cs.AI - 人工智能
cs.CC - 计算复杂度
cs.CL - 计算与语言
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.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.NE - 神经与进化计算
cs.RO - 机器人学
cs.SE - 软件工程
cs.SI - 社交网络与信息网络
eess.AS - 语音处理
math-ph - 数学物理
math.CO - 组合数学
math.ST - 统计理论
physics.app-ph - 应用物理
physics.med-ph - 医学物理学
q-bio.NC - 神经元与认知
stat.AP - 应用统计
stat.CO - 统计计算
stat.ME - 统计方法论
stat.ML - (统计)机器学习
• [cs.AI]ELF OpenGo: An Analysis and Open Reimplementation of AlphaZero
• [cs.AI]Federated Machine Learning: Concept and Applications
• [cs.AI]Relative rationality: Is machine rationality subjective?
• [cs.CC]CSPs with Global Modular Constraints: Algorithms and Hardness via Polynomial Representations
• [cs.CL]Explainable Text-Driven Neural Network for Stock Prediction
• [cs.CL]Learning to Select Knowledge for Response Generation in Dialog Systems
• [cs.CL]Machine Reading Comprehension for Answer Re-Ranking in Customer Support Chatbots
• [cs.CL]SECTOR: A Neural Model for Coherent Topic Segmentation and Classification
• [cs.CV]3D Face Modeling from Diverse Raw Scan Data
• [cs.CV]3D Robot Pose Estimation from 2D Images
• [cs.CV]Accurate 3D Cell Segmentation using Deep Feature and CRF Refinement
• [cs.CV]Automated Segmentation of the Optic Disk and Cup using Dual-Stage Fully Convolutional Networks
• [cs.CV]Can We Automate Diagrammatic Reasoning?
• [cs.CV]Forensic Similarity for Digital Images
• [cs.CV]Gated2Depth: Real-time Dense Lidar from Gated Images
• [cs.CV]Learning to see across Domains and Modalities
• [cs.CV]Multi-Prototype Networks for Unconstrained Set-based Face Recognition
• [cs.CV]Multi-views Embedding for Cattle Re-identification
• [cs.CV]Person Re-identification in Videos by Analyzing Spatio-Temporal Tubes
• [cs.CV]Progressively Growing Generative Adversarial Networks for High Resolution Semantic Segmentation of Satellite Images
• [cs.CV]Real-time tracker with fast recovery from target loss
• [cs.CV]Self-adaptive Single and Multi-illuminant Estimation Framework based on Deep Learning
• [cs.CV]Super-Resolution of Brain MRI Images using Overcomplete Dictionaries and Nonlocal Similarity
• [cs.CV]Unsupervised 3D End-to-End Medical Image Registration with Volume Tweening Network
• [cs.CV]You Only Look & Listen Once: Towards Fast and Accurate Visual Grounding
• [cs.CY]Mathematical Notions vs. Human Perception of Fairness: A Descriptive Approach to Fairness for Machine Learning
• [cs.DC]An Empirical Study of Blockchain-based Decentralized Applications
• [cs.DC]Arbitrary Pattern Formation by Asynchronous Opaque Robots
• [cs.DC]Concurrent Computing with Shared Replicated Memory
• [cs.DC]Petascale Cloud Supercomputing for Terapixel Visualization of a Digital Twin
• [cs.DC]Salus: Fine-Grained GPU Sharing Primitives for Deep Learning Applications
• [cs.DC]Task-based Augmented Contour Trees with Fibonacci Heaps
• [cs.DC]Two-Dimensional Batch Linear Programming on the GPU
• [cs.DS]Learning Ising Models with Independent Failures
• [cs.GT]Stable-Predictive Optimistic Counterfactual Regret Minimization
• [cs.HC]A Subject-Specific Four-Degree-of-Freedom Foot Interface to Control a Robot Arm
• [cs.IR]A Survey on Session-based Recommender Systems
• [cs.IR]Delog: A Privacy Preserving Log Filtering Framework for Online Compute Platforms
• [cs.IR]Session-based Sequential Skip Prediction via Recurrent Neural Networks
• [cs.IT]Channel-Statistics-Based Hybrid Precoding for Millimeter-Wave MIMO Systems With Dynamic Subarrays
• [cs.IT]HetNets Coverage Modeling and Analysis Over Fox's -Fading Channels
• [cs.IT]Inequalities and Approximations for Fisher Information in the Presence of Nuisance Parameters
• [cs.IT]Quantifying the Loss of Information from Binning List-Mode Data
• [cs.IT]Seamless Rate Adaptation at Receiver Side for Indoor Visible Light Communications by Using Raptor Codes
• [cs.IT]Simultaneous Sparse Recovery and Blind Demodulation
• [cs.IT]Throughput-Outage Analysis and Evaluation of Cache-Aided D2D Networks with Measured Popularity Distributions
• [cs.IT]Towards Jointly Optimal Placement and Delivery: To Code or Not to Code in Wireless Caching Networks
• [cs.IT]User-Antenna Selection for Physical-Layer Network Coding based on Euclidean Distance
• [cs.LG]A Tunable Loss Function for Binary Classification
• [cs.LG]ATMSeer: Increasing Transparency and Controllability in Automated Machine Learning
• [cs.LG]An Optimized Recurrent Unit for Ultra-Low-Power Keyword Spotting
• [cs.LG]Classifying Signals on Irregular Domains via Convolutional Cluster Pooling
• [cs.LG]Contrastive Variational Autoencoder Enhances Salient Features
• [cs.LG]Crowdsourced PAC Learning under Classification Noise
• [cs.LG]Differential Description Length for Hyperparameter Selection in Machine Learning
• [cs.LG]Differentially Private Learning of Geometric Concepts
• [cs.LG]Distributed Online Linear Regression
• [cs.LG]Efficient Cross-Validation for Semi-Supervised Learning
• [cs.LG]Extreme Tensoring for Low-Memory Preconditioning
• [cs.LG]Gauge Equivariant Convolutional Networks and the Icosahedral CNN
• [cs.LG]Harnessing Low-Fidelity Data to Accelerate Bayesian Optimization via Posterior Regularization
• [cs.LG]How do infinite width bounded norm networks look in function space?
