffic Signal Control
• [cs.LG]Black-box Probe for Unsupervised Domain Adaptation without Model Transferring
• [cs.LG]Boundary of Distribution Support Generator (BDSG): Sample Generation on the Boundary
• [cs.LG]Bridging the Gap between Spatial and Spectral Domains: A Theoretical Framework for Graph Neural Networks
• [cs.LG]CGANs with Auxiliary Discriminative Classifier
• [cs.LG]Checkovid: A COVID-19 misinformation detection system on Twitter using network and content mining perspectives
• [cs.LG]Communication and Computation Reduction for Split Learning using Asynchronous Training
• [cs.LG]Deep learning for temporal data representation in electronic health records: A systematic review of challenges and methodologies
• [cs.LG]Defending against Reconstruction Attack in Vertical Federated Learning
• [cs.LG]Demonstration-Guided Reinforcement Learning with Learned Skills
• [cs.LG]Design of Experiments for Stochastic Contextual Linear Bandits
• [cs.LG]Differentiable Feature Selection, a Reparameterization Approach
• [cs.LG]Distribution of Classification Margins: Are All Data Equal?
• [cs.LG]ECG Heartbeat Classification Using Multimodal Fusion
• [cs.LG]Efficient Algorithms for Learning Depth-2 Neural Networks with General ReLU Activations
• [cs.LG]Fast and Scalable Adversarial Training of Kernel SVM via Doubly Stochastic Gradients
• [cs.LG]Faster Matchings via Learned Duals
• [cs.LG]GLIME: A new graphical methodology for interpretable model-agnostic explanations
• [cs.LG]Group Contrastive Self-Supervised Learning on Graphs
• [cs.LG]High-dimensional Multivariate Time Series Forecasting in IoT Applications using Embedding Non-stationary Fuzzy Time Series
• [cs.LG]Incentivizing Compliance with Algorithmic Instruments
• [cs.LG]Integration of Autoencoder and Functional Link Artificial Neural Network for Multi-label Classification
• [cs.LG]Interpreting diffusion score matching using normalizing flow
• [cs.LG]Leave-one-out Unfairness
• [cs.LG]MG-NET: Leveraging Pseudo-Imaging for Multi-Modal Metagenome Analysis
• [cs.LG]Machine Learning for Real-World Evidence Analysis of COVID-19 Pharmacotherapy
• [cs.LG]Memorization in Deep Neural Networks: Does the Loss Function matter?
• [cs.LG]Neural Fixed-Point Acceleration for Convex Optimization
• [cs.LG]On the Memorization Properties of Contrastive Learning
• [cs.LG]Online structural kernel selection for mobile health
• [cs.LG]Preventing dataset shift from breaking machine-learning biomarkers
• [cs.LG]Private Alternating Least Squares: Practical Private Matrix Completion with Tighter Rates
• [cs.LG]Relational Graph Convolutional Networks: A Closer Look
• [cs.LG]Statistical Estimation from Dependent Data
• [cs.LG]Toward Collaborative Reinforcement Learning Agents that Communicate Through Text-Based Natural Language
• [cs.LG]Training Electric Vehicle Charging Controllers with Imitation Learning
• [cs.LG]Uncertainty Estimation and Out-of-Distribution Detection for Counterfactual Explanations: Pitfalls and Solutions
• [cs.LO]JEFL: Joint Embedding of Formal Proof Libraries
• [cs.LO]Learning Theorem Proving Components
• [cs.MA]Multi-agent Reinforcement Learning Improvement in a Dynamic Environment Using Knowledge Transfer
• [cs.MM]Objective video quality metrics application to video codecs comparisons: choosing the best for subjective quality estimation
• [cs.NE]An Efficient Multi-objective Evolutionary Approach for Solving the Operation of Multi-Reservoir System Scheduling in Hydro-Power Plants
• [cs.NE]An Exploration of Exploration: Measuring the ability of lexicase selection to find obscure pathways to optimality
• [cs.NE]Evolutionary Innovation Viewed as Novel Physical Phenomena and Hierarchical Systems Building
• [cs.NI]Into Summarization Techniques for IoT Data Discovery Routing
• [cs.RO]A Factor Graph-based approach to vehicle sideslip angle estimation
• [cs.RO]Assured Mission Adaptation of UAVs
• [cs.RO]Bayesian Controller Fusion: Leveraging Control Priors in Deep Reinforcement Learning for Robotics
• [cs.RO]Enumeration of Polyominoes & Polycubes Composed of Magnetic Cubes
• [cs.RO]Learning compliant grasping and manipulation by teleoperation with adaptive force control
• [cs.RO]Levels of Automation for a Mobile Robot Teleoperated by a Caregiver
• [cs.RO]MarsExplorer: Exploration of Unknown Terrains via Deep Reinforcement Learning and Procedurally Generated Environments
• [cs.RO]Multi-Agent Belief Sharing through Autonomous Hierarchical Multi-Level Clustering
• [cs.RO]Track based Offline Policy Learning for Overtaking Maneuvers with Autonomous Racecars
• [cs.SE]Predicting Issue Types on GitHub
• [cs.SI]Characterizing Social Imaginaries and Self-Disclosures of Dissonance in Online Conspiracy Discussion Communities
• [cs.SI]Robust subgraph counting with distribution-free random graph analysis
• [eess.AS]Audio Captioning Transformer
• [eess.AS]CL4AC: A Contrastive Loss for Audio Captioning
• [eess.AS]Conditional Sound Generation Using Neural Discrete Time-Frequency Representation Learning
• [eess.IV]10fe
Modality-aware Mutual Learning for Multi-modal Medical Image Segmentation
• [eess.IV]3D fluorescence microscopy data synthesis for segmentation and benchmarking
• [eess.IV]3D-StyleGAN: A Style-Based Generative Adversarial Network for Generative Modeling of Three-Dimensional Medical Images
• [eess.IV]A Point Cloud Generative Model via Tree-Structured Graph Convolutions for 3D Brain Shape Reconstruction
• [eess.IV]High-Resolution Pelvic MRI Reconstruction Using a Generative Adversarial Network with Attention and Cyclic Loss
• [eess.IV]HistoCartography: A Toolkit for Graph Analytics in Digital Pathology
• [eess.IV]Towards Lower-Dose PET using Physics-Based Uncertainty-Aware Multimodal Learning with Robustness to Out-of-Distribution Data
• [eess.SP]EMG Pattern Recognition via Bayesian Inference with Scale Mixture-Based Stochastic Generative Models
• [eess.SP]KalmanNet: Neural Network Aided Kalman Filtering for Partially Known Dynamics
