cond-mat.stat-mech - 统计数学

    cs.AI - 人工智能 cs.CC - 计算复杂度 cs.CG - 计算几何学 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.HC - 人机接口 cs.IT - 信息论 cs.LG - 自动学习 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 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.NA - 数值分析 math.OC - 优化与控制 math.PR - 概率 math.ST - 统计理论 physics.med-ph - 医学物理学 physics.soc-ph - 物理学与社会 q-bio.NC - 神经元与认知 q-fin.GN - 通用财务 q-fin.ST - 统计金融学 quant-ph - 量子物理 stat.AP - 应用统计 stat.ME - 统计方法论 stat.ML - (统计)机器学习

    • [cond-mat.stat-mech]Efficient training of energy-based models via spin-glass control
    • [cs.AI]A Commentary on “Breaking Row and Column Symmetries in Matrix Models”
    • [cs.AI]Efficient Local Causal Discovery Based on Markov Blanket
    • [cs.AI]GRAVITAS: A Model Checking Based Planning and Goal Reasoning Framework for Autonomous Systems
    • [cs.AI]Method for the semantic indexing of concept hierarchies, uniform representation, use of relational database systems and generic and case-based reasoning
    • [cs.CC]Optimal Joint Subcarrier and Power Allocation in NOMA is Strongly NP-Hard
    • [cs.CG]A Grid-based Approach for Convexity Analysis of a Density-based Cluster
    • [cs.CL]Complex networks based word embeddings
    • [cs.CL]Cracking the Contextual Commonsense Code: Understanding Commonsense Reasoning Aptitude of Deep Contextual Representations
    • [cs.CL]Data-Efficient Goal-Oriented Conversation with Dialogue Knowledge Transfer Networks
    • [cs.CL]Extracting UMLS Concepts from Medical Text Using General and Domain-Specific Deep Learning Models
    • [cs.CL]Hitachi at MRP 2019: Unified Encoder-to-Biaffine Network for Cross-Framework Meaning Representation Parsing
    • [cs.CL]Identifying Nuances in Fake News vs. Satire: Using Semantic and Linguistic Cues
    • [cs.CL]Linking artificial and human neural representations of language
    • [cs.CL]Mapping (Dis-)Information Flow about the MH17 Plane Crash
    • [cs.CL]Modeling Color Terminology Across Thousands of Languages
    • [cs.CL]Neural Zero-Inflated Quality Estimation Model For Automatic Speech Recognition System
    • [cs.CL]TexTrolls: Identifying Russian Trolls on Twitter from a Textual Perspective
    • [cs.CL]Topic-aware Pointer-Generator Networks for Summarizing Spoken Conversations
    • [cs.CL]Towards Understanding of Medical Randomized Controlled Trials by Conclusion Generation
    • [cs.CR]A Cryptanalysis of Two Cancelable Biometric Schemes based on Index-of-Max Hashing
    • [cs.CR]A Data Science Approach for Honeypot Detection in Ethereum
    • [cs.CR]Coded Merkle Tree: Solving Data Availability Attacks in Blockchains
    • [cs.CR]Persistent and Unforgeable Watermarks for Deep Neural Networks
    • [cs.CV]3D Neighborhood Convolution: Learning Depth-Aware Features for RGB-D and RGB Semantic Segmentation
    • [cs.CV]A Neural Network for Detailed Human Depth Estimation from a Single Image
    • [cs.CV]ANDA: A Novel Data Augmentation Technique Applied to Salient Object Detection
    • [cs.CV]Automatic Group Cohesiveness Detection With Multi-modal Features
    • [cs.CV]CLEVRER: CoLlision Events for Video REpresentation and Reasoning
    • [cs.CV]DeepMark: One-Shot Clothing Detection
    • [cs.CV]Embodied Language Grounding with Implicit 3D Visual Feature Representations
    • [cs.CV]Face Manifold: Manifold Learning for Synthetic Face Generation
    • [cs.CV]IIITM Face: A Database for Facial Attribute Detection in Constrained and Simulated Unconstrained Environments
    • [cs.CV]Incremental learning for the detection and classification of GAN-generated images
    • [cs.CV]Learning Dense Wide Baseline Stereo Matching for People
    • [cs.CV]Learning Point Embeddings from Shape Repositories for Few-Shot Segmentation
    • [cs.CV]Learning Temporal Action Proposals With Fewer Labels
    • [cs.CV]OpenVSLAM: A Versatile Visual SLAM Framework
    • [cs.CV]ROMark: A Robust Watermarking System Using Adversarial Training
    • [cs.CV]Score-CAM:Improved Visual Explanations Via Score-Weighted Class Activation Mapping
    • [cs.CV]Self-supervised learning for autonomous vehicles perception: A conciliation between analytical and learning methods
    • [cs.CV]Sit-to-Stand Analysis in the Wild using Silhouettes for Longitudinal Health Monitoring
    • [cs.CV]Slanted Stixels: A way to represent steep streets
    • [cs.CV]Using Image Priors to Improve Scene Understanding
    • [cs.CV]Weakly supervised segmentation from extreme points
    • [cs.CV]YOLO Nano: a Highly Compact You Only Look Once Convolutional Neural Network for Object Detection
    • [cs.CY]Annotating Antisemitic Online Content. Towards an Applicable Definition of Antisemitism
    • [cs.CY]MASS-UMAP: Fast and accurate analog ensemble search in weather radar archive
    • [cs.CY]Mobile Phone Use as Sequential Processes: From Discrete Behaviors to Sessions of Behaviors and Trajectories of Sessions
    • [cs.CY]The tension between openness and prudence in AI research
    • [cs.DC]Flexible Development of Dependability Services: An Experience Derived from Energy Automation Systems
    • [cs.DC]Optimal Patrolling of High Priority Segments While Visiting the Unit Interval with a Set of Mobile Robots
    • [cs.DC]Running Alchemist on Cray XC and CS Series Supercomputers: Dask and PySpark Interfaces, Deployment Options, and Data Transfer Times
    • [cs.DC]SAFA: a Semi-Asynchronous Protocol for Fast Federated Learning with Low Overhead
    • [cs.DC]Scheduling Stochastic Real-Time Coflows in Unreliable Computing Machines
    • [cs.DC]Toward Edge-enabled Cyber-Physical Systems Testbeds
    • [cs.HC]Adaptive Generation of Phantom Limbs Using Visible Hierarchical Autoencoders
    • [cs.HC]User-Adaptive Text Entry for Augmentative and Alternative Communication
    • [cs.IT]Capacity Characterization for Intelligent Reflecting Surface Aided MIMO Communication
    • [cs.IT]Low Complexity LMMSE Receiver for OTFS
    • [cs.IT]Minimax Bounds for Distributed Logistic Regression
    • [cs.IT]On the Stable Resolution Limit of Total Variation Regularization for Spike Deconvolution
    • [cs.IT]Skewness of von Neumann entanglement entropy
    • [cs.IT]Ternary Quantized Polar Code Decoders: Analysis and Design
    • [cs.LG]A Function Space View of Bounded Norm Infinite Width ReLU Nets: The Multivariate Case
    • [cs.LG]A General Upper Bound for Unsupervised Domain Adaptation
    • [cs.LG]Accelerating Data Loading in Deep Neural Network Training
    • [cs.LG]An empirical study of pretrained representations for few-shot classification
    • [cs.LG]Analyzing the Variance of Policy Gradient Estimators for the Linear-Quadratic Regulator
    • [cs.LG]Beyond Linearization: On Quadratic and Higher-Order Approximation of Wide Neural Networks
    • [cs.LG]Blood lactate concentration prediction in critical care patients: handling missing values
    • [cs.LG]Deep Reinforcement Learning for Single-Shot Diagnosis and Adaptation in Damaged Robots
    • [cs.LG]Elastic-InfoGAN: Unsupervised Disentangled Representation Learning in Imbalanced Data
    • [cs.LG]Emotion Recognition with Spatial Attention and Temporal Softmax Pooling
    • [cs.LG]Exploiting multi-CNN features in CNN-RNN based Dimensional Emotion Recognition on the OMG in-the-wild Dataset
    • [cs.LG]Geometric Online Adaptation: Graph-Based OSFS for Streaming Samples
    • [cs.LG]Graph Analysis and Graph Pooling in the Spatial Domain
    • [cs.LG]Graph-Hist: Graph Classification from Latent Feature Histograms With Application to Bot Detection
    • [cs.LG]LabelSens: Enabling Real-time Sensor Data Label-ling at the point of Collection on Edge Computing
    • [cs.LG]Learning Calibratable Policies using Programmatic Style-Consistency
    • [cs.LG]MLPerf Training Benchmark
    • [cs.LG]On Universal Approximation by Neural Networks with Uniform Guarantees on Approximation of Infinite Dimensional Maps
    • [cs.LG]On the Efficacy of Knowledge Distillation
    • [cs.LG]Partial differential equation regularization for supervised machine learning
    • [cs.LG]Pay Attention: Leveraging Sequence Models to Predict the Useful Life of Batteries
    • [cs.LG]Perturbations are not Enough: Generating Adversarial Examples with Spatial Distortions
    • [cs.LG]Prediction of GNSS Phase Scintillations: A Machine Learning Approach
    • [cs.LG]Reducing Overestimation Bias in Multi-Agent Domains Using Double Centralized Critics
    • [cs.LG]Regularizing Neural Networks via Stochastic Branch Layers
    • [cs.LG]Silas: High Performance, Explainable and Verifiable Machine Learning
    • [cs.LG]Stochastic Bandits with Delayed Composite Anonymous Feedback
    • [cs.LG]The Bouncer Problem: Challenges to Remote Explainability
    • [cs.LG]Variational PSOM: Deep Probabilistic Clustering with Self-Organizing Maps
    • [cs.NE]A Hybrid Cooperative Co-evolution Algorithm Framework for Optimising Power Take Off and Placements of Wave Energy Converters
    • [cs.NE]Bootstrapping Conditional GANs for Video Game Level Generation
    • [cs.NE]Neural Network Applications in Earthquake Prediction (1994-2019): Meta-Analytic Insight on their Limitations
    • [cs.NI]RouteNet: Leveraging Graph Neural Networks for network modeling and optimization in SDN
    • [cs.NI]SensorDrop: A Reinforcement Learning Framework for Communication Overhead Reduction on the Edge
    • [cs.RO]A Mobile Manipulation System for One-Shot Teaching of Complex Tasks in Homes
    • [cs.RO]CyPhyHouse: A Programming, Simulation, and Deployment Toolchain for Heterogeneous Distributed Coordination
    • [cs.RO]Distributed Attack-Robust Submodular Maximization for Multi-Robot Planning
    • [cs.RO]Exoskeleton-covered soft finger with vision-based proprioception and exteroception
    • [cs.RO]Pose Estimation for Omni-directional Cameras using Sinusoid Fitting
    • [cs.RO]Resilience in multi-robot target tracking through reconfiguration
    • [cs.SI]From Senseless Swarms to Smart Mobs: Tuning Networks for Prosocial Behaviour
    • [cs.SI]Introducing multilayer stream graphs and layer centralities
    • [eess.AS]From Senones to Chenones: Tied Context-Dependent Graphemes for Hybrid Speech Recognition
    • [eess.IV]Cardiac Segmentation of LGE MRI with Noisy Labels
    • [eess.IV]High-dimensional Dense Residual Convolutional Neural Network for Light Field Reconstruction
    • [eess.IV]Improving Limited Angle CT Reconstruction with a Robust GAN Prior
    • [eess.IV]Kidney Recognition in CT Using YOLOv3
    • [eess.IV]The LoDoPaB-CT Dataset: A Benchmark Dataset for Low-Dose CT Reconstruction Methods
    • [eess.SP]Exploring Positive Noise in Estimation Theory
    • [eess.SP]Fault Detection Using Nonlinear Low-Dimensional Representation of Sensor Data
    • [eess.SY]Verification of Neural Network Behaviour: Formal Guarantees for Power System Applications
    • [math.NA]A deep surrogate approach to efficient Bayesian inversion in PDE and integral equation models
    • [math.OC]A sparse semismooth Newton based augmented Lagrangian method for large-scale support vector machines
    • [math.OC]Escaping Saddle Points for Zeroth-order Nonconvex Optimization using Estimated Gradient Descent
    • [math.PR]Generalized bounds for active subspaces
    • [math.ST]Empirical Likelihood Under Mis-specification: Degeneracies and Random Critical Points
    • [math.ST]On some spectral properties of stochastic similarity matrices for data clustering
    • [math.ST]Privately detecting changes in unknown distributions
    • [math.ST]Statistical inference of subcritical strongly stationary Galton—Watson processes with regularly varying immigration
    • [physics.med-ph]Design of an assistive trunk exoskeleton based on multibody dynamic modelling
    • [physics.soc-ph]Spatial Strength Centrality and the Effect of Spatial Embeddings on Network Architecture
    • [q-bio.NC]Inference of a mesoscopic population model from population spike trains
    • [q-fin.GN]A tale of two sentiment scales: Disentangling short-run and long-run components in multivariate sentiment dynamics
    • [q-fin.ST]A Robust Transferable Deep Learning Framework for Cross-sectional Investment Strategy
    • [quant-ph]Quantum tensor singular value decomposition with applications to recommendation systems
    • [quant-ph]Stochastic gradient descent for hybrid quantum-classical optimization
    • [stat.AP]A Simple Yet Efficient Parametric Method of Local False Discovery Rate Estimation Designed for Genome-Wide Association Data Analysis
    • [stat.AP]Assessing the predictive ability of the UPDRS for falls classification in early stage Parkinson’s disease
    • [stat.AP]Indicators of retention in remote digital health studies: A cross-study evaluation of 100,000 participants
    • [stat.AP]Modeling of Electrical Resistivity of Soil Based on Geotechnical Properties
    • [stat.AP]Robustly Extracting Medical Knowledge from EHRs: A Case Study of Learning a Health Knowledge Graph
    • [stat.AP]The effects of degrees of freedom estimation in the Asymmetric GARCH model with Student-t Innovations
    • [stat.ME]A Pseudo-Likelihood Approach to Linear Regression with Partially Shuffled Data
    • [stat.ME]Combining multiple imputation with raking of weights in the setting of nearly-true models
    • [stat.ML]A note on the consistency of the random forest algorithm
    • [stat.ML]Causal inference with Bayes rule
    • [stat.ML]Distillation $\approx$ Early Stopping? Harvesting Dark Knowledge Utilizing Anisotropic Information Retrieval For Overparameterized Neural Network
    • [stat.ML]Generalization Bounds for Convolutional Neural Networks
    • [stat.ML]Improving Differentially Private Models with Active Learning
    • [stat.ML]Reconsidering Analytical Variational Bounds for Output Layers of Deep Networks
    • [stat.ML]Robust Risk Minimization for Statistical Learning

    ·····································

    • [cond-mat.stat-mech]Efficient training of energy-based models via spin-glass control
    Alejandro Pozas-Kerstjens, Gorka Muñoz-Gil, Miguel Ángel García-March, Antonio Acín, Maciej Lewenstein, Przemysław R. Grzybowski
    http://arxiv.org/abs/1910.01592v1

    • [cs.AI]A Commentary on “Breaking Row and Column Symmetries in Matrix Models”
    Alan M. Frisch, Brahim Hnich, Zeynep Kiziltan, Ian Miguel, Toby Walsh
    http://arxiv.org/abs/1910.01423v1

    • [cs.AI]Efficient Local Causal Discovery Based on Markov Blanket
    Shuai Yang, Hao Wang, Xuegang hu
    http://arxiv.org/abs/1910.01288v1

    • [cs.AI]GRAVITAS: A Model Checking Based Planning and Goal Reasoning Framework for Autonomous Systems
    Hadrien Bride, Jin Song Dong, Ryan Green, Zhe Hou, Brendan Mahony, Martin Oxenham
    http://arxiv.org/abs/1910.01380v1

    • [cs.AI]Method for the semantic indexing of concept hierarchies, uniform representation, use of relational database systems and generic and case-based reasoning
    Uwe Petersohn, Sandra Zimmer, Jens Lehmann
    http://arxiv.org/abs/1910.01539v1

    • [cs.CC]Optimal Joint Subcarrier and Power Allocation in NOMA is Strongly NP-Hard
    Lou Salaun, Chung Shue Chen, Marceau Coupechoux
    http://arxiv.org/abs/1910.01331v1

    • [cs.CG]A Grid-based Approach for Convexity Analysis of a Density-based Cluster
    Sayyed-Ahmad Naghavi-Nozad, Seyed-Mojtaba Banaei, Mohsen Saberi
    http://arxiv.org/abs/1910.01492v1

    • [cs.CL]Complex networks based word embeddings
    Nicolas Dugué, Victor Connes
    http://arxiv.org/abs/1910.01489v1

    • [cs.CL]Cracking the Contextual Commonsense Code: Understanding Commonsense Reasoning Aptitude of Deep Contextual Representations
    Jeff Da, Jungo Kusai
    http://arxiv.org/abs/1910.01157v1

    • [cs.CL]Data-Efficient Goal-Oriented Conversation with Dialogue Knowledge Transfer Networks
    Igor Shalyminov, Sungjin Lee, Arash Eshghi, Oliver Lemon
    http://arxiv.org/abs/1910.01302v1

    • [cs.CL]Extracting UMLS Concepts from Medical Text Using General and Domain-Specific Deep Learning Models
    Kathleen C. Fraser, Isar Nejadgholi, Berry De Bruijn, Muqun Li, Astha LaPlante, Khaldoun Zine El Abidine
    http://arxiv.org/abs/1910.01274v1

    • [cs.CL]Hitachi at MRP 2019: Unified Encoder-to-Biaffine Network for Cross-Framework Meaning Representation Parsing
    Yuta Koreeda, Gaku Morio, Terufumi Morishita, Hiroaki Ozaki, Kohsuke Yanai
    http://arxiv.org/abs/1910.01299v1

    • [cs.CL]Identifying Nuances in Fake News vs. Satire: Using Semantic and Linguistic Cues
    Or Levi, Pedram Hosseini, Mona Diab, David A. Broniatowski
    http://arxiv.org/abs/1910.01160v1

    • [cs.CL]Linking artificial and human neural representations of language
    Jon Gauthier, Roger Levy
    http://arxiv.org/abs/1910.01244v1

    • [cs.CL]Mapping (Dis-)Information Flow about the MH17 Plane Crash
    Mareike Hartmann, Yevgeniy Golovchenko, Isabelle Augenstein
    http://arxiv.org/abs/1910.01363v1

    • [cs.CL]Modeling Color Terminology Across Thousands of Languages
    Arya D. McCarthy, Winston Wu, Aaron Mueller, Bill Watson, David Yarowsky
    http://arxiv.org/abs/1910.01531v1

    • [cs.CL]Neural Zero-Inflated Quality Estimation Model For Automatic Speech Recognition System
    Kai Fan, Jiayi Wang, Bo Li, Boxing Chen, Niyu Ge
    http://arxiv.org/abs/1910.01289v1