• [cs.LG]Hyperbolic Disk Embeddings for Directed Acyclic Graphs
• [cs.LG]Learning Theory and Support Vector Machines - a primer
• [cs.LG]Learning and Generalization for Matching Problems
• [cs.LG]Modeling default rate in P2P lending via LSTM
• [cs.LG]On the Expressive Power of Kernel Methods and the Efficiency of Kernel Learning by Association Schemes
• [cs.LG]PAC-Bayes Analysis of Sentence Representation
• [cs.LG]Phaseless Low Rank Matrix Recovery and Subspace Tracking
• [cs.LG]Privacy-Utility Trade-off of Linear Regression under Random Projections and Additive Noise
• [cs.LG]Sample-Optimal Parametric Q-Learning with Linear Transition Models
• [cs.LG]Simultaneously Learning Vision and Feature-based Control Policies for Real-world Ball-in-a-Cup
• [cs.LG]Stochastic Gradient Descent Escapes Saddle Points Efficiently
• [cs.LG]The Complexity of Making the Gradient Small in Stochastic Convex Optimization
• [cs.LG]The Odds are Odd: A Statistical Test for Detecting Adversarial Examples
• [cs.LG]Towards moderate overparameterization: global convergence guarantees for training shallow neural networks
• [cs.LG]Uniform convergence may be unable to explain generalization in deep learning
• [cs.LG]Variance-Preserving Initialization Schemes Improve Deep Network Training: But Which Variance is Preserved?
• [cs.LG]Wasserstein Barycenter Model Ensembling
• [cs.LG]Weighted Tensor Completion for Time-Series Causal Information
• [cs.LG]Why are Saliency Maps Noisy? Cause of and Solution to Noisy Saliency Maps
• [cs.LG]Wireless Traffic Prediction with Scalable Gaussian Process: Framework, Algorithms, and Verification
• [cs.NE]A characterisation of S-box fitness landscapes in cryptography
• [cs.NE]Analysis of Baseline Evolutionary Algorithms for the Packing While Travelling Problem
• [cs.NE]Evolutionary Algorithms for the Chance-Constrained Knapsack Problem
• [cs.NE]Guiding Neuroevolution with Structural Objectives
• [cs.NE]Scaling Limits of Wide Neural Networks with Weight Sharing: Gaussian Process Behavior, Gradient Independence, and Neural Tangent Kernel Derivation
• [cs.RO]A Differentiable Augmented Lagrangian Method for Bilevel Nonlinear Optimization
• [cs.RO]A Scalable FPGA-based Architecture for Depth Estimation in SLAM
• [cs.RO]A bistable soft gripper with mechanically embedded sensing and actuation for fast closed-loop grasping
• [cs.RO]Motion Scaling Solutions for Improved Performance in High Delay Surgical Teleoperation
• [cs.RO]Proximity Queries for Absolutely Continuous Parametric Curves
• [cs.RO]Self-Supervised Surgical Tool Segmentation using Kinematic Information
• [cs.RO]Using Approximate Models in Robot Learning
• [cs.RO]Value constrained model-free continuous control
• [cs.SE]Time-aware Test Case Execution Scheduling for Cyber-Physical Systems
• [cs.SI]Global labor flow network reveals the hierarchical organization and dynamics of geo-industrial clusters in the world economy
• [cs.SI]Spectra of networks containing short loops
• [eess.AS]Recurrent Neural Networks with Stochastic Layers for Acoustic Novelty Detection
• [math-ph]On the information content of the difference from hamiltonian evolution
• [math.CO]Local approximation of the Maximum Cut in regular graphs
• [math.ST]Dependence Properties of B-Spline Copulas
• [math.ST]Estimation of causal CARMA random fields
• [math.ST]Minimax rates in outlier-robust estimation of discrete models
• [math.ST]Quickest Change Detection in the Presence of a Nuisance Change
• [physics.app-ph]Analytical Modeling for Rapid Design of Bistable Buckled Beams
• [physics.med-ph]Bone Texture Analysis for Prediction of Incident Radio-graphic Hip Osteoarthritis Using Machine Learning: Data from the Cohort Hip and Cohort Knee (CHECK) study
• [q-bio.NC]Neural network models and deep learning - a primer for biologists
• [stat.AP]A Data-Driven Approach for Assessing Biking Safety in Cities
• [stat.AP]A Novel Maneuvering Target Tracking Approach by Stochastic Volatility GARCH Model
• [stat.AP]A simple statistical approach to prediction in open high dimensional chaotic systems
• [stat.AP]Impact of Inter-Country Distances on International Tourism
• [stat.AP]Selective Inference for Testing Trees and Edges in Phylogenetics
• [stat.AP]Statistical Failure Mechanism Analysis of Earthquakes Revealing Time Relationships
• [stat.CO]Bayesian inference and non-linear extensions of the CIRCE method for quantifying the uncertainty of closure relationships integrated into thermal-hydraulic system codes
• [stat.ME]A nonparametric graphical tests of significance in functional GLM
• [stat.ME]An efficient methodology to estimate the parameters of a two-dimensional chirp signal model
• [stat.ME]Efficient Bayesian shape-restricted function estimation with constrained Gaussian process priors
• [stat.ME]Enhanced Balancing of Bias-Variance Tradeoff in Stochastic Estimation: A Minimax Perspective
• [stat.ME]High dimensionality: The latest challenge to data analysis
• [stat.ME]Incorporating correlations between drugs and heterogeneity of multi-omics data in structured penalized regression for drug sensitivity prediction
• [stat.ML]Beyond the Chinese Restaurant and Pitman-Yor processes: Statistical Models with Double Power-law Behavior
• [stat.ML]Deep Divergence-Based Approach to Clustering
• [stat.ML]Do Subsampled Newton Methods Work for High-Dimensional Data?
• [stat.ML]Group level MEG/EEG source imaging via optimal transport: minimum Wasserstein estimates
• [stat.ML]Identity Crisis: Memorization and Generalization under Extreme Overparameterization
• [stat.ML]Learning Generative Models of Structured Signals from Their Superposition Using GANs with Application to Denoising and Demixing
• [stat.ML]Rethinking Generative Coverage: A Pointwise Guaranteed Approac
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• [cs.AI]ELF OpenGo: An Analysis and Open Reimplementation of AlphaZero
Yuandong Tian, Jerry Ma, Qucheng Gong, Shubho Sengupta, Zhuoyuan Chen, James Pinkerton, C. Lawrence Zitnick
http://arxiv.org/abs/1902.04522v2
• [cs.AI]Federated Machine Learning: Concept and Applications
Qiang Yang, Yang Liu, Tianjian Chen, Yongxin Tong
http://arxiv.org/abs/1902.04885v1
• [cs.AI]Relative rationality: Is machine rationality subjective?
Tshilidzi Marwala
http://arxiv.org/abs/1902.04832v1
• [cs.CC]CSPs with Global Modular Constraints: Algorithms and Hardness via Polynomial Representations
Joshua Brakensiek, Sivakanth Gopi, Venkatesan Guruswami
http://arxiv.org/abs/1902.04740v1
• [cs.CL]Explainable Text-Driven Neural Network for Stock Prediction
Linyi Yang, Zheng Zhang, Su Xiong, Lirui Wei, James Ng, Lina Xu, Ruihai Dong
http://arxiv.org/abs/1902.04994v1
• [cs.CL]Learning to Select Knowledge for Response Generation in Dialog Systems
Rongzhong Lian, Min Xie, Fan Wang, Jinhua Peng, Hua Wu
http://arxiv.org/abs/1902.04911v1
• [cs.CL]Machine Reading Comprehension for Answer Re-Ranking in Customer Support Chatbots
Momchil Hardalov, Ivan Koychev, Preslav Nakov
http://arxiv.org/abs/1902.04574v1
• [cs.CL]SECTOR: A Neural Model for Coherent Topic Segmentation and Classification
Sebastian Arnold, Rudolf Schneider, Philippe Cudré-Mauroux, Felix A. Gers, Alexander Löser
http://arxiv.org/abs/1902.04793v1
• [cs.CV]3D Face Modeling from Diverse Raw Scan Data
Feng Liu, Tran Luan, Xiaoming Liu
http://arxiv.org/abs/1902.04943v1
• [cs.CV]3D Robot Pose Estimation from 2D Images
Christoph Heindl, Sebastian Zambal, Thomas Ponitz, Andreas Pichler, Josef Scharinger
http://arxiv.org/abs/1902.04987v1
• [cs.CV]Accurate 3D Cell Segmentation using Deep Feature and CRF Refinement
Jiaxiang Jiang, Po-Yu Kao, Samuel A. Belteton, Daniel B. Szymanski, B. S. Manjunath
http://arxiv.org/abs/1902.04729v1
• [cs.CV]Automated Segmentation of the Optic Disk and Cup using Dual-Stage Fully Convolutional Networks
Lei Bi, Yuyu Guo, Qian Wang, Dagan Feng, Michael Fulham, Jinman Kim
http://arxiv.org/abs/1902.04713v1
• [cs.CV]Can We Automate Diagrammatic Reasoning?