• [eess.SY]Optimal Operation of Power Systems with Energy Storage under Uncertainty: A Scenario-based Method with Strategic Sampling
• [eess.SY]Strategic Mitigation of Agent Inattention in Drivers with Open-Quantum Cognition Models
• [math.OC]Learning a Large Neighborhood Search Algorithm for Mixed Integer Programs
• [math.ST]Extracting Governing Laws from Sample Path Data of Non-Gaussian Stochastic Dynamical Systems
• [math.ST]Inner spike and slab Bayesian nonparametric models
• [math.ST]Linear spectral statistics of sequential sample covariance matrices
• [math.ST]On ageing properties of lifetime distributions
• [math.ST]Optimal Rates for Nonparametric Density Estimation under Communication Constraints
• [physics.chem-ph]Predicting trajectory behaviour via machine-learned invariant manifolds
• [physics.data-an]Manifold learning-based polynomial chaos expansions for high-dimensional surrogate models
• [q-fin.TR]Order Book Queue Hawkes-Markovian Modeling
• [quant-ph]Modulus of continuity of the quantum -entropy with respect to the trace distance
• [quant-ph]Quantum Measurement Classification with Qudits
• [stat.AP]Decoupling Systemic Risk into Endopathic and Exopathic Competing Risks Through Autoregressive Conditional Accelerated Fréchet Model
• [stat.AP]The impact of increasing COVID-19 cases/deaths on the number of uncivil tweets directed at governments
• [stat.AP]Tracking the Transmission Dynamics of COVID-19 with a Time-Varying Coefficient State-Space Model
• [stat.ME]A Stochastic Version of the EM Algorithm for Mixture Cure Rate Model with Exponentiated Weibull Family of Lifetimes
• [stat.ME]Bayesian iterative screening in ultra-high dimensional settings
• [stat.ME]Evaluating Effectiveness of Public Health Intervention Strategies for Mitigating COVID-19 Pandemic
• [stat.ME]Frequentist inference for cluster randomised trials with multiple primary outcomes
• [stat.ME]Improving the Power to Detect Indirect Effects in Mediation Analysis
• [stat.ME]Log-symmetric models with cure fraction with application to leprosy reactions data
• [stat.ME]Strategies for variable selection in large-scale healthcare database studies with missing covariate and outcome data
• [stat.ML]A variational approximate posterior for the deep Wishart process
• [stat.ML]Adaptive Inducing Points Selection For Gaussian Processes
• [stat.ML]Delving Into Deep Walkers: A Convergence Analysis of Random-Walk-Based Vertex Embeddings
• [stat.ML]Differentiable Annealed Importance Sampling and the Perils of Gradient Noise
• [stat.ML]Discovering Latent Causal Variables via Mechanism Sparsity: A New Principle for Nonlinear ICA
• [stat.ML]On the Convergence of Prior-Guided Zeroth-Order Optimization Algorithms
·····································
• [cs.AI]Answer-Set Programs for Reasoning about Counterfactual Interventions and Responsibility Scores for Classification
Leopoldo Bertossi, Gabriela Reyes
http://arxiv.org/abs/2107.10159v1
• [cs.AI]Learning MR-Sort Models from Non-Monotone Data
Pegdwende Minoungou, Vincent Mousseau, Wassila Ouerdane, Paolo Scotton
http://arxiv.org/abs/2107.09668v1
• [cs.AI]MIMO: Mutual Integration of Patient Journey and Medical Ontology for Healthcare Representation Learning
Xueping Peng, Guodong Long, Tao Shen, Sen Wang, Zhendong Niu, Chengqi Zhang
http://arxiv.org/abs/2107.09288v2
• [cs.AI]SituationCO v1.2’s Terms, Properties, Relationships and Axioms — A Core Ontology for Particular and Generic Situations
Luis Olsina, Guido Tebes, Pablo Becker
http://arxiv.org/abs/2107.10083v1
• [cs.CL]A Statistical Model of Word Rank Evolution
Alex John Quijano, Rick Dale, Suzanne Sindi
http://arxiv.org/abs/2107.09948v1
• [cs.CL]An artificial intelligence natural language processing pipeline for information extraction in neuroradiology
Henry Watkins, Robert Gray, Ashwani Jha, Parashkev Nachev
http://arxiv.org/abs/2107.10021v1
• [cs.CL]CATE: CAusality Tree Extractor from Natural Language Requirements
Noah Jadallah, Jannik Fischbach, Julian Frattini, Andreas Vogelsang
http://arxiv.org/abs/2107.10023v1
• [cs.CL]CausalBERT: Injecting Causal Knowledge Into Pre-trained Models with Minimal Supervision
Zhongyang Li, Xiao Ding, Kuo Liao, Ting Liu, Bing Qin
http://arxiv.org/abs/2107.09852v1
• [cs.CL]Comparison of Czech Transformers on Text Classification Tasks
Jan Lehečka, Jan Švec
http://arxiv.org/abs/2107.10042v1
• [cs.CL]Debiasing Multilingual Word Embeddings: A Case Study of Three Indian Languages
Srijan Bansal, Vishal Garimella, Ayush Suhane, Animesh Mukherjee
http://arxiv.org/abs/2107.10181v1
• [cs.CL]Fine-Grained Causality Extraction From Natural Language Requirements Using Recursive Neural Tensor Networks
Jannik Fischbach, Tobias Springer, Julian Frattini, Henning Femmer, Andreas Vogelsang, Daniel Mendez
http://arxiv.org/abs/2107.09980v1
• [cs.CL]Guided Generation of Cause and Effect
Zhongyang Li, Xiao Ding, Ting Liu, J. Edward Hu, Benjamin Van Durme
http://arxiv.org/abs/2107.09846v1
• [cs.CL]How Do Pedophiles Tweet? Investigating the Writing Styles and Online Personas of Child Cybersex Traffickers in the Philippines
Joseph Marvin Imperial
http://arxiv.org/abs/2107.09881v1
• [cs.CL]Improved Text Classification via Contrastive Adversarial Training
Lin Pan, Chung-Wei Hang, Avirup Sil, Saloni Potdar, Mo Yu
http://arxiv.org/abs/2107.10137v1
• [cs.CL]Soft Layer Selection with Meta-Learning for Zero-Shot Cross-Lingual Transfer
Weijia Xu, Batool Haider, Jason Krone, Saab Mansour
http://arxiv.org/abs/2107.09840v1
• [cs.CL]TLA: Twitter Linguistic Analysis
Tushar Sarkar, Nishant Rajadhyaksha
http://arxiv.org/abs/2107.09710v1
• [cs.CL]The Effectiveness of Intermediate-Task Training for Code-Switched Natural Language Understanding
Archiki Prasad, Mohammad Ali Rehan, Shreya Pathak, Preethi Jyothi
http://arxiv.org/abs/2107.09931v1
• [cs.CL]Using Adversarial Debiasing to Remove Bias from Word Embeddings
Dana Kenna
http://arxiv.org/abs/2107.10251v1
• [cs.CL]What Do You Get When You Cross Beam Search with Nucleus Sampling?
Uri Shaham, Omer Levy
http://arxiv.org/abs/2107.09729v1
• [cs.CR]Using Undervolting as an On-Device Defense Against Adversarial Machine Learning Attacks
Saikat Majumdar, Mohammad Hossein Samavatian, Kristin Barber, Radu Teodorescu
http://arxiv.org/abs/2107.09804v1
• [cs.CV]An overview of mixing augmentation methods and augmentation strategies
Dominik Lewy, Jacek Mańdziuk
http://arxiv.org/abs/2107.09887v1
• [cs.CV]Anomaly Detection via Self-organizing Map
Ning Li, Kaitao Jiang, Zhiheng Ma, Xing Wei, Xiaopeng Hong, Yihong Gong
http://arxiv.org/abs/2107.09903v1
• [cs.CV]Cell Detection from Imperfect Annotation by Pseudo Label Selection Using P-classification
Kazuma Fujii, Daiki Suehiro, Kazuya Nishimura, Ryoma Bise
http://arxiv.org/abs/2107.09289v2
• [cs.CV]Characterization Multimodal Connectivity of Brain Network by Hypergraph GAN for Alzheimer’s Disease Analysis
Junren Pan, Baiying Lei, Yanyan Shen, Yong Liu, Zhiguang Feng, Shuqiang Wang
http://arxiv.org/abs/2107.09953v1
• [cs.CV]CogME: A Novel Evaluation Metric for Video Understanding Intelligence
Minjung Shin, Jeonghoon Kim, Seongho Choi, Yu-Jung Heo, Donghyun Kim, Minsu Lee, Byoung-Tak Zhang, Jeh-Kwang Ryu
http://arxiv.org/abs/2107.09847v1
• [cs.CV]CycleMLP: A MLP-like Architecture for Dense Prediction
Shoufa Chen, Enze Xie, Chongjian Ge, Ding Liang, Ping Luo
http://arxiv.org/abs/2107.10224v1
• [cs.CV]DRDF: Determining the Importance of Different Multimodal Information with Dual-Router Dynamic Framework
Haiwen Hong, Xuan Jin, Yin Zhang, Yunqing Hu, Jingfeng Zhang, Yuan He, Hui Xue
http://arxiv.org/abs/2107.09909v1
• [cs.CV]DRIVE: Deep Reinforced Accident Anticipation with Visual Explanation
Wentao Bao, Qi Yu, Yu Kong
http://arxiv.org/abs/2107.10189v1
• [cs.CV]Deep Iterative 2D/3D Registration
Srikrishna Jaganathan, Jian Wang, Anja Borsdorf, Karthik Shetty, Andreas Maier
http://arxiv.org/abs/2107.10004v1
• [cs.CV]Evidential Deep Learning for Open Set Action Recognition
Wentao Bao, Qi Yu, Yu Kong
http://arxiv.org/abs/2107.10161v1
• [cs.CV]Fabrication-Aware Reverse Engineering for Carpentry
James Noeckel, Haisen Zhao, Brian Curless, Adriana Schulz
http://arxiv.org/abs/2107.09965v1
• [cs.CV]Few Shots Is All You Need: A Progressive Few Shot Learning Approach for Low Resource Handwriting Recognition
Mohamed Ali Souibgui, Alicia Fornés, Yousri Kessentini, Beáta Megyesi
http://arxiv.org/abs/2107.10064v1
• [cs.CV]From Single to Multiple: Leveraging Multi-level Prediction Spaces for Video Forecasting
Mengcheng Lan, Shuliang Ning, Yanran Li, Qian Chen, Xunlai Chen, Xiaoguang Han, Shuguang Cui
http://arxiv.org/abs/2107.10068v1
• [cs.CV]Multimodal Representations Learning and Adversarial Hypergraph Fusion for Early Alzheimer’s Disease Prediction
Qiankun Zuo, Baiying Lei, Yanyan Shen, Yong Liu, Zhiguang Feng, Shuqiang Wang
http://arxiv.org/abs/2107.09928v1
• [cs.CV]Registration of 3D Point Sets Using Correntropy Similarity Matrix
Ashutosh Singandhupe, Hung La, Trung Dung Ngo, Van Ho
http://arxiv.org/abs/2107.09725v1
• [cs.CV]S4T: Source-free domain adaptation for semantic segmentation via self-supervised selective self-training
Viraj Prabhu, Shivam Khare, Deeksha Kartik, Judy Hoffman
http://arxiv.org/abs/2107.10140v1
• [cs.CV]Structure-Aware Long Short-Term Memory Network for 3D Cephalometric Landmark Detection
Runnan Chen, Yuexin Ma, Nenglun Chen, Lingjie Liu, Zhiming Cui, Yanhong Lin, Wenping Wang
http://arxiv.org/abs/2107.09899v1
• [cs.CV]Superpixel-guided Iterative Learning from Noisy Labels for Medical Image Segmentation
Shuailin Li, Zhitong Gao, Xuming He
http://arxiv.org/abs/2107.10100v1
• [cs.CV]TumorCP: A Simple but Effective Object-Level Data Augmentation for Tumor Segmentation
Jiawei Yang, Yao Zhang, Yuan Liang, Yang Zhang, Lei He, Zhiqiang He
http://arxiv.org/abs/2107.09843v1
• [cs.CV]Unsupervised Domain Adaptation in LiDAR Semantic Segmentation with Self-Supervision and Gated Adapters
Mrigank Rochan, Shubhra Aich, Eduardo R. Corral-Soto, Amir Nabatchian, Bingbing Liu
http://arxiv.org/abs/2107.09783v1
• [cs.CV]Weighted Intersection over Union (wIoU): A New Evaluation Metric for Image Segmentation
Yeong-Jun Cho
http://arxiv.org/abs/2107.09858v1
• [cs.CV]Window Detection In Facade Imagery: A Deep Learning Approach Using Mask R-CNN
Nils Nordmark, Mola Ayenew
http://arxiv.org/abs/2107.10006v1
• [cs.CV]You Better Look Twice: a new perspective for designing accurate detectors with reduced computations
Alexandra Dana, Maor Shutman, Yotam Perlitz, Ran Vitek, Tomer Peleg, Roy Jevnisek
http://arxiv.org/abs/2107.10050v1
• [cs.DB]Provenance, Anonymisation and Data Environments: a Unifying Construction
Muhammad Aslam Jarwar, Adriane Chapman, Mark Elliot, Fatemeh Raji
http://arxiv.org/abs/2107.09966v1
• [cs.DB]Understanding the Scalability of Hyperledger Fabric
Minh Quang Nguyen, Dumitrel Loghin, Tien Tuan Anh Dinh
http://arxiv.org/abs/2107.09886v1
• [cs.DC]Architecture-Specific Performance Optimization of Compute-Intensive FaaS Functions
Mohak Chadha, Anshul Jindal, Michael Gerndt
http://arxiv.org/abs/2107.10008v1
• [cs.DC]Communication Lower Bounds for Nested Bilinear Algorithms
Caleb Ju, Yifan Zhang, Edgar Solomonik
http://arxiv.org/abs/2107.09834v1
• [cs.DC]Formal method of synthesis of optimal topologies of computing systems based on projective description of graphs
V. A. Melent’ev
http://arxiv.org/abs/2107.10018v1
• [cs.DS]Optimizing the order of actions in contact tracing
Michela Meister, Jon Kleinberg
http://arxiv.org/abs/2107.09803v1
• [cs.GT]A Cooperative Optimal Mining Model for Bitcoin
David Lajeunesse, Hugo D. Scolnik
http://arxiv.org/abs/2107.09707v1
• [cs.GT]Peer Selection with Noisy Assessments
Omer Lev, Nicholas Mattei, Paolo Turrini, Stanislav Zhydkov
http://arxiv.org/abs/2107.10121v1
• [cs.HC]Audit, Don’t Explain — Recommendations Based on a Socio-Technical Understanding of ML-Based Systems
Hendrik Heuer
http://arxiv.org/abs/2107.09917v1
• [cs.HC]Auditing the Biases Enacted by YouTube for Political Topics in Germany
Hendrik Heuer, Hendrik Hoch, Andreas Breiter, Yannis Theocharis
http://arxiv.org/abs/2107.09922v1
• [cs.HC]Human Perception of Audio Deepfakes
Nicolas M. Müller, Karla Markert, Konstantin Böttinger
http://arxiv.org/abs/2107.09667v1
• [cs.IR]Learned Sorted Table Search and Static Indexes in Small Space: Methodological and Practical Insights via an Experimental Study
Domenico Amato, Raffaele Giancarlo, Giosuè Lo Bosco
http://arxiv.org/abs/2107.09480v2
• [cs.IT]Bidirectional Approximate Message Passing for RIS-Assisted Multi-User MISO Communications
Li Wei, Chongwen Huang, Qinghua Guo, Zhaoyang Zhang, Merouane Debbah, Chau Yuen
http://arxiv.org/abs/2107.09836v1
• [cs.IT]Conjugate Beamforming with Fractional-Exponent Normalization and Scalable Power Control in Cell-Free Massive MIMO
Giovanni Interdonato, Stefano Buzzi
http://arxiv.org/abs/2107.09777v1
• [cs.IT]DOA Estimation for Hybrid Massive MIMO Systems using Mixed-ADCs: Performance Loss and Energy Efficiency
Baihua Shi, Rongen Dong, Qijuan Jie, Lingling Zhu, Feng Shu, Jiangzhou Wang
http://arxiv.org/abs/2107.09934v1
• [cs.IT]Fast polar codes for terabits-per-second throughput communications
Jiajie Tong, Xianbin Wang, Qifan Zhang, Huazi Zhang, Rong Li, Jun Wang, Wen Tong
http://arxiv.org/abs/
52d4
/2107.08600v1
52d4
/2107.08600v1)
• [cs.IT]Limits of Detecting Extraterrestrial Civilizations
Ian George, Xinan Chen, Lav R. Varshney
http://arxiv.org/abs/2107.09794v1
• [cs.IT]Maximizing the Set Cardinality of Users Scheduled for Ultra-dense uRLLC Networks
Shiwen He, Jun Yuan, Zhenyu An, Yunshan Yi, Yongming Huang
http://arxiv.org/abs/2107.09404v2
• [cs.IT]On the Generalized Covering Radii of Reed-Muller Codes
Dor Elimelech, Hengjia Wei, Moshe Schwartz
http://arxiv.org/abs/2107.09902v1
• [cs.IT]On the Modulus in Matching Vector Codes
Lin Zhu, Wen Ming Li, Liang Feng Zhang
http://arxiv.org/abs/2107.09830v1
• [cs.IT]Single-Shot Compression for Hypothesis Testing
Fabrizio Carpi, Siddharth Garg, Elza Erkip
http://arxiv.org/abs/2107.09778v1
• [cs.IT]THz Transmission meets Untrusted UAV-Relaying; Trajectory and Communication Co-design for Secrecy Energy Efficiency Maximization
Milad Tatar Mamaghani, Yi Hong
http://arxiv.org/abs/2107.09896v1
• [cs.LG]A Deep Reinforcement Learning Approach for Fair Traffic Signal Control
Majid Raeis, Alberto Leon-Garcia
http://arxiv.org/abs/2107.10146v1
• [cs.LG]Black-box Probe for Unsupervised Domain Adaptation without Model Transferring
Kunhong Wu, Yucheng Shi, Yahong Han, Yunfeng Shao, Bingshuai Li
http://arxiv.org/abs/2107.10174v1
• [cs.LG]Boundary of Distribution Support Generator (BDSG): Sample Generation on the Boundary
Nikolaos Dionelis
http://arxiv.org/abs/2107.09950v1
• [cs.LG]Bridging the Gap between Spatial and Spectral Domains: A Theoretical Framework for Graph Neural Networks
Zhiqian Chen, Fanglan Chen, Lei Zhang, Taoran Ji, Kaiqun Fu, Liang Zhao, Feng Chen, Lingfei Wu, Charu Aggarwal, Chang-Tien Lu
http://arxiv.org/abs/2107.10234v1
• [cs.LG]CGANs with Auxiliary Discriminative Classifier
Liang Hou, Qi Cao, Huawei Shen, Xueqi Cheng
http://arxiv.org/abs/2107.10060v1
• [cs.LG]Checkovid: A COVID-19 misinformation detection system on Twitter using network and content mining perspectives
Sajad Dadgar, Mehdi Ghatee
http://arxiv.org/abs/2107.09768v1
• [cs.LG]Communication and Computation Reduction for Split Learning using Asynchronous Training
Xing Chen, Jingtao Li, Chaitali Chakrabarti
http://arxiv.org/abs/2107.09786v1
• [cs.LG]Deep learning for temporal data representation in electronic health records: A systematic review of challenges and methodologies
Feng Xie, Han Yuan, Yilin Ning, Marcus Eng Hock Ong, Mengling Feng, Wynne Hsu, Bibhas Chakraborty, Nan Liu
http://arxiv.org/abs/2107.09951v1
• [cs.LG]Defending against Reconstruction Attack in Vertical Federated Learning
Jiankai Sun, Yuanshun Yao, Weihao Gao, Junyuan Xie, Chong Wang
http://arxiv.org/abs/2107.09898v1
• [cs.LG]Demonstration-Guided Reinforcement Learning with Learned Skills
Karl Pertsch, Youngwoon Lee, Yue Wu, Joseph J. Lim
http://arxiv.org/abs/2107.10253v1
• [cs.LG]Design of Experiments for Stochastic Contextual Linear Bandits
Andrea Zanette, Kefan Dong, Jonathan Lee, Emma Brunskill
http://arxiv.org/abs/2107.09912v1
• [cs.LG]Differentiable Feature Selection, a Reparameterization Approach
Jérémie Dona, Patrick Gallinari
http://arxiv.org/abs/2107.10030v1
• [cs.LG]Distribution of Classification Margins: Are All Data Equal?
Andrzej Banburski, Fernanda De La Torre, Nishka Pant, Ishana Shastri, Tomaso Poggio
http://arxiv.org/abs/2107.10199v1
• [cs.LG]ECG Heartbeat Classification Using Multimodal Fusion
Zeeshan Ahmad, Anika Tabassum, Ling Guan, Naimul Khan
http://arxiv.org/abs/2107.09869v1
• [cs.LG]Efficient Algorithms for Learning Depth-2 Neural Networks with General ReLU Activations
Pranjal Awasthi, Alex Tang, Aravindan Vijayaraghavan
http://arxiv.org/abs/2107.10209v1
• [cs.LG]Fast and Scalable Adversarial Training of Kernel SVM via Doubly Stochastic Gradients
Huimin Wu, Zhengmian Hu, Bin Gu
http://arxiv.org/abs/2107.09937v1
• [cs.LG]Faster Matchings via Learned Duals
Michael Dinitz, Sungjin Im, Thomas Lavastida, Benjamin Moseley, Sergei Vassilvitskii
http://arxiv.org/abs/2107.09770v1
• [cs.LG]GLIME: A new graphical methodology for interpretable model-agnostic explanations
Zoumpolia Dikopoulou, Serafeim Moustakidis, Patrik Karlsson
http://arxiv.org/abs/2107.09927v1
• [cs.LG]Group Contrastive Self-Supervised Learning on Graphs
Xinyi Xu, Cheng Deng, Yaochen Xie, Shuiwang Ji
http://arxiv.org/abs/2107.09787v1
• [cs.LG]High-dimensional Multivariate Time Series Forecasting in IoT Applications using Embedding Non-stationary Fuzzy Time Series
Hugo Vinicius Bitencourt, Frederico Gadelha Guimarães
http://arxiv.org/abs/2107.09785v1
• [cs.LG]Incentivizing Compliance with Algorithmic Instruments
Daniel Ngo, Logan Stapleton, Vasilis Syrgkanis, Zhiwei Steven Wu
http://arxiv.org/abs/2107.10093v1
• [cs.LG]Integration of Autoencoder and Functional Link Artificial Neural Network for Multi-label Classification
Anwesha Law, Ashish Ghosh
http://arxiv.org/abs/2107.09904v1
• [cs.LG]Interpreting diffusion score matching using normalizing flow
Wenbo Gong, Yingzhen Li
http://arxiv.org/abs/2107.10072v1
• [cs.LG]Leave-one-out Unfairness
Emily Black, Matt Fredrikson
http://arxiv.org/abs/2107.10171v1
• [cs.LG]MG-NET: Leveraging Pseudo-Imaging for Multi-Modal Metagenome Analysis
Sathyanarayanan N. Aakur, Sai Narayanan, Vineela Indla, Arunkumar Bagavathi, Vishalini Laguduva Ramnath, Akhilesh Ramachandran
http://arxiv.org/abs/2107.09883v1
• [cs.LG]Machine Learning for Real-World Evidence Analysis of COVID-19 Pharmacotherapy
Aurelia Bustos, Patricio Mas_Serrano, Mari L. Boquera, Jose M. Salinas
http://arxiv.org/abs/2107.10239v1
• [cs.LG]Memorization in Deep Neural Networks: Does the Loss Function matter?
Deep Patel, P. S. Sastry
http://arxiv.org/abs/2107.09957v1
• [cs.LG]Neural Fixed-Point Acceleration for Convex Optimization
Shobha Venkataraman, Brandon Amos
http://arxiv.org/abs/2107.10254v1
• [cs.LG]On the Memorization Properties of Contrastive Learning
Ildus Sadrtdinov, Nadezhda Chirkova, Ekaterina Lobacheva
http://arxiv.org/abs/2107.10143v1
• [cs.LG]Online structural kernel selection for mobile health
Eura Shin, Pedja Klasnja, Susan Murphy, Finale Doshi-Velez
http://arxiv.org/abs/2107.09949v1
• [cs.LG]Preventing dataset shift from breaking machine-learning biomarkers
Jéroôme Dockès, Gaël Varoquaux, Jean-Baptiste Poline
http://arxiv.org/abs/2107.09947v1
• [cs.LG]Private Alternating Least Squares: Practical Private Matrix Completion with Tighter Rates
Steve Chien, Prateek Jain, Walid Krichene, Steffen Rendle, Shuang Song, Abhradeep Thakurta, Li Zhang
http://arxiv.org/abs/2107.09802v1
• [cs.LG]Relational Graph Convolutional Networks: A Closer Look
Thiviyan Thanapalasingam, Lucas van Berkel, Peter Bloem, Paul Groth
http://arxiv.org/abs/2107.10015v1
• [cs.LG]Statistical Estimation from Dependent Data
Yuval Dagan, Constantinos Daskalakis, Nishanth Dikkala, Surbhi Goel, Anthimos Vardis Kandiros
http://arxiv.org/abs/2107.09773v1
• [cs.LG]Toward Collaborative Reinforcement Learning Agents that Communicate Through Text-Based Natural Language
Kevin Eloff, Herman A. Engelbrecht
http://arxiv.org/abs/2107.09356v2
• [cs.LG]Training Electric Vehicle Charging Controllers with Imitation Learning
Martin Pilát
http://arxiv.org/abs/2107.10111v1
• [cs.LG]Uncertainty Estimation and Out-of-Distribution Detection for Counterfactual Explanations: Pitfalls and Solutions
Eoin Delaney, Derek Greene, Mark T. Keane
http://arxiv.org/abs/2107.09734v1
• [cs.LO]JEFL: Joint Embedding of Formal Proof Libraries
Qingxiang Wang, Cezary Kaliszyk
http://arxiv.org/abs/2107.10188v1
• [cs.LO]Learning Theorem Proving Components
Karel Chvalovský, Jan Jakubův, Miroslav Olšák, Josef Urban
http://arxiv.org/abs/2107.10034v1
• [cs.MA]Multi-agent Reinforcement Learning Improvement in a Dynamic Environment Using Knowledge Transfer
Mahnoosh Mahdavimoghaddama, Amin Nikanjama, Monireh Abdoos
http://arxiv.org/abs/2107.09807v1
• [cs.MM]Objective video quality metrics application to video codecs comparisons: choosing the best for subjective quality estimation
Anastasia Antsiferova, Alexander Yakovenko, Nickolay Safonov, Dmitriy Kulikov, Alexander Gushin, Dmitriy Vatolin
http://arxiv.org/abs/2107.10220v1
• [cs.NE]An Efficient Multi-objective Evolutionary Approach for Solving the Operation of Multi-Reservoir System Scheduling in Hydro-Power Plants
C. G. Marcelino, G. M. C. Leite, C. A. D. M Delgado, L. B. de Oliveira, E. F. Wanner, S. Jiménez-Fernández, S. Salcedo-Sanz
http://arxiv.org/abs/2107.09718v1
• [cs.NE]An Exploration of Exploration: Measuring the ability of lexicase selection to find obscure pathways to optimality
Jose Guadalupe Hernandez, Alexander Lalejini, Charles Ofria
http://arxiv.org/abs/2107.09760v1
• [cs.NE]Evolutionary Innovation Viewed as Novel Physical Phenomena and Hierarchical Systems Building
Tim Taylor
http://arxiv.org/abs/2107.09669v1
• [cs.NI]Into Summarization Techniques for IoT Data Discovery Routing
Hieu Tran, Son Nguyen, I-Ling Yen, Farokh Bastani
http://arxiv.org/abs/2107.09558v2
• [cs.RO]A Factor Graph-based approach to vehicle sideslip angle estimation
Antonio Leanza, Giulio Reina, Jose-Luis Blanco-Claraco
http://arxiv.org/abs/2107.09815v1
• [cs.RO]Assured Mission Adaptation of UAVs
Sebastián Zudaire, Leandro Nahabedian, Sebastián Uchitel
http://arxiv.org/abs/2107.10173v1
• [cs.RO]Bayesian Controller Fusion: Leveraging Control Priors in Deep Reinforcement Learning for Robotics
Krishan Rana, Vibhavari Dasagi, Jesse Haviland, Ben Talbot, Michael Milford, Niko Sünderhauf
http://arxiv.org/abs/2107.09822v1
• [cs.RO]Enumeration of Polyominoes & Polycubes Composed of Magnetic Cubes
Yitong Lu, Anuruddha Bhattacharjee, Daniel Biediger, Min Jun Kim, Aaron T. Becker
http://arxiv.org/abs/2107.10167v1
• [cs.RO]Learning compliant grasping and manipulation by teleoperation with adaptive force control
Chao Zeng, Shuang Li, Yiming Jiang, Qiang Li, Zhaopeng Chen, Chenguang Yang, Jianwei Zhang
http://arxiv.org/abs/2107.08996v2
• [cs.RO]Levels of Automation for a Mobile Robot Teleoperated by a Caregiver
Samuel Olatunji, Andre Potenza, Andrey Kiselev, Tal Oron-Gilad, Amy Loutfi, Yael Edan
http://arxiv.org/abs/2107.09992v1
