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
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• [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