Sk. Arif Ahmed, Debi Prosad Dogra, Samarjit Kar, Partha Pratim Roy, Dilip K. Prasad
http://arxiv.org/abs/1902.04955v1
• [cs.CV]Forensic Similarity for Digital Images
Owen Mayer, Matthew C. Stamm
http://arxiv.org/abs/1902.04684v1
• [cs.CV]Gated2Depth: Real-time Dense Lidar from Gated Images
Tobias Gruber, Frank Julca-Aguilar, Mario Bijelic, Werner Ritter, Klaus Dietmayer, Felix Heide
http://arxiv.org/abs/1902.04997v1
• [cs.CV]Learning to see across Domains and Modalities
Fabio Maria Carlucci
http://arxiv.org/abs/1902.04992v1
• [cs.CV]Multi-Prototype Networks for Unconstrained Set-based Face Recognition
Jian Zhao, Jianshu Li, Xiaoguang Tu, Fang Zhao, Yuan Xin, Junliang Xing, Hengzhu Liu, Shuicheng Yan, Jiashi Feng
http://arxiv.org/abs/1902.04755v1
• [cs.CV]Multi-views Embedding for Cattle Re-identification
Luca Bergamini, Angelo Porrello, Andrea Capobianco Dondona, Ercole Del Negro, Mauro Mattioli, Nicola D'Alterio, Simone Calderara
http://arxiv.org/abs/1902.04886v1
• [cs.CV]Person Re-identification in Videos by Analyzing Spatio-Temporal Tubes
Sk. Arif Ahmed, Debi Prosad Dogra, Heeseung Choi, Seungho Chae, Ig-Jae Kim
http://arxiv.org/abs/1902.04856v1
• [cs.CV]Progressively Growing Generative Adversarial Networks for High Resolution Semantic Segmentation of Satellite Images
Edward Collier, Kate Duffy, Sangram Ganguly, Geri Madanguit, Subodh Kalia, Gayaka Shreekant, Ramakrishna Nemani, Andrew Michaelis, Shuang Li, Auroop Ganguly, Supratik Mukhopadhyay
http://arxiv.org/abs/1902.04604v1
• [cs.CV]Real-time tracker with fast recovery from target loss
Alessandro Bay, Panagiotis Sidiropoulos, Eduard Vazquez, Michele Sasdelli
http://arxiv.org/abs/1902.04570v1
• [cs.CV]Self-adaptive Single and Multi-illuminant Estimation Framework based on Deep Learning
Yongjie Liu, Sijie Shen
http://arxiv.org/abs/1902.04705v1
• [cs.CV]Super-Resolution of Brain MRI Images using Overcomplete Dictionaries and Nonlocal Similarity
Yinghua Li, Bin Song, Jie Guo, Xiaojiang Du, Mohsen Guizani
http://arxiv.org/abs/1902.04902v1
• [cs.CV]Unsupervised 3D End-to-End Medical Image Registration with Volume Tweening Network
Tingfung Lau, Ji Luo, Shengyu Zhao, Eric I-Chao Chang, Yan Xu
http://arxiv.org/abs/1902.05020v1
• [cs.CV]You Only Look & Listen Once: Towards Fast and Accurate Visual Grounding
Chaorui Deng, Qi Wu, Guanghui Xu, Zhuliang Yu, Yanwu Xu, Kui Jia, Mingkui Tan
http://arxiv.org/abs/1902.04213v2
• [cs.CY]Mathematical Notions vs. Human Perception of Fairness: A Descriptive Approach to Fairness for Machine Learning
Megha Srivastava, Hoda Heidari, Andreas Krause
http://arxiv.org/abs/1902.04783v1
• [cs.DC]An Empirical Study of Blockchain-based Decentralized Applications
Kaidong Wu
http://arxiv.org/abs/1902.04969v1
• [cs.DC]Arbitrary Pattern Formation by Asynchronous Opaque Robots
Kaustav Bose, Manash Kumar Kundu, Ranendu Adhikary, Buddhadeb Sau
http://arxiv.org/abs/1902.04950v1
• [cs.DC]Concurrent Computing with Shared Replicated Memory
Klaus-Dieter Schewe, Andreas Prinz, Egon Börger
http://arxiv.org/abs/1902.04789v1
• [cs.DC]Petascale Cloud Supercomputing for Terapixel Visualization of a Digital Twin
Nicolas S. Holliman, Manu Antony, James Charlton, Stephen Dowsland, Philip James, Mark Turner
http://arxiv.org/abs/1902.04820v1
• [cs.DC]Salus: Fine-Grained GPU Sharing Primitives for Deep Learning Applications
Peifeng Yu, Mosharaf Chowdhury
http://arxiv.org/abs/1902.04610v1
• [cs.DC]Task-based Augmented Contour Trees with Fibonacci Heaps
Charles Gueunet, P. Fortin, J Jomier, J Tierny
http://arxiv.org/abs/1902.04805v1
• [cs.DC]Two-Dimensional Batch Linear Programming on the GPU
John Charlton, Steve Maddock, Paul Richmond
http://arxiv.org/abs/1902.04995v1
• [cs.DS]Learning Ising Models with Independent Failures
Surbhi Goel, Daniel M. Kane, Adam R. Klivans
http://arxiv.org/abs/1902.04728v1
• [cs.GT]Stable-Predictive Optimistic Counterfactual Regret Minimization
Gabriele Farina, Christian Kroer, Noam Brown, Tuomas Sandholm
http://arxiv.org/abs/1902.04982v1
• [cs.HC]A Subject-Specific Four-Degree-of-Freedom Foot Interface to Control a Robot Arm
Yanpei Huang, Etienne Burdet, Lin Cao, Phuoc Thien Phan, Anthony Meng Huat Tiong, Soo Jay Phee