• [cs.RO]MarsExplorer: Exploration of Unknown Terrains via Deep Reinforcement Learning and Procedurally Generated Environments
Dimitrios I. Koutras, Athanasios Ch. Kapoutsis, Angelos A. Amanatiadis, Elias B. Kosmatopoulos
http://arxiv.org/abs/2107.09996v1
• [cs.RO]Multi-Agent Belief Sharing through Autonomous Hierarchical Multi-Level Clustering
Mirco Theile, Jonathan Ponniah, Or Dantsker, Marco Caccamo
http://arxiv.org/abs/2107.09973v1
• [cs.RO]Track based Offline Policy Learning for Overtaking Maneuvers with Autonomous Racecars
Jayanth Bhargav, Johannes Betz, Hongrui Zheng, Rahul Mangharam
http://arxiv.org/abs/2107.09782v1
• [cs.SE]Predicting Issue Types on GitHub
Rafael Kallis, Andrea Di Sorbo, Gerardo Canfora, Sebastiano Panichella
http://arxiv.org/abs/2107.09936v1
• [cs.SI]Characterizing Social Imaginaries and Self-Disclosures of Dissonance in Online Conspiracy Discussion Communities
Shruti Phadke, Mattia Samory, Tanushree Mitra
http://arxiv.org/abs/2107.10204v1
• [cs.SI]Robust subgraph counting with distribution-free random graph analysis
Johan S. H. van Leeuwaarden, Clara Stegehuis
http://arxiv.org/abs/2107.10089v1
• [eess.AS]Audio Captioning Transformer
Xinhao Mei, Xubo Liu, Qiushi Huang, Mark D. Plumbley, Wenwu Wang
http://arxiv.org/abs/2107.09817v1
• [eess.AS]CL4AC: A Contrastive Loss for Audio Captioning
Xubo Liu, Qiushi Huang, Xinhao Mei, Tom Ko, H Lilian Tang, Mark D. Plumbley, Wenwu Wang
http://arxiv.org/abs/2107.09990v1
• [eess.AS]Conditional Sound Generation Using Neural Discrete Time-Frequency Representation Learning
Xubo Liu, Turab Iqbal, Jinzheng Zhao, Qiushi Huang, Mark D. Plumbley, Wenwu Wang
http://arxiv.org/abs/2107.09998v1
• [eess.IV]10fe
Modality-aware Mutual Learning for Multi-modal Medical Image Segmentation
Yao Zhang, Jiawei Yang, Jiang Tian, Zhongchao Shi, Cheng Zhong, Yang Zhang, Zhiqiang He
http://arxiv.org/abs/2107.09842v1
• [eess.IV]3D fluorescence microscopy data synthesis for segmentation and benchmarking
Dennis Eschweiler, Malte Rethwisch, Mareike Jarchow, Simon Koppers, Johannes Stegmaier
http://arxiv.org/abs/2107.10180v1
• [eess.IV]3D-StyleGAN: A Style-Based Generative Adversarial Network for Generative Modeling of Three-Dimensional Medical Images
Sungmin Hong, Razvan Marinescu, Adrian V. Dalca, Anna K. Bonkhoff, Martin Bretzner, Natalia S. Rost, Polina Golland
http://arxiv.org/abs/2107.09700v1
• [eess.IV]A Point Cloud Generative Model via Tree-Structured Graph Convolutions for 3D Brain Shape Reconstruction
Bowen Hu, Baiying Lei, Yanyan Shen, Yong Liu, Shuqiang Wang
http://arxiv.org/abs/2107.09923v1
• [eess.IV]High-Resolution Pelvic MRI Reconstruction Using a Generative Adversarial Network with Attention and Cyclic Loss
Guangyuan Li, Jun Lv, Xiangrong Tong, Chengyan Wang, Guang Yang
http://arxiv.org/abs/2107.09989v1
• [eess.IV]HistoCartography: A Toolkit for Graph Analytics in Digital Pathology
Guillaume Jaume, Pushpak Pati, Valentin Anklin, Antonio Foncubierta, Maria Gabrani
http://arxiv.org/abs/2107.10073v1
• [eess.IV]Towards Lower-Dose PET using Physics-Based Uncertainty-Aware Multimodal Learning with Robustness to Out-of-Distribution Data
Viswanath P. Sudarshan, Uddeshya Upadhyay, Gary F. Egan, Zhaolin Chen, Suyash P. Awate
http://arxiv.org/abs/2107.09892v1
• [eess.SP]EMG Pattern Recognition via Bayesian Inference with Scale Mixture-Based Stochastic Generative Models
Akira Furui, Takuya Igaue, Toshio Tsuji
http://arxiv.org/abs/2107.09853v1
• [eess.SP]KalmanNet: Neural Network Aided Kalman Filtering for Partially Known Dynamics
Guy Revach, Nir Shlezinger, Xiaoyong Ni, Adria Lopez Escoriza, Ruud J. G. van Sloun, Yonina C. Eldar
http://arxiv.org/abs/2107.10043v1
• [eess.SY]Optimal Operation of Power Systems with Energy Storage under Uncertainty: A Scenario-based Method with Strategic Sampling
Ren Hu, Qifeng Li
http://arxiv.org/abs/2107.10013v1
• [eess.SY]Strategic Mitigation of Agent Inattention in Drivers with Open-Quantum Cognition Models
Qizi Zhang, Venkata Sriram Siddhardh Nadendla, S. N. Balakrishnan, Jerome Busemeyer
http://arxiv.org/abs/2107.09888v1
• [math.OC]Learning a Large Neighborhood Search Algorithm for Mixed Integer Programs
Nicolas Sonnerat, Pengming Wang, Ira Ktena, Sergey Bartunov, Vinod Nair
http://arxiv.org/abs/2107.10201v1
• [math.ST]Extracting Governing Laws from Sample Path Data of Non-Gaussian Stochastic Dynamical Systems
Yang Li, Jinqiao Duan
http://arxiv.org/abs/2107.10127v1
• [math.ST]Inner spike and slab Bayesian nonparametric models