    • [cs.CL]TexTrolls: Identifying Russian Trolls on Twitter from a Textual Perspective
    Bilal Ghanem, Davide Buscaldi, Paolo Rosso
    http://arxiv.org/abs/1910.01340v1

    • [cs.CL]Topic-aware Pointer-Generator Networks for Summarizing Spoken Conversations
    Zhengyuan Liu, Angela Ng, Sheldon Lee, Ai Ti Aw, Nancy F. Chen
    http://arxiv.org/abs/1910.01335v1

    • [cs.CL]Towards Understanding of Medical Randomized Controlled Trials by Conclusion Generation
    Alexander Te-Wei Shieh, Yung-Sung Chuang, Shang-Yu Su, Yun-Nung Chen
    http://arxiv.org/abs/1910.01462v1

    • [cs.CR]A Cryptanalysis of Two Cancelable Biometric Schemes based on Index-of-Max Hashing
    Kevin Atighehchi, Loubna Ghammam, Koray Karabina, Patrick Lacharme
    http://arxiv.org/abs/1910.01389v1

    • [cs.CR]A Data Science Approach for Honeypot Detection in Ethereum
    Ramiro Camino, Christof Ferreira Torres, Radu State
    http://arxiv.org/abs/1910.01449v1

    • [cs.CR]Coded Merkle Tree: Solving Data Availability Attacks in Blockchains
    Mingchao Yu, Saeid Sahraei, Songze Li, Salman Avestimehr, Sreeram Kannan, Pramod Viswanath
    http://arxiv.org/abs/1910.01247v1

    • [cs.CR]Persistent and Unforgeable Watermarks for Deep Neural Networks
    Huiying Li, Emily Willson, Haitao Zheng, Ben Y. Zhao
    http://arxiv.org/abs/1910.01226v1

    • [cs.CV]3D Neighborhood Convolution: Learning Depth-Aware Features for RGB-D and RGB Semantic Segmentation
    Yunlu Chen, Thomas Mensink, Efstratios Gavves
    http://arxiv.org/abs/1910.01460v1

    • [cs.CV]A Neural Network for Detailed Human Depth Estimation from a Single Image
    Sicong Tang, Feitong Tan, Kelvin Cheng, Zhaoyang Li, Siyu Zhu, Ping Tan
    http://arxiv.org/abs/1910.01275v1

    • [cs.CV]ANDA: A Novel Data Augmentation Technique Applied to Salient Object Detection
    Daniel V. Ruiz, Bruno A. Krinski, Eduardo Todt
    http://arxiv.org/abs/1910.01256v1

    • [cs.CV]Automatic Group Cohesiveness Detection With Multi-modal Features
    Bin Zhu, Xin Guo, Kenneth Barner, Charles Boncelet
    http://arxiv.org/abs/1910.01197v1

    • [cs.CV]CLEVRER: CoLlision Events for Video REpresentation and Reasoning
    Kexin Yi, Chuang Gan, Yunzhu Li, Pushmeet Kohli, Jiajun Wu, Antonio Torralba, Joshua B. Tenenbaum
    http://arxiv.org/abs/1910.01442v1

    • [cs.CV]DeepMark: One-Shot Clothing Detection
    Alexey Sidnev, Alexey Trushkov, Maxim Kazakov, Ivan Korolev, Vladislav Sorokin
    http://arxiv.org/abs/1910.01225v1

    • [cs.CV]Embodied Language Grounding with Implicit 3D Visual Feature Representations
    Mihir Prabhudesai, Hsiao-Yu Fish Tung, Syed Ashar Javed, Maximilian Sieb, Adam W. Harley, Katerina Fragkiadaki
    http://arxiv.org/abs/1910.01210v1

    • [cs.CV]Face Manifold: Manifold Learning for Synthetic Face Generation
    Kimia Dinashi, Ramin Toosi, Mohammad Ali Akhaee
    http://arxiv.org/abs/1910.01403v1

    • [cs.CV]IIITM Face: A Database for Facial Attribute Detection in Constrained and Simulated Unconstrained Environments
    Raj Kuwar Gupta, Shresth Verma, KV Arya, Soumya Agarwal, Prince Gupta
    http://arxiv.org/abs/1910.01219v1

    • [cs.CV]Incremental learning for the detection and classification of GAN-generated images
    Francesco Marra, Cristiano Saltori, Giulia Boato, Luisa Verdoliva
    http://arxiv.org/abs/1910.01568v1

    • [cs.CV]Learning Dense Wide Baseline Stereo Matching for People
    Akin Caliskan, Armin Mustafa, Evren Imre, Adrian Hilton
    http://arxiv.org/abs/1910.01241v1

    • [cs.CV]Learning Point Embeddings from Shape Repositories for Few-Shot Segmentation
    Gopal Sharma, Evangelos Kalogerakis, Subhransu Maji
    http://arxiv.org/abs/1910.01269v1

    • [cs.CV]Learning Temporal Action Proposals With Fewer Labels
    Jingwei Ji, Kaidi Cao, Juan Carlos Niebles
    http://arxiv.org/abs/1910.01286v1

    • [cs.CV]OpenVSLAM: A Versatile Visual SLAM Framework
    Shinya Sumikura, Mikiya Shibuya, Ken Sakurada
    http://arxiv.org/abs/1910.01122v1

    • [cs.CV]ROMark: A Robust Watermarking System Using Adversarial Training
    Bingyang Wen, Sergul Aydore
    http://arxiv.org/abs/1910.01221v1

    • [cs.CV]Score-CAM:Improved Visual Explanations Via Score-Weighted Class Activation Mapping
    Haofan Wang, Mengnan Du, Fan Yang, Zijian Zhang
    http://arxiv.org/abs/1910.01279v1

    • [cs.CV]Self-supervised learning for autonomous vehicles perception: A conciliation between analytical and learning methods
    Florent Chiaroni, Mohamed-Cherif Rahal, Nicolas Hueber, Frederic Dufaux
    http://arxiv.org/abs/1910.01636v1

    • [cs.CV]Sit-to-Stand Analysis in the Wild using Silhouettes for Longitudinal Health Monitoring
    Alessandro Masullo, Tilo Burghardt, Toby Perrett, Dima Damen, Majid Mirmehdi
    http://arxiv.org/abs/1910.01370v1

    • [cs.CV]Slanted Stixels: A way to represent steep streets
    Daniel Hernandez-Juarez, Lukas Schneider, Pau Cebrian, Antonio Espinosa, David Vazquez, Antonio M. Lopez, Uwe Franke, Marc Pollefeys, Juan C. Moure
    http://arxiv.org/abs/1910.01466v1

    • [cs.CV]Using Image Priors to Improve Scene Understanding
    Brigit Schroeder, Hanlin Tang, Alexandre Alahi
    http://arxiv.org/abs/1910.01198v1

    • [cs.CV]Weakly supervised segmentation from extreme points
    Holger Roth, Ling Zhang, Dong Yang, Fausto Milletari, Ziyue Xu, Xiaosong Wang, Daguang Xu
    http://arxiv.org/abs/1910.01236v1

    • [cs.CV]YOLO Nano: a Highly Compact You Only Look Once Convolutional Neural Network for Object Detection
    Alexander Wong, Mahmoud Famuori, Mohammad Javad Shafiee, Francis Li, Brendan Chwyl, Jonathan Chung
    http://arxiv.org/abs/1910.01271v1

    • [cs.CY]Annotating Antisemitic Online Content. Towards an Applicable Definition of Antisemitism
    Gunther Jikeli, Damir Cavar, Daniel Miehling
    http://arxiv.org/abs/1910.01214v1

    • [cs.CY]MASS-UMAP: Fast and accurate analog ensemble search in weather radar archive
    Gabriele Franch, Giuseppe Jurman, Luca Coviello, Marta Pendesini, Cesare Furlanello
    http://arxiv.org/abs/1910.01211v1

    • [cs.CY]Mobile Phone Use as Sequential Processes: From Discrete Behaviors to Sessions of Behaviors and Trajectories of Sessions
    Tai-Quan Peng, Jonathan J. H. Zhu
    http://arxiv.org/abs/1910.01290v1

    • [cs.CY]The tension between openness and prudence in AI research
    Jess Whittlestone, Aviv Ovadya
    http://arxiv.org/abs/1910.01170v1

    • [cs.DC]Flexible Development of Dependability Services: An Experience Derived from Energy Automation Systems
    Vincenzo De Florio, Susanna Donatelli, Giovanna Dondossola
    http://arxiv.org/abs/1910.01483v1

    • [cs.DC]Optimal Patrolling of High Priority Segments While Visiting the Unit Interval with a Set of Mobile Robots
    Oscar Morales-Ponce
    http://arxiv.org/abs/1910.01250v1

    • [cs.DC]Running Alchemist on Cray XC and CS Series Supercomputers: Dask and PySpark Interfaces, Deployment Options, and Data Transfer Times
    Kai Rothauge, Haripriya Ayyalasomayajula, Kristyn J. Maschhoff, Michael Ringenburg, Michael W. Mahoney
    http://arxiv.org/abs/1910.01354v1

    • [cs.DC]SAFA: a Semi-Asynchronous Protocol for Fast Federated Learning with Low Overhead
    Wentai Wu, Ligang He, Weiwei Lin, RuiMao, Stephen Jarvis
    http://arxiv.org/abs/1910.01355v1

    • [cs.DC]Scheduling Stochastic Real-Time Coflows in Unreliable Computing Machines
    Yu-Pin Hsu
    http://arxiv.org/abs/1910.00916v2

    • [cs.DC]Toward Edge-enabled Cyber-Physical Systems Testbeds
    V. K. Cody Bumgardner, Nima Seyedtalebi, Caylin Hickey
    http://arxiv.org/abs/1910.01173v1

    • [cs.HC]Adaptive Generation of Phantom Limbs Using Visible Hierarchical Autoencoders
    Dakila Ledesma, Yu Liang, Dalei Wu
    http://arxiv.org/abs/1910.01191v1

    • [cs.HC]User-Adaptive Text Entry for Augmentative and Alternative Communication
    Matt Higger, Fernando Quivira, Deniz Erdogmus
    http://arxiv.org/abs/1910.01216v1

    • [cs.IT]Capacity Characterization for Intelligent Reflecting Surface Aided MIMO Communication
    Shuowen Zhang, Rui Zhang
    http://arxiv.org/abs/1910.01573v1

    • [cs.IT]Low Complexity LMMSE Receiver for OTFS
    Shashank Tiwari, Suvra Sekhar das, Vivek Rangamgari
    http://arxiv.org/abs/1910.01350v1

    • [cs.IT]Minimax Bounds for Distributed Logistic Regression
    Leighton Pate Barnes, Ayfer Ozgur
    http://arxiv.org/abs/1910.01625v1

    • [cs.IT]On the Stable Resolution Limit of Total Variation Regularization for Spike Deconvolution
    Maxime Ferreira Da Costa, Yuejie Chi
    http://arxiv.org/abs/1910.01629v1

    • [cs.IT]Skewness of von Neumann entanglement entropy
    Lu Wei
    http://arxiv.org/abs/1910.01199v1

    • [cs.IT]Ternary Quantized Polar Code Decoders: Analysis and Design
    Joachim Neu, Mustafa Cemil Coşkun, Gianluigi Liva
    http://arxiv.org/abs/1910.01176v1

    • [cs.LG]A Function Space View of Bounded Norm Infinite Width ReLU Nets: The Multivariate Case
    Greg Ongie, Rebecca Willett, Daniel Soudry, Nathan Srebro
    http://arxiv.org/abs/1910.01635v1

    • [cs.LG]A General Upper Bound for Unsupervised Domain Adaptation
    Dexuan Zhang, Tatsuya Harada
    http://arxiv.org/abs/1910.01409v1

    • [cs.LG]Accelerating Data Loading in Deep Neural Network Training
    Chih-Chieh Yang, Guojing Cong
    http://arxiv.org/abs/1910.01196v1

    • [cs.LG]An empirical study of pretrained representations for few-shot classification
    Tiago Ramalho, Thierry Sousbie, Stefano Peluchetti
    http://arxiv.org/abs/1910.01319v1

    • [cs.LG]Analyzing the Variance of Policy Gradient Estimators for the Linear-Quadratic Regulator
    James A. Preiss, Sébastien M. R. Arnold, Chen-Yu Wei, Marius Kloft
    http://arxiv.org/abs/1910.01249v1

    • [cs.LG]Beyond Linearization: On Quadratic and Higher-Order Approximation of Wide Neural Networks
    Yu Bai, Jason D. Lee
    http://arxiv.org/abs/1910.01619v1

    • [cs.LG]Blood lactate concentration prediction in critical care patients: handling missing values
    Behrooz Mamandipoor, Mahshid Majd, Monica Moz, Venet Osmani
    http://arxiv.org/abs/1910.01473v1

    • [cs.LG]Deep Reinforcement Learning for Single-Shot Diagnosis and Adaptation in Damaged Robots
    Shresth Verma, Haritha S. Nair, Gaurav Agarwal, Joydip Dhar, Anupam Shukla
    http://arxiv.org/abs/1910.01240v1

    • [cs.LG]Elastic-InfoGAN: Unsupervised Disentangled Representation Learning in Imbalanced Data
    Utkarsh Ojha, Krishna Kumar Singh, Cho-Jui Hsieh, Yong Jae Lee
    http://arxiv.org/abs/1910.01112v1

    • [cs.LG]Emotion Recognition with Spatial Attention and Temporal Softmax Pooling
    Masih Aminbeidokhti, Marco Pedersoli, Patrick Cardinal, Eric Granger
    http://arxiv.org/abs/1910.01254v1

    • [cs.LG]Exploiting multi-CNN features in CNN-RNN based Dimensional Emotion Recognition on the OMG in-the-wild Dataset
    Dimitrios Kollias, Stefanos Zafeiriou
    http://arxiv.org/abs/1910.01417v1

    • [cs.LG]Geometric Online Adaptation: Graph-Based OSFS for Streaming Samples
    Salimeh Yasaei Sekeh, Madan Ravi Ganesh, Shurjo Banerjee, Jason J. Corso, Alfred O. Hero
    http://arxiv.org/abs/1910.01182v1

    • [cs.LG]Graph Analysis and Graph Pooling in the Spatial Domain
    Mostafa Rahmani, Ping Li
    http://arxiv.org/abs/1910.01589v1

    • [cs.LG]Graph-Hist: Graph Classification from Latent Feature Histograms With Application to Bot Detection
    Thomas Magelinski, David Beskow, Kathleen M. Carley
    http://arxiv.org/abs/1910.01180v1

    • [cs.LG]LabelSens: Enabling Real-time Sensor Data Label-ling at the point of Collection on Edge Computing
    Kieran Woodward, Eiman Kanjo, Andreas Oikonomou
    http://arxiv.org/abs/1910.01400v1

    • [cs.LG]Learning Calibratable Policies using Programmatic Style-Consistency
    Eric Zhan, Albert Tseng, Yisong Yue, Adith Swaminathan, Matthew Hausknecht
    http://arxiv.org/abs/1910.01179v1

    • [cs.LG]MLPerf Training Benchmark
    Peter Mattson, Christine Cheng, Cody Coleman, Greg Diamos, Paulius Micikevicius, David Patterson, Hanlin Tang, Gu-Yeon Wei, Peter Bailis, Victor Bittorf, David Brooks, Dehao Chen, Debojyoti Dutta, Udit Gupta, Kim Hazelwood, Andrew Hock, Xinyuan Huang, Bill Jia, Daniel Kang, David Kanter, Naveen Kumar, Jeffery Liao, Deepak Narayanan, Tayo Oguntebi, Gennady Pekhimenko, Lillian Pentecost, Vijay Janapa Reddi, Taylor Robie, Tom St. John, Carole-Jean Wu, Lingjie Xu, Cliff Young, Matei Zaharia
    http://arxiv.org/abs/1910.01500v1

    • [cs.LG]On Universal Approximation by Neural Networks with Uniform Guarantees on Approximation of Infinite Dimensional Maps
    William H. Guss, Ruslan Salakhutdinov
    http://arxiv.org/abs/1910.01545v1

    • [cs.LG]On the Efficacy of Knowledge Distillation
    Jang Hyun Cho, Bharath Hariharan
    http://arxiv.org/abs/1910.01348v1

    • [cs.LG]Partial differential equation regularization for supervised machine learning
    Adam M Oberman
    http://arxiv.org/abs/1910.01612v1

    • [cs.LG]Pay Attention: Leveraging Sequence Models to Predict the Useful Life of Batteries
    Samuel Paradis, Michael Whitmeyer
    http://arxiv.org/abs/1910.01347v1

    • [cs.LG]Perturbations are not Enough: Generating Adversarial Examples with Spatial Distortions
    He Zhao, Trung Le, Paul Montague, Olivier De Vel, Tamas Abraham, Dinh Phung
    http://arxiv.org/abs/1910.01329v1

    • [cs.LG]Prediction of GNSS Phase Scintillations: A Machine Learning Approach
    Kara Lamb, Garima Malhotra, Athanasios Vlontzos, Edward Wagstaff, Atılım Günes Baydin, Anahita Bhiwandiwalla, Yarin Gal, Alfredo Kalaitzis, Anthony Reina, Asti Bhatt
    http://arxiv.org/abs/1910.01570v1

    • [cs.LG]Reducing Overestimation Bias in Multi-Agent Domains Using Double Centralized Critics
    Johannes Ackermann, Volker Gabler, Takayuki Osa, Masashi Sugiyama
    http://arxiv.org/abs/1910.01465v1

    • [cs.LG]Regularizing Neural Networks via Stochastic Branch Layers
    Wonpyo Park, Paul Hongsuck Seo, Bohyung Han, Minsu Cho
    http://arxiv.org/abs/1910.01467v1

    • [cs.LG]Silas: High Performance, Explainable and Verifiable Machine Learning
    Hadrien Bride, Zhe Hou, Jie Dong, Jin Song Dong, Ali Mirjalili
    http://arxiv.org/abs/1910.01382v1

    • [cs.LG]Stochastic Bandits with Delayed Composite Anonymous Feedback
    Siddhant Garg, Aditya Kumar Akash
    http://arxiv.org/abs/1910.01161v1

    • [cs.LG]The Bouncer Problem: Challenges to Remote Explainability
    Erwan Le Merrer, Gilles Tredan
    http://arxiv.org/abs/1910.01432v1

    • [cs.LG]Variational PSOM: Deep Probabilistic Clustering with Self-Organizing Maps
    Laura Manduchi, Matthias Hüser, Gunnar Rätsch, Vincent Fortuin
    http://arxiv.org/abs/1910.01590v1

    • [cs.NE]A Hybrid Cooperative Co-evolution Algorithm Framework for Optimising Power Take Off and Placements of Wave Energy Converters
    Mehdi Neshat, Bradley Alexander, Markus Wagner
    http://arxiv.org/abs/1910.01280v1

    • [cs.NE]Bootstrapping Conditional GANs for Video Game Level Generation
    Ruben Rodriguez Torrado, Ahmed Khalifa, Michael Cerny Green, Niels Justesen, Sebastian Risi, Julian Togelius
    http://arxiv.org/abs/1910.01603v1

    • [cs.NE]Neural Network Applications in Earthquake Prediction (1994-2019): Meta-Analytic Insight on their Limitations
    Arnaud Mignan, Marco Broccardo
    http://arxiv.org/abs/1910.01178v1

    • [cs.NI]RouteNet: Leveraging Graph Neural Networks for network modeling and optimization in SDN
    Krzysztof Rusek, José Suárez-Varela, Paul Almasan, Pere Barlet-Ros, Albert Cabellos-Aparicio
    http://arxiv.org/abs/1910.01508v1

    • [cs.NI]SensorDrop: A Reinforcement Learning Framework for Communication Overhead Reduction on the Edge
    Pooya Khandel, Amir Hossein Rassafi, Vahid Pourahmadi, Saeed Sharifian, Rong Zheng
    http://arxiv.org/abs/1910.01601v1

    • [cs.RO]A Mobile Manipulation System for One-Shot Teaching of Complex Tasks in Homes
    Max Bajracharya, James Borders, Dan Helmick, Thomas Kollar, Michael Laskey, John Leichty, Jeremy Ma, Umashankar Nagarajan, Akiyoshi Ochiai, Josh Petersen, Krishna Shankar, Kevin Stone, Yutaka Takaoka
    http://arxiv.org/abs/1910.00127v2

    • [cs.RO]CyPhyHouse: A Programming, Simulation, and Deployment Toolchain for Heterogeneous Distributed Coordination
    Ritwika Ghosh, Joao P. Jansch-Porto, Chiao Hsieh, Amelia Gosse, Minghao Jiang, Hebron Taylor, Peter Du, Sayan Mitra, Geir Dullerud
    http://arxiv.org/abs/1910.01557v1

    • [cs.RO]Distributed Attack-Robust Submodular Maximization for Multi-Robot Planning
    Lifeng Zhou, Vasileios Tzoumas, George J. Pappas, Pratap Tokekar
    http://arxiv.org/abs/1910.01208v1

    • [cs.RO]Exoskeleton-covered soft finger with vision-based proprioception and exteroception
    Yu She, Sandra Q. Liu, Peiyu Yu, Edward Adelson
    http://arxiv.org/abs/1910.01287v1

    • [cs.RO]Pose Estimation for Omni-directional Cameras using Sinusoid Fitting
    Haofei Kuang, Qingwen Xu, Xiaoling Long, Sören Schwertfeger
    http://arxiv.org/abs/1910.00882v2

    • [cs.RO]Resilience in multi-robot target tracking through reconfiguration
    Ragesh K. Ramachandran, Nicole Fronda, Gaurav S. Sukhatme
    http://arxiv.org/abs/1910.01300v1

    • [cs.SI]From Senseless Swarms to Smart Mobs: Tuning Networks for Prosocial Behaviour
    Roland Bouffanais, Sun Sun Lim
    http://arxiv.org/abs/1910.01303v1

    • [cs.SI]Introducing multilayer stream graphs and layer centralities
    Pimprenelle Parmentier, Tiphaine Viard, Benjamin Renoust, Jean-François Baffier
    http://arxiv.org/abs/1910.01511v1

    • [eess.AS]From Senones to Chenones: Tied Context-Dependent Graphemes for Hybrid Speech Recognition
    Duc Le, Xiaohui Zhang, Weiyi Zheng, Christian Fügen, Geoffrey Zweig, Michael L. Seltzer
    http://arxiv.org/abs/1910.01493v1

    • [eess.IV]Cardiac Segmentation of LGE MRI with Noisy Labels
    Holger Roth, Wentao Zhu, Dong Yang, Ziyue Xu, Daguang Xu
    http://arxiv.org/abs/1910.01242v1

    • [eess.IV]High-dimensional Dense Residual Convolutional Neural Network for Light Field Reconstruction
    Nan Meng, Hayden K. -H. So, Xing Sun, Edmund Y. Lam
    http://arxiv.org/abs/1910.01426v1

    • [eess.IV]Improving Limited Angle CT Reconstruction with a Robust GAN Prior
    Rushil Anirudh, Hyojin Kim, Jayaraman J. Thiagarajan, Aditya K. Mohan, Kyle M. Champley
    http://arxiv.org/abs/1910.01634v1

    • [eess.IV]Kidney Recognition in CT Using YOLOv3
    Andréanne Lemay
    http://arxiv.org/abs/1910.01268v1

    • [eess.IV]The LoDoPaB-CT Dataset: A Benchmark Dataset for Low-Dose CT Reconstruction Methods
    Johannes Leuschner, Maximilian Schmidt, Daniel Otero Baguer, Peter Maaß
    http://arxiv.org/abs/1910.01113v1

    • [eess.SP]Exploring Positive Noise in Estimation Theory
    Kamiar Radnosrati, Gustaf Hendeby, Fredrik Gustafsson
    http://arxiv.org/abs/1910.01569v1

    • [eess.SP]Fault Detection Using Nonlinear Low-Dimensional Representation of Sensor Data
    Kai Shen, Anya Mcguirk, Yuwei Liao, Arin Chaudhuri, Deovrat Kakde
    http://arxiv.org/abs/1910.01150v1

    • [eess.SY]Verification of Neural Network Behaviour: Formal Guarantees for Power System Applications
    Andreas Venzke, Spyros Chatzivasileiadis
    http://arxiv.org/abs/1910.01624v1

    • [math.NA]A deep surrogate approach to efficient Bayesian inversion in PDE and integral equation models
    Teo Deveney, Eike Mueller, Tony Shardlow
    http://arxiv.org/abs/1910.01547v1

    • [math.OC]A sparse semismooth Newton based augmented Lagrangian method for large-scale support vector machines
    Dunbiao Niu, Chengjing Wang, Peipei Tang, Qingsong Wang, Enbin Song
    http://arxiv.org/abs/1910.01312v1

    • [math.OC]Escaping Saddle Points for Zeroth-order Nonconvex Optimization using Estimated Gradient Descent
    Qinbo Bai, Mridul Agarwal, Vaneet Aggarwal
    http://arxiv.org/abs/1910.01277v1

    • [math.PR]Generalized bounds for active subspaces
    Mario Teixeira Parente, Jonas Wallin, Barbara Wohlmuth
    http://arxiv.org/abs/1910.01399v1

    • [math.ST]Empirical Likelihood Under Mis-specification: Degeneracies and Random Critical Points
    Subhro Ghosh, Sanjay Chaudhuri
    http://arxiv.org/abs/1910.01396v1

    • [math.ST]On some spectral properties of stochastic similarity matrices for data clustering
    Denis Gaidashev, Ralf Pihlström, Martin Ryner
    http://arxiv.org/abs/1910.01392v1

    • [math.ST]Privately detecting changes in unknown distributions
    Rachel Cummings, Sara Krehbiel, Yuliia Lut, Wanrong Zhang
    http://arxiv.org/abs/1910.01327v1

    • [math.ST]Statistical inference of subcritical strongly stationary Galton—Watson processes with regularly varying immigration
    Matyas Barczy, Bojan Basrak, Péter Kevei, Gyula Pap, Hrvoje Planinić
    http://arxiv.org/abs/1910.01420v1

    • [physics.med-ph]Design of an assistive trunk exoskeleton based on multibody dynamic modelling
    Pierre Lifeng Li, Sofiane Achiche, Laurent Blanchet, Samuel Lecours, Maxime Raison
    http://arxiv.org/abs/1910.01184v1

    • [physics.soc-ph]Spatial Strength Centrality and the Effect of Spatial Embeddings on Network Architecture
    Andrew Liu, Mason A. Porter
    http://arxiv.org/abs/1910.01174v1

    • [q-bio.NC]Inference of a mesoscopic population model from population spike trains
    Alexandre René, André Longtin, Jakob H. Macke
    http://arxiv.org/abs/1910.01618v1

    • [q-fin.GN]A tale of two sentiment scales: Disentangling short-run and long-run components in multivariate sentiment dynamics
    Danilo Vassallo, Giacomo Bormetti, Fabrizio Lillo
    http://arxiv.org/abs/1910.01407v1

    • [q-fin.ST]A Robust Transferable Deep Learning Framework for Cross-sectional Investment Strategy
    Kei Nakagawa, Masaya Abe, Junpei Komiyama
    http://arxiv.org/abs/1910.01491v1

    • [quant-ph]Quantum tensor singular value decomposition with applications to recommendation systems
    Xiaoqiang Wang, Lejia Gu, Heung-wing Joseph Lee, Guofeng Zhang
    http://arxiv.org/abs/1910.01262v1

    • [quant-ph]Stochastic gradient descent for hybrid quantum-classical optimization
    Ryan Sweke, Frederik Wilde, Johannes Meyer, Maria Schuld, Paul K. Fährmann, Barthélémy Meynard-Piganeau, Jens Eisert
    http://arxiv.org/abs/1910.01155v1

    • [stat.AP]A Simple Yet Efficient Parametric Method of Local False Discovery Rate Estimation Designed for Genome-Wide Association Data Analysis
    Ali Karimnezhad
    http://arxiv.org/abs/1909.13307v2

    • [stat.AP]Assessing the predictive ability of the UPDRS for falls classification in early stage Parkinson’s disease
    Sarini Abdullah, Nicole White, James McGree, Kerrie Mengersen, Graham Kerr
    http://arxiv.org/abs/1910.01313v1

    • [stat.AP]Indicators of retention in remote digital health studies: A cross-study evaluation of 100,000 participants
    Abhishek Pratap, Elias Chaibub Neto, Phil Snyder, Carl Stepnowsky, Noémie Elhadad, Daniel Grant, Matthew H. Mohebbi, Sean Mooney, Christine Suver, John Wilbanks, Lara Mangravite, Patrick Heagerty, Pat Arean, Larsson Omberg
    http://arxiv.org/abs/1910.01165v1

    • [stat.AP]Modeling of Electrical Resistivity of Soil Based on Geotechnical Properties
    Bandar Alsharari, Andriy Olenko, Hossam Abuel-Naga
    http://arxiv.org/abs/1910.01325v1

    • [stat.AP]Robustly Extracting Medical Knowledge from EHRs: A Case Study of Learning a Health Knowledge Graph
    Irene Y. Chen, Monica Agrawal, Steven Horng, David Sontag
    http://arxiv.org/abs/1910.01116v1

    • [stat.AP]The effects of degrees of freedom estimation in the Asymmetric GARCH model with Student-t Innovations
    T. C. O. Fonseca, V. S. Cerqueira, H. S. Migon, C. A. C. Torres
    http://arxiv.org/abs/1910.01398v1

    • [stat.ME]A Pseudo-Likelihood Approach to Linear Regression with Partially Shuffled Data
    Martin Slawski, Guoqing Diao, Emanuel Ben-David
    http://arxiv.org/abs/1910.01623v1

    • [stat.ME]Combining multiple imputation with raking of weights in the setting of nearly-true models
    Kyunghee Han, Pamela A. Shaw, Thomas Lumley
    http://arxiv.org/abs/1910.01162v1

    • [stat.ML]A note on the consistency of the random forest algorithm
    José A. Ferreira
    http://arxiv.org/abs/1910.00943v2

    • [stat.ML]Causal inference with Bayes rule
    Finnian Lattimore, David Rohde
    http://arxiv.org/abs/1910.01510v1

    • [stat.ML]Distillation $\approx$ Early Stopping? Harvesting Dark Knowledge Utilizing Anisotropic Information Retrieval For Overparameterized Neural Network
    Bin Dong, Jikai Hou, Yiping Lu, Zhihua Zhang
    http://arxiv.org/abs/1910.01255v1

    • [stat.ML]Generalization Bounds for Convolutional Neural Networks
    Shan Lin, Jingwei Zhang
    http://arxiv.org/abs/1910.01487v1

    • [stat.ML]Improving Differentially Private Models with Active Learning
    Zhengli Zhao, Nicolas Papernot, Sameer Singh, Neoklis Polyzotis, Augustus Odena
    http://arxiv.org/abs/1910.01177v1

    • [stat.ML]Reconsidering Analytical Variational Bounds for Output Layers of Deep Networks
    Otmane Sakhi, Stephen Bonner, David Rohde, Flavian Vasile
    http://arxiv.org/abs/1910.00877v2

    • [stat.ML]Robust Risk Minimization for Statistical Learning
    Muhammad Osama, Dave Zachariah, Peter Stoica
    http://arxiv.org/abs/1910.01544v1