http://arxiv.org/abs/1902.04752v1
• [cs.IR]A Survey on Session-based Recommender Systems
Shoujin Wang, Longbing Cao, Yan Wang
http://arxiv.org/abs/1902.04864v1
• [cs.IR]Delog: A Privacy Preserving Log Filtering Framework for Online Compute Platforms
Amey Agrawal, Abhishek Dixit, Darshil Kapadia, Rohit Karlupia, Vikram Agrawal, Rajat Gupta
http://arxiv.org/abs/1902.04843v1
• [cs.IR]Session-based Sequential Skip Prediction via Recurrent Neural Networks
Lin Zhu, Yihong Chen
http://arxiv.org/abs/1902.04743v1
• [cs.IT]Channel-Statistics-Based Hybrid Precoding for Millimeter-Wave MIMO Systems With Dynamic Subarrays
Juening Jin, Chengshan Xiao, Wen Chen, Yongpeng Wu
http://arxiv.org/abs/1902.04677v1
• [cs.IT]HetNets Coverage Modeling and Analysis Over Fox's -Fading Channels
Imène Trigui, Sofiène Affes
http://arxiv.org/abs/1902.05053v1
• [cs.IT]Inequalities and Approximations for Fisher Information in the Presence of Nuisance Parameters
Eric Clarkson
http://arxiv.org/abs/1902.04607v1
• [cs.IT]Quantifying the Loss of Information from Binning List-Mode Data
Eric Clarkson
http://arxiv.org/abs/1902.04606v1
• [cs.IT]Seamless Rate Adaptation at Receiver Side for Indoor Visible Light Communications by Using Raptor Codes
Cenk Albayrak, Kadir Turk, Emin Tugcu, Cemaleddin Simsek, Ayhan Yazgan
http://arxiv.org/abs/1902.04649v1
• [cs.IT]Simultaneous Sparse Recovery and Blind Demodulation
Youye Xie, Michael B. Wakin, Gongguo Tang
http://arxiv.org/abs/1902.05023v1
• [cs.IT]Throughput-Outage Analysis and Evaluation of Cache-Aided D2D Networks with Measured Popularity Distributions
Ming-Chun Lee, Mingyue Ji, Andreas F. Molisch, Nishanth Sastry
http://arxiv.org/abs/1902.04563v1
• [cs.IT]Towards Jointly Optimal Placement and Delivery: To Code or Not to Code in Wireless Caching Networks
Yousef AlHassoun, Faisal Alotaibi, Aly El Gamal, Hesham El Gamal
http://arxiv.org/abs/1902.04600v1
• [cs.IT]User-Antenna Selection for Physical-Layer Network Coding based on Euclidean Distance
Vaibhav Kumar, Barry Cardiff, Mark F. Flanagan
http://arxiv.org/abs/1902.04571v1
• [cs.LG]A Tunable Loss Function for Binary Classification
Tyler Sypherd, Mario Diaz, Lalitha Sankar, Peter Kairouz
http://arxiv.org/abs/1902.04639v1
• [cs.LG]ATMSeer: Increasing Transparency and Controllability in Automated Machine Learning
Qianwen Wang, Yao Ming, Zhihua Jin, Qiaomu Shen, Dongyu Liu, Micah J. Smith, Kalyan Veeramachaneni, Huamin Qu
http://arxiv.org/abs/1902.05009v1
• [cs.LG]An Optimized Recurrent Unit for Ultra-Low-Power Keyword Spotting
Justice Amoh, Kofi Odame
http://arxiv.org/abs/1902.05026v1
• [cs.LG]Classifying Signals on Irregular Domains via Convolutional Cluster Pooling
Angelo Porrello, Davide Abati, Simone Calderara, Rita Cucchiara
http://arxiv.org/abs/1902.04850v1
• [cs.LG]Contrastive Variational Autoencoder Enhances Salient Features
Abubakar Abid, James Zou
http://arxiv.org/abs/1902.04601v1
• [cs.LG]Crowdsourced PAC Learning under Classification Noise
Shelby Heinecke, Lev Reyzin
http://arxiv.org/abs/1902.04629v1
• [cs.LG]Differential Description Length for Hyperparameter Selection in Machine Learning
Anders Host-Madsen, Mojtaba Abolfazli, June Zhang
http://arxiv.org/abs/1902.04699v1
• [cs.LG]Differentially Private Learning of Geometric Concepts
Haim Kaplan, Yishay Mansour, Yossi Matias, Uri Stemmer
http://arxiv.org/abs/1902.05017v1
• [cs.LG]Distributed Online Linear Regression
Deming Yuan, Alexandre Proutiere, Guodong Shi
http://arxiv.org/abs/1902.04774v1
• [cs.LG]Efficient Cross-Validation for Semi-Supervised Learning
Yong Liu, Jian Li, Guangjun Wu, Lizhong Ding, Weiping Wang
http://arxiv.org/abs/1902.04768v1
• [cs.LG]Extreme Tensoring for Low-Memory Preconditioning
Xinyi Chen, Naman Agarwal, Elad Hazan, Cyril Zhang, Yi Zhang
http://arxiv.org/abs/1902.04620v1
• [cs.LG]Gauge Equivariant Convolutional Networks and the Icosahedral CNN
Taco S. Cohen, Maurice Weiler, Berkay Kicanaoglu, Max Welling
http://arxiv.org/abs/1902.04615v1
• [cs.LG]Harnessing Low-Fidelity Data to Accelerate Bayesian Optimization via Posterior Regularization
Bin Liu
http://arxiv.org/abs/1902.03740v2
• [cs.LG]How do infinite width bounded norm networks look in function space?