Antonio Canale, Antonio Lijoi, Bernardo Nipoti, Igor Prünster
http://arxiv.org/abs/2107.10223v1
• [math.ST]Linear spectral statistics of sequential sample covariance matrices
Nina Dörnemann, Holger Dette
http://arxiv.org/abs/2107.10036v1
• [math.ST]On ageing properties of lifetime distributions
Anakha K K, V M Chacko
http://arxiv.org/abs/2107.09921v1
• [math.ST]Optimal Rates for Nonparametric Density Estimation under Communication Constraints
Jayadev Acharya, Clément L. Canonne, Aditya Vikram Singh, Himanshu Tyagi
http://arxiv.org/abs/2107.10078v1
• [physics.chem-ph]Predicting trajectory behaviour via machine-learned invariant manifolds
Vladimír Krajňák, Shibabrat Naik, Stephen Wiggins
http://arxiv.org/abs/2107.10154v1
• [physics.data-an]Manifold learning-based polynomial chaos expansions for high-dimensional surrogate models
Katiana Kontolati, Dimitrios Loukrezis, Ketson R. M. dos Santos, Dimitrios G. Giovanis, Michael D. Shields
http://arxiv.org/abs/2107.09814v1
• [q-fin.TR]Order Book Queue Hawkes-Markovian Modeling
Philip Protter, Qianfan Wu, Shihao Yang
http://arxiv.org/abs/2107.09629v2
• [quant-ph]Modulus of continuity of the quantum -entropy with respect to the trace distance
Iosif Pinelis
http://arxiv.org/abs/2107.10112v1
• [quant-ph]Quantum Measurement Classification with Qudits
Diego H. Useche, Andres Giraldo-Carvajal, Hernan M. Zuluaga-Bucheli, Jose A. Jaramillo-Villegas, Fabio A. González
http://arxiv.org/abs/2107.09781v1
• [stat.AP]Decoupling Systemic Risk into Endopathic and Exopathic Competing Risks Through Autoregressive Conditional Accelerated Fréchet Model
Jingyu Ji, Deyuan Li, Zhengjun Zhang
http://arxiv.org/abs/2107.10148v1
• [stat.AP]The impact of increasing COVID-19 cases/deaths on the number of uncivil tweets directed at governments
Kohei Nishi
http://arxiv.org/abs/2107.10041v1
• [stat.AP]Tracking the Transmission Dynamics of COVID-19 with a Time-Varying Coefficient State-Space Model
Joshua P. Keller, Tianjian Zhou, Andee Kaplan, G. Brooke Anderson, Wen Zhou
http://arxiv.org/abs/2107.10118v1
• [stat.ME]A Stochastic Version of the EM Algorithm for Mixture Cure Rate Model with Exponentiated Weibull Family of Lifetimes
Sandip Barui, Suvra Pal, Nutan Mishra, Katherine Davies
http://arxiv.org/abs/2107.09810v1
• [stat.ME]Bayesian iterative screening in ultra-high dimensional settings
Run Wang, Somak Dutta, Vivekananda Roy
http://arxiv.org/abs/2107.10175v1
• [stat.ME]Evaluating Effectiveness of Public Health Intervention Strategies for Mitigating COVID-19 Pandemic
Shanghong Xie, Wenbo Wang, Qinxia Wang, Yuanjia Wang, Donglin Zeng
http://arxiv.org/abs/2107.09749v1
• [stat.ME]Frequentist inference for cluster randomised trials with multiple primary outcomes
Samuel I Watson, Joshua Akinyemi, Karla Hemming
http://arxiv.org/abs/2107.10017v1
• [stat.ME]Improving the Power to Detect Indirect Effects in Mediation Analysis
John Kidd, Dan-Yu Lin
http://arxiv.org/abs/2107.09812v1
• [stat.ME]Log-symmetric models with cure fraction with application to leprosy reactions data
Joyce B. Rocha, Francisco M. C. Medeiros, Dione M. Valença
http://arxiv.org/abs/2107.09757v1
• [stat.ME]Strategies for variable selection in large-scale healthcare database studies with missing covariate and outcome data
Jung-Yi Joyce Lin, Liangyuan Hu, Chuyue Huang, Steven Lawrence, Usha Govindarajulu
http://arxiv.org/abs/2107.09730v1
• [stat.ML]A variational approximate posterior for the deep Wishart process
Sebastian W. Ober, Laurence Aitchison
http://arxiv.org/abs/2107.10125v1
• [stat.ML]Adaptive Inducing Points Selection For Gaussian Processes
Théo Galy-Fajou, Manfred Opper
http://arxiv.org/abs/2107.10066v1
• [stat.ML]Delving Into Deep Walkers: A Convergence Analysis of Random-Walk-Based Vertex Embeddings
Dominik Kloepfer, Angelica I. Aviles-Rivero, Daniel Heydecker
http://arxiv.org/abs/2107.10014v1
• [stat.ML]Differentiable Annealed Importance Sampling and the Perils of Gradient Noise
Guodong Zhang, Kyle Hsu, Jianing Li, Chelsea Finn, Roger Grosse
http://arxiv.org/abs/2107.10211v1
• [stat.ML]Discovering Latent Causal Variables via Mechanism Sparsity: A New Principle for Nonlinear ICA
Sébastien Lachapelle, Pau Rodríguez López, Rémi Le Priol, Alexandre Lacoste, Simon Lacoste-Julien
http://arxiv.org/abs/2107.10098v1
• [stat.ML]On the Convergence of Prior-Guided Zeroth-Order Optimization Algorithms
Shuyu Cheng, Guoqiang Wu, Jun Zhu
http://arxiv.org/abs/2107.10110v1
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