cs.AI - 人工智能
cs.CL - 计算与语言
cs.CR - 加密与安全
cs.CV - 机器视觉与模式识别
cs.CY - 计算与社会
cs.DC - 分布式、并行与集群计算
cs.FL - 形式语言与自动机理论
cs.HC - 人机接口
cs.IR - 信息检索
cs.IT - 信息论
cs.LG - 自动学习
cs.MA - 多代理系统
cs.NE - 神经与进化计算
cs.NI - 网络和互联网体系结构
cs.RO - 机器人学
cs.SE - 软件工程
cs.SI - 社交网络与信息网络
cs.SY - 系统与控制
eess.AS - 语音处理
eess.IV - 图像与视频处理
eess.SP - 信号处理
math.OC - 优化与控制
math.ST - 统计理论
physics.soc-ph - 物理学与社会
q-bio.NC - 神经元与认知
q-bio.QM - 定量方法
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]Aesthetics of Neural Network Art
• [cs.AI]Can Robot Attract Passersby without Causing Discomfort by User-Centered Reinforcement Learning?
• [cs.AI]Computing the Scope of Applicability for Acquired Task Knowledge in Experience-Based Planning Domains
• [cs.AI]Generating and Sampling Orbits for Lifted Probabilistic Inference
• [cs.AI]Incremental Learning of Discrete Planning Domains from Continuous Perceptions
• [cs.AI]Natural Language Interaction with Explainable AI Models
• [cs.CL]A Deep Patent Landscaping Model using Transformer and Graph Convolutional Network
• [cs.CL]Consistent Dialogue Generation with Self-supervised Feature Learning
• [cs.CL]Interactive Concept Mining on Personal Data -- Bootstrapping Semantic Services
• [cs.CL]Low-Resource Syntactic Transfer with Unsupervised Source Reordering
• [cs.CL]MirrorGAN: Learning Text-to-image Generation by Redescription
• [cs.CL]OffensEval at SemEval-2018 Task 6: Identifying and Categorizing Offensive Language in Social Media
• [cs.CL]Survey of Text-based Epidemic Intelligence: A Computational Linguistic Perspective
• [cs.CL]To Tune or Not to Tune? Adapting Pretrained Representations to Diverse Tasks
• [cs.CR]ALOHA: Auxiliary Loss Optimization for Hypothesis Augmentation
• [cs.CV]Audiovisual Speaker Tracking using Nonlinear Dynamical Systems with Dynamic Stream Weights
• [cs.CV]Deep Residual Autoencoder for quality independent JPEG restoration
• [cs.CV]Deep learning enabled multi-wavelength spatial coherence microscope for the classification of malaria-infected stages with limited labelled data size
• [cs.CV]Dense Relational Captioning: Triple-Stream Networks for Relationship-Based Captioning
• [cs.CV]LPM: Learnable Pooling Module for Efficient Full-Face Gaze Estimation
• [cs.CV]Learning Orientation-Estimation Convolutional Neural Network for Building Detection in Optical Remote Sensing Image
• [cs.CV]Learning Parallax Attention for Stereo Image Super-Resolution
• [cs.CV]Learning to Reconstruct People in Clothing from a Single RGB Camera
• [cs.CV]Looking for the Devil in the Details: Learning Trilinear Attention Sampling Network for Fine-grained Image Recognition
• [cs.CV]MSG-GAN: Multi-Scale Gradients GAN for more stable and synchronized multi-scale image synthesis
• [cs.CV]Neural Scene Decomposition for Multi-Person Motion Capture
• [cs.CV]Neural Style Transfer for Point Clouds
• [cs.CV]PointNetLK: Robust & Efficient Point Cloud Registration using PointNet
• [cs.CV]Purifying Naturalistic Images through a Real-time Style Transfer Semantics Network
• [cs.CV]Putting Humans in a Scene: Learning Affordance in 3D Indoor Environments
• [cs.CV]Scalable Deep Convolutional Neural Networks for Sparse, Locally Dense Liquid Argon Time Projection Chamber Data
• [cs.CV]Scalable Facial Image Compression with Deep Feature Reconstruction
• [cs.CV]SimpleDet: A Simple and Versatile Distributed Framework for Object Detection and Instance Recognition
• [cs.CV]Superpixel-based Color Transfer
• [cs.CY]Using Machine Learning and Big Data Analytics to Prioritize Outpatients in HetNets
• [cs.DC]A Random Walk based Trust Ranking in Distributed Systems
• [cs.DC]Fast Approximate Shortest Paths in the Congested Clique