Pedro Savarese, Itay Evron, Daniel Soudry, Nathan Srebro
http://arxiv.org/abs/1902.05040v1
• [cs.LG]Hyperbolic Disk Embeddings for Directed Acyclic Graphs
Ryota Suzuki, Ryusuke Takahama, Shun Onoda
http://arxiv.org/abs/1902.04335v2
• [cs.LG]Learning Theory and Support Vector Machines - a primer
Michael Banf
http://arxiv.org/abs/1902.04622v1
• [cs.LG]Learning and Generalization for Matching Problems
Alon Cohen, Avinatan Hassidim, Haim Kaplan, Yishay Mansour, Shay Moran
http://arxiv.org/abs/1902.04741v1
• [cs.LG]Modeling default rate in P2P lending via LSTM
Yan Wang, Xuelei Sherry Ni
http://arxiv.org/abs/1902.04954v1
• [cs.LG]On the Expressive Power of Kernel Methods and the Efficiency of Kernel Learning by Association Schemes
Pravesh K. Kothari, Roi Livni
http://arxiv.org/abs/1902.04782v1
• [cs.LG]PAC-Bayes Analysis of Sentence Representation
Kento Nozawa, Issei Sato
http://arxiv.org/abs/1902.04247v2
• [cs.LG]Phaseless Low Rank Matrix Recovery and Subspace Tracking
Seyedehsara Nayer, Praneeth Narayanamurthy, Namrata Vaswani
http://arxiv.org/abs/1902.04972v1
• [cs.LG]Privacy-Utility Trade-off of Linear Regression under Random Projections and Additive Noise
Mehrdad Showkatbakhsh, Can Karakus, Suhas Diggavi
http://arxiv.org/abs/1902.04688v1
• [cs.LG]Sample-Optimal Parametric Q-Learning with Linear Transition Models
Lin F. Yang, Mengdi Wang
http://arxiv.org/abs/1902.04779v1
• [cs.LG]Simultaneously Learning Vision and Feature-based Control Policies for Real-world Ball-in-a-Cup
Devin Schwab, Tobias Springenberg, Murilo F. Martins, Thomas Lampe, Michael Neunert, Abbas Abdolmaleki, Tim Herkweck, Roland Hafner, Francesco Nori, Martin Riedmiller
http://arxiv.org/abs/1902.04706v1
• [cs.LG]Stochastic Gradient Descent Escapes Saddle Points Efficiently
Chi Jin, Praneeth Netrapalli, Rong Ge, Sham M. Kakade, Michael I. Jordan
http://arxiv.org/abs/1902.04811v1
• [cs.LG]The Complexity of Making the Gradient Small in Stochastic Convex Optimization
Dylan Foster, Ayush Sekhari, Ohad Shamir, Nathan Srebro, Karthik Sridharan, Blake Woodworth
http://arxiv.org/abs/1902.04686v1
• [cs.LG]The Odds are Odd: A Statistical Test for Detecting Adversarial Examples
Kevin Roth, Yannic Kilcher, Thomas Hofmann
http://arxiv.org/abs/1902.04818v1
• [cs.LG]Towards moderate overparameterization: global convergence guarantees for training shallow neural networks
Samet Oymak, Mahdi Soltanolkotabi
http://arxiv.org/abs/1902.04674v1
• [cs.LG]Uniform convergence may be unable to explain generalization in deep learning
Vaishnavh Nagarajan, J. Zico Kolter
http://arxiv.org/abs/1902.04742v1
• [cs.LG]Variance-Preserving Initialization Schemes Improve Deep Network Training: But Which Variance is Preserved?
Kyle Luther, H. Sebastian Seung
http://arxiv.org/abs/1902.04942v1
• [cs.LG]Wasserstein Barycenter Model Ensembling
Pierre Dognin, Igor Melnyk, Youssef Mroueh, Jerret Ross, Cicero Dos Santos, Tom Sercu
http://arxiv.org/abs/1902.04999v1
• [cs.LG]Weighted Tensor Completion for Time-Series Causal Information
Debmalya Mandal, David Parkes
http://arxiv.org/abs/1902.04646v1
• [cs.LG]Why are Saliency Maps Noisy? Cause of and Solution to Noisy Saliency Maps
Beomsu Kim, Junghoon Seo, SeungHyun Jeon, Jamyoung Koo, Jeongyeol Choe, Taegyun Jeon
http://arxiv.org/abs/1902.04893v1
• [cs.LG]Wireless Traffic Prediction with Scalable Gaussian Process: Framework, Algorithms, and Verification
Yue Xu, Feng Yin, Wenjun Xu, Jiaru Lin, Shuguang Cui
http://arxiv.org/abs/1902.04763v1
• [cs.NE]A characterisation of S-box fitness landscapes in cryptography
Domagoj Jakobovic, Stjepan Picek, Marcella S. R. Martins, Markus Wagner
http://arxiv.org/abs/1902.04724v1
• [cs.NE]Analysis of Baseline Evolutionary Algorithms for the Packing While Travelling Problem
Vahid Roostapour, Mojgan Pourhassan, Frank Neumann
http://arxiv.org/abs/1902.04692v1
• [cs.NE]Evolutionary Algorithms for the Chance-Constrained Knapsack Problem
Yue Xie, Oscar Harper, Hirad Assimi, Aneta Neumann, Frank Neumann
http://arxiv.org/abs/1902.04767v1
• [cs.NE]Guiding Neuroevolution with Structural Objectives
Kai Olav Ellefsen, Joost Huizinga, Jim Torresen
http://arxiv.org/abs/1902.04346v2
• [cs.NE]Scaling Limits of Wide Neural Networks with Weight Sharing: Gaussian Process Behavior, Gradient Independence, and Neural Tangent Kernel Derivation
Greg Yang
http://arxiv.org/abs/1902.04760v1
• [cs.RO]A Differentiable Augmented Lagrangian Method for Bilevel Nonlinear Optimization
Benoit Landry, Zachary Manchester, Marco Pavone
http://arxiv.org/abs/1902.03319v1
• [cs.RO]A Scalable FPGA-based Architecture for Depth Estimation in SLAM
Konstantinos Boikos, Christos-Savvas Bouganis
http://arxiv.org/abs/1902.04907v1
• [cs.RO]A bistable soft gripper with mechanically embedded sensing and actuation for fast closed-loop grasping
Thomas George Thuruthel, Syed Haider Abidi, Matteo Cianchetti, Cecilia Laschi, Egidio Falotico
http://arxiv.org/abs/1902.04896v1
• [cs.RO]Motion Scaling Solutions for Improved Performance in High Delay Surgical Teleoperation
Florian Richter, Ryan K. Orosco, Michael C. Yip
http://arxiv.org/abs/1902.03290v1
• [cs.RO]Proximity Queries for Absolutely Continuous Parametric Curves
Arun Lakshmanan, Andrew Patterson, Venanzio Cichella, Naira Hovakimyan
http://arxiv.org/abs/1902.05027v1
• [cs.RO]Self-Supervised Surgical Tool Segmentation using Kinematic Information
Cristian da Costa Rocha, Nicolas Padoy, Benoit Rosa
http://arxiv.org/abs/1902.04810v1
• [cs.RO]Using Approximate Models in Robot Learning
Ali Lenjani
http://arxiv.org/abs/1902.04696v1
• [cs.RO]Value constrained model-free continuous control
Steven Bohez, Abbas Abdolmaleki, Michael Neunert, Jonas Buchli, Nicolas Heess, Raia Hadsell
http://arxiv.org/abs/1902.04623v1
• [cs.SE]Time-aware Test Case Execution Scheduling for Cyber-Physical Systems
Morten Mossige, Arnaud Gotlieb, Helge Spieker, Hein Meling, Mats Carlsson
http://arxiv.org/abs/1902.04627v1
• [cs.SI]Global labor flow network reveals the hierarchical organization and dynamics of geo-industrial clusters in the world economy
Jaehyuk Park, Ian Wood, Elise Jing, Azadeh Nematzadeh, Souvik Ghosh, Michael Conover, Yong-Yeol Ahn
http://arxiv.org/abs/1902.04613v1
• [cs.SI]Spectra of networks containing short loops
M. E. J. Newman
http://arxiv.org/abs/1902.04595v1
• [eess.AS]Recurrent Neural Networks with Stochastic Layers for Acoustic Novelty Detection
Duong Nguyen, Oliver S. Kirsebom, Fábio Frazão, Ronan Fablet, Stan Matwin
http://arxiv.org/abs/1902.04980v1
• [math-ph]On the information content of the difference from hamiltonian evolution
Marius Buliga
http://arxiv.org/abs/1902.04598v1
• [math.CO]Local approximation of the Maximum Cut in regular graphs
Étienne Bamas, Louis Esperet
http://arxiv.org/abs/1902.04899v1
• [math.ST]Dependence Properties of B-Spline Copulas
Xiaoling Dou, Satoshi Kuriki, Gwo Dong Lin, Donald Richards
http://arxiv.org/abs/1902.04749v1
• [math.ST]Estimation of causal CARMA random fields
Claudia Klüppelberg, Viet Son Pham
http://arxiv.org/abs/1902.04962v1
• [math.ST]Minimax rates in outlier-robust estimation of discrete models
Amir-Hossein Bateni, Arnak S. Dalalyan
http://arxiv.org/abs/1902.04650v1
• [math.ST]Quickest Change Detection in the Presence of a Nuisance Change
Tze Siong Lau, Wee Peng Tay
http://arxiv.org/abs/1902.03460v3
• [physics.app-ph]Analytical Modeling for Rapid Design of Bistable Buckled Beams
Wenzhong Yan, Yunchen Yu, Ankur Mehta
http://arxiv.org/abs/1902.05038v1
• [physics.med-ph]Bone Texture Analysis for Prediction of Incident Radio-graphic Hip Osteoarthritis Using Machine Learning: Data from the Cohort Hip and Cohort Knee (CHECK) study
Jukka Hirvasniemi, Willem Paul Gielis, Saeed Arbabi, Rintje Agricola, Willem Evert van Spil, Vahid Arbabi, Harrie Weinans
http://arxiv.org/abs/1902.04880v1
• [q-bio.NC]Neural network models and deep learning - a primer for biologists
Nikolaus Kriegeskorte, Tal Golan
http://arxiv.org/abs/1902.04704v1
• [stat.AP]A Data-Driven Approach for Assessing Biking Safety in Cities
Sara Daraei, Konstantinos Pelechrinis, Daniele Quercia
http://arxiv.org/abs/1902.05015v1
• [stat.AP]A Novel Maneuvering Target Tracking Approach by Stochastic Volatility GARCH Model
Ehsan Hajiramezanali, Seyyed Hamed Fouladi, Hamidreza Amindavar
http://arxiv.org/abs/1902.04671v1
• [stat.AP]A simple statistical approach to prediction in open high dimensional chaotic systems
M. LuValle
http://arxiv.org/abs/1902.04727v1
• [stat.AP]Impact of Inter-Country Distances on International Tourism
T. Verma, L. Rebelo, N. A. M. Araújo
http://arxiv.org/abs/1902.04944v1
• [stat.AP]Selective Inference for Testing Trees and Edges in Phylogenetics
Hidetoshi Shimodaira, Yoshikazu Terada
http://arxiv.org/abs/1902.04964v1
• [stat.AP]Statistical Failure Mechanism Analysis of Earthquakes Revealing Time Relationships
Parsa Rastin, Michael LuValle
http://arxiv.org/abs/1902.04732v1
• [stat.CO]Bayesian inference and non-linear extensions of the CIRCE method for quantifying the uncertainty of closure relationships integrated into thermal-hydraulic system codes
Guillaume Damblin, Pierre Gaillard
http://arxiv.org/abs/1902.04931v1
• [stat.ME]A nonparametric graphical tests of significance in functional GLM
Tomas Mrkvicka, Tomas Roskovec, Michael Rost
http://arxiv.org/abs/1902.04926v1
• [stat.ME]An efficient methodology to estimate the parameters of a two-dimensional chirp signal model
Rhythm Grover, Debasis Kundu, Amit Mitra
http://arxiv.org/abs/1902.04765v1
• [stat.ME]Efficient Bayesian shape-restricted function estimation with constrained Gaussian process priors
Pallavi Ray, Debdeep Pati, Anirban Bhattacharya
http://arxiv.org/abs/1902.04701v1
• [stat.ME]Enhanced Balancing of Bias-Variance Tradeoff in Stochastic Estimation: A Minimax Perspective
Henry Lam, Xinyu Zhang, Xuhui Zhang
http://arxiv.org/abs/1902.04673v1
• [stat.ME]High dimensionality: The latest challenge to data analysis
A. M. Pires, J. A. Branco
http://arxiv.org/abs/1902.04679v1
• [stat.ME]Incorporating correlations between drugs and heterogeneity of multi-omics data in structured penalized regression for drug sensitivity prediction
Zhi Zhao, Manuela Zucknick
http://arxiv.org/abs/1902.04996v1
• [stat.ML]Beyond the Chinese Restaurant and Pitman-Yor processes: Statistical Models with Double Power-law Behavior
Fadhel Ayed, Juho Lee, François Caron
http://arxiv.org/abs/1902.04714v1
• [stat.ML]Deep Divergence-Based Approach to Clustering
Michael Kampffmeyer, Sigurd Løkse, Filippo M. Bianchi, Lorenzo Livi, Arnt-Børre Salberg, Robert Jenssen
http://arxiv.org/abs/1902.04981v1
• [stat.ML]Do Subsampled Newton Methods Work for High-Dimensional Data?
Xiang Li, Shusen Wang, Zhihua Zhang
http://arxiv.org/abs/1902.04952v1
• [stat.ML]Group level MEG/EEG source imaging via optimal transport: minimum Wasserstein estimates
Hicham Janati, Thomas Bazeille, Bertrand Thirion, Marco Cuturi, Alexandre Gramfort
http://arxiv.org/abs/1902.04812v1
• [stat.ML]Identity Crisis: Memorization and Generalization under Extreme Overparameterization
Chiyuan Zhang, Samy Bengio, Moritz Hardt, Yoram Singer
http://arxiv.org/abs/1902.04698v1
• [stat.ML]Learning Generative Models of Structured Signals from Their Superposition Using GANs with Application to Denoising and Demixing
Mohammadreza Soltani, Swayambhoo Jain, Abhinav Sambasivan
http://arxiv.org/abs/1902.04664v1
• [stat.ML]Rethinking Generative Coverage: A Pointwise Guaranteed Approac
Peilin Zhong, Yuchen Mo, Chang Xiao, Pengyu Chen, Changxi Zheng
http://arxiv.org/abs/1902.04697v1
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