• [cs.DC]Fault Tolerant Network Constructors
• [cs.DC]Hadoop Perfect File: A fast access container for small files with direct in disc metadata access
• [cs.DC]More Bang for Your Buck: Improved use of GPU Nodes for GROMACS 2018
• [cs.FL]Regular Expressions with Backreferences: Polynomial-Time Matching Techniques
• [cs.HC]A Comprehensive Analysis of 2D&3D Video Watching of EEG Signals by Increasing PLSR and SVM Classification Results
• [cs.HC]VRKitchen: an Interactive 3D Virtual Environment for Task-oriented Learning
• [cs.IR]The Skipping Behavior of Users of Music Streaming Services and its Relation to Musical Structure
• [cs.IT]Bit-Interleaved Coded Multiple Beamforming in Millimeter-Wave Massive MIMO Systems
• [cs.IT]Channel Estimation and Hybrid Precoding for Distributed Phased Arrays Based MIMO Wireless Communications
• [cs.IT]Distributed Detection with Empirically Observed Statistics
• [cs.IT]Energy-Efficient Joint User-RB Association and Power Allocation for Uplink Hybrid NOMA-OMA
• [cs.IT]Energy-Efficient Power Allocation in Uplink mmWave Massive MIMO with NOMA
• [cs.IT]Large-Scale Beamforming for Massive MIMO via Randomized Sketching
• [cs.IT]Metrics which turn tilings into binary perfect codes
• [cs.IT]Multi-target detection with application to cryo-electron microscopy
• [cs.IT]Non-Coherent Joint Transmission in Poisson Cellular Networks Under Pilot Contamination
• [cs.IT]On power chi expansions of -divergences
• [cs.IT]Queue-Aware Variable-Length Coding for Ultra Reliable Low Latency Communications with Random Arrival
• [cs.IT]Rate Statistics in Cellular Downlink: PHY Rateless vs Adaptive Modulation and Coding
• [cs.IT]Reactive Sensing and Multiplicative Frame Super-resolution
• [cs.IT]Robust Matrix Completion via Maximum Correntropy Criterion and Half Quadratic Optimization
• [cs.IT]Routing-Based Delivery in Combination-Type Networks with Random Topology
• [cs.IT]Secure and Efficient Compressed Sensing Based Encryption With Sparse Matrices
• [cs.IT]Securing Downlink Massive MIMO-NOMA Networks with Artificial Noise
• [cs.IT]Wiretap Secret Key Capacity of Tree-PIN
• [cs.LG]Attribution-driven Causal Analysis for Detection of Adversarial Examples
• [cs.LG]AutoML @ NeurIPS 2018 challenge: Design and Results
• [cs.LG]Deep Reinforcement Learning with Feedback-based Exploration
• [cs.LG]Deep Switch Networks for Generating Discrete Data and Language
• [cs.LG]Diagnosing and Enhancing VAE Models
• [cs.LG]Functional Variational Bayesian Neural Networks
• [cs.LG]Fuzzy Rough Set Feature Selection to Enhance Phishing Attack Detection
• [cs.LG]High-Throughput CNN Inference on Embedded ARM big.LITTLE Multi-Core Processors
• [cs.LG]Improving Prostate Cancer Detection with Breast Histopathology Images
• [cs.LG]Inferring Personalized Bayesian Embeddings for Learning from Heterogeneous Demonstration
• [cs.LG]Learning Fast Algorithms for Linear Transforms Using Butterfly Factorizations
• [cs.LG]Low-rank Kernel Learning for Graph-based Clustering
• [cs.LG]On Applications of Bootstrap in Continuous Space Reinforcement Learning
• [cs.LG]Rectified Decision Trees: Towards Interpretability, Compression and Empirical Soundness
• [cs.LG]Reinforcement Learning with Dynamic Boltzmann Softmax Updates
• [cs.LG]Stochastic Beams and Where to Find Them: The Gumbel-Top-k Trick for Sampling Sequences Without Replacement
• [cs.LG]Tucker Tensor Layer in Fully Connected Neural Networks
• [cs.LG]Understanding Straight-Through Estimator in Training Activation Quantized Neural Nets
• [cs.MA]Simulating Emergent Properties of Human Driving Behavior Using Multi-Agent Reward Augmented Imitation Learning
• [cs.NE]Water Distribution System Design Using Multi-Objective Particle Swarm Optimisation
• [cs.NI]Cardinality Estimation in a Virtualized Network Device Using Online Machine Learning
• [cs.RO]Cooperative decentralized circumnavigation with application to algal bloom tracking
• [cs.RO]Detection and Tracking of Small Objects in Sparse 3D Laser Range Data
• [cs.RO]Inferring 3D Shapes of Unknown Rigid Objects in Clutter through Inverse Physics Reasoning
• [cs.RO]Multi-Robot Routing for Persistent Monitoring with Latency Constraints
• [cs.RO]Nuclear Environments Inspection with Micro Aerial Vehicles: Algorithms and Experiments
• [cs.RO]Persistification of Robotic Tasks
• [cs.RO]Sequence Planner - Automated Planning and Control for ROS2-based Collaborative and Intelligent Automation Systems
• [cs.RO]Spatiotemporal Decoupling Based LiDAR-Camera Calibration under Arbitrary Configurations
• [cs.RO]Uncertainty Aware Learning from Demonstrations in Multiple Contexts using Bayesian Neural Networks
• [cs.SE]A Novel Re-Targetable Application Development Platform for Healthcare Mobile Applications
• [cs.SE]Maybe Deep Neural Networks are the Best Choice for Modeling Source Code
• [cs.SE]What Makes Research Software Sustainable? An Interview Study With Research Software Engineers
• [cs.SI]Covert Networks: How Hard is It to Hide?
• [cs.SI]HopRank: How Semantic Structure Influences Teleportation in PageRank (A Case Study on BioPortal)
• [cs.SI]Interacting spreading processes in multilayer networks
• [cs.SY]A New Approach for Distributed Hypothesis Testing with Extensions to Byzantine-Resilience
• [eess.AS]Generative adversarial network-based glottal waveform model for statistical parametric speech synthesis
• [eess.IV]On Learning from Ghost Imaging without Imaging
• [eess.SP]Accessing From The Sky: A Tutorial on UAV Communications for 5G and Beyond
• [math.OC]Forecasting Spatio-Temporal Renewable Scenarios: a Deep Generative Approach
• [math.ST]Bayesian/Graphoid intersection property for factorisation models
• [math.ST]Discrete Statistical Models with Rational Maximum Likelihood Estimator
• [math.ST]High-dimensional nonparametric density estimation via symmetry and shape constraints
• [physics.soc-ph]Accumulation charts for instant-runoff elections
• [physics.soc-ph]Demarcating Geographic Regions using Community Detection in Commuting Networks
• [q-bio.NC]Recurrence required to capture the dynamic computations of the human ventral visual stream
• [q-bio.QM]Who and When to Screen: Multi-Round Active Screening for Recurrent Infectious Diseases Under Uncertainty
• [stat.AP]Implementation of Frequency-Severity Association in BMS Ratemaking
• [stat.CO]A Multi-armed Bandit MCMC, with applications in sampling from doubly intractable posterior
• [stat.CO]HCmodelSets: An R package for specifying sets of well-fitting models in regression with a large number of potential explanatory variables
• [stat.ME]Detecting causality in multivariate time series via non-uniform embedding
• [stat.ME]Distributionally Robust Selection of the Best
• [stat.ME]Rejoinder: "Gene Hunting with Hidden Markov Model Knockoffs"
• [stat.ME]Simultaneous Confidence Band for Stationary Covariance Function of Dense Functional Data
• [stat.ML]Deep Distribution Regression
• [stat.ML]Learning Dependency Structures for Weak Supervision Models
• [stat.ML]Trajectory Optimization for Unknown Constrained Systems using Reinforcement Learning
·····································
• [cs.AI]Aesthetics of Neural Network Art
Aaron Hertzmann
http://arxiv.org/abs/1903.05696v1
• [cs.AI]Can Robot Attract Passersby without Causing Discomfort by User-Centered Reinforcement Learning?
Yasunori Ozaki, Tatsuya Ishihara, Narimune Matsumura, Tadashi Nunobiki
http://arxiv.org/abs/1903.05881v1
• [cs.AI]Computing the Scope of Applicability for Acquired Task Knowledge in Experience-Based Planning Domains
Vahid Mokhtari, Luis Seabra Lopes, Armando Pinho, Roman Manevich
http://arxiv.org/abs/1903.06015v1
• [cs.AI]Generating and Sampling Orbits for Lifted Probabilistic Inference
Steven Holtzen, Todd Millstein, Guy Van den Broeck
http://arxiv.org/abs/1903.04672v2
• [cs.AI]Incremental Learning of Discrete Planning Domains from Continuous Perceptions
Luciano Serafini, Paolo Traverso
http://arxiv.org/abs/1903.05937v1
• [cs.AI]Natural Language Interaction with Explainable AI Models
Arjun R Akula, Sinisa Todorovic, Joyce Y Chai, Song-Chun Zhu
http://arxiv.org/abs/1903.05720v1
• [cs.CL]A Deep Patent Landscaping Model using Transformer and Graph Convolutional Network
Seokkyu Choi, Hyeonju Lee, Eunjeong Lucy Park, Sungchul Choi
http://arxiv.org/abs/1903.05823v1
• [cs.CL]Consistent Dialogue Generation with Self-supervised Feature Learning
Yizhe Zhang, Xiang Gao, Sungjin Lee, Chris Brockett, Michel Galley, Jianfeng Gao, Bill Dolan
http://arxiv.org/abs/1903.05759v1
• [cs.CL]Interactive Concept Mining on Personal Data -- Bootstrapping Semantic Services
Markus Schröder, Christian Jilek, Andreas Dengel
http://arxiv.org/abs/1903.05872v1
• [cs.CL]Low-Resource Syntactic Transfer with Unsupervised Source Reordering
Mohammad Sadegh Rasooli, Michael Collins
http://arxiv.org/abs/1903.05683v1
• [cs.CL]MirrorGAN: Learning Text-to-image Generation by Redescription
Tingting Qiao, Jing Zhang, Duanqing Xu, Dacheng Tao
http://arxiv.org/abs/1903.05854v1
• [cs.CL]OffensEval at SemEval-2018 Task 6: Identifying and Categorizing Offensive Language in Social Media
Silvia Sapora, Bogdan Lazarescu, Christo Lolov
http://arxiv.org/abs/1903.05929v1
• [cs.CL]Survey of Text-based Epidemic Intelligence: A Computational Linguistic Perspective
Aditya Joshi, Sarvnaz Karimi, Ross Sparks, Cecile Paris, C Raina MacIntyre
http://arxiv.org/abs/1903.05801v1
• [cs.CL]To Tune or Not to Tune? Adapting Pretrained Representations to Diverse Tasks
Matthew Peters, Sebastian Ruder, Noah A. Smith
http://arxiv.org/abs/1903.05987v1
• [cs.CR]ALOHA: Auxiliary Loss Optimization for Hypothesis Augmentation
Ethan M. Rudd, Felipe N. Ducau, Cody Wild, Konstantin Berlin, Richard Harang
http://arxiv.org/abs/1903.05700v1
• [cs.CV]Audiovisual Speaker Tracking using Nonlinear Dynamical Systems with Dynamic Stream Weights
Christopher Schymura, Dorothea Kolossa
http://arxiv.org/abs/1903.06031v1
• [cs.CV]Deep Residual Autoencoder for quality independent JPEG restoration
Simone Zini, Simone Bianco, Raimondo Schettini
http://arxiv.org/abs/1903.06117v1
• [cs.CV]Deep learning enabled multi-wavelength spatial coherence microscope for the classification of malaria-infected stages with limited labelled data size
Neeru Singla, Vishal Srivastava
http://arxiv.org/abs/1903.06056v1
• [cs.CV]Dense Relational Captioning: Triple-Stream Networks for Relationship-Based Captioning
Dong-Jin Kim, Jinsoo Choi, Tae-Hyun Oh, In So Kweon
http://arxiv.org/abs/1903.05942v1
• [cs.CV]LPM: Learnable Pooling Module for Efficient Full-Face Gaze Estimation
Reo Ogusu, Takao Yamanaka
http://arxiv.org/abs/1903.05761v1
• [cs.CV]Learning Orientation-Estimation Convolutional Neural Network for Building Detection in Optical Remote Sensing Image
Yongliang Chen
http://arxiv.org/abs/1903.05862v1
• [cs.CV]Learning Parallax Attention for Stereo Image Super-Resolution
Longguang Wang, Yingqian Wang, Zhengfa Liang, Zaiping Lin, Jungang Yang, Wei An, Yulan Guo
http://arxiv.org/abs/1903.05784v1
• [cs.CV]Learning to Reconstruct People in Clothing from a Single RGB Camera
Thiemo Alldieck, Marcus Magnor, Bharat Lal Bhatnagar, Christian Theobalt, Gerard Pons-Moll
http://arxiv.org/abs/1903.05885v1
• [cs.CV]Looking for the Devil in the Details: Learning Trilinear Attention Sampling Network for Fine-grained Image Recognition
Heliang Zheng, Jianlong Fu, Zheng-Jun Zha, Jiebo Luo
http://arxiv.org/abs/1903.06150v1
• [cs.CV]MSG-GAN: Multi-Scale Gradients GAN for more stable and synchronized multi-scale image synthesis
Animesh Karnewar, Raghu Sesha Iyengar
http://arxiv.org/abs/1903.06048v1
• [cs.CV]Neural Scene Decomposition for Multi-Person Motion Capture
Helge Rhodin, Victor Constantin, Isinsu Katircioglu, Mathieu Salzmann, Pascal Fua
http://arxiv.org/abs/1903.05684v1
• [cs.CV]Neural Style Transfer for Point Clouds
Xu Cao, Weimin Wang, Katashi Nagao
http://arxiv.org/abs/1903.05807v1
• [cs.CV]PointNetLK: Robust & Efficient Point Cloud Registration using PointNet
Yasuhiro Aoki, Hunter Goforth, Rangaprasad Arun Srivatsan, Simon Lucey
http://arxiv.org/abs/1903.05711v1
• [cs.CV]Purifying Naturalistic Images through a Real-time Style Transfer Semantics Network
Tongtong Zhao, Yuxiao Yan, Ibrahim Shehi Shehu, Xianping Fu, Huibing Wang
http://arxiv.org/abs/1903.05820v1
• [cs.CV]Putting Humans in a Scene: Learning Affordance in 3D Indoor Environments
Xueting Li, SIfei Liu, Kihwan Kim, Xiaolong Wang, Ming-Hsuan Yang, Jan Kautz
http://arxiv.org/abs/1903.05690v1
• [cs.CV]Scalable Deep Convolutional Neural Networks for Sparse, Locally Dense Liquid Argon Time Projection Chamber Data
Laura Dominé, Kazuhiro Terao
http://arxiv.org/abs/1903.05663v1
• [cs.CV]Scalable Facial Image Compression with Deep Feature Reconstruction
Shurun Wang, Shiqi Wang, Xinfeng Zhang, Shanshe Wang, Siwei Ma, Wen Gao
http://arxiv.org/abs/1903.05921v1
• [cs.CV]SimpleDet: A Simple and Versatile Distributed Framework for Object Detection and Instance Recognition
Yuntao Chen, Chenxia Han, Yanghao Li, Zehao Huang, Yi Jiang, Naiyan Wang, Zhaoxiang Zhang
http://arxiv.org/abs/1903.05831v1
• [cs.CV]Superpixel-based Color Transfer
Rémi Giraud, Vinh-Thong Ta, Nicolas Papadakis
http://arxiv.org/abs/1903.06010v1
• [cs.CY]Using Machine Learning and Big Data Analytics to Prioritize Outpatients in HetNets
Mohammed Hadi, Ahmed Lawey, Taisir El-Gorashi, Jaafar Elmirghani
http://arxiv.org/abs/1903.06045v1
• [cs.DC]A Random Walk based Trust Ranking in Distributed Systems
Alexander Stannat, Johan Pouwelse
http://arxiv.org/abs/1903.05900v1
• [cs.DC]Fast Approximate Shortest Paths in the Congested Clique
Keren Censor-Hillel, Michal Dory, Janne H. Korhonen, Dean Leitersdorf
http://arxiv.org/abs/1903.05956v1
• [cs.DC]Fault Tolerant Network Constructors
Othon Michail, Paul G. Spirakis, Michail Theofilatos
http://arxiv.org/abs/1903.05992v1
• [cs.DC]Hadoop Perfect File: A fast access container for small files with direct in disc metadata access
Jude Tchaye-Kondi, Yanlong Zhai, Kwei-Jay Lin, Wenjun Tao, Kai Yang
http://arxiv.org/abs/1903.05838v1
• [cs.DC]More Bang for Your Buck: Improved use of GPU Nodes for GROMACS 2018
Carsten Kutzner, Szilárd Páll, Martin Fechner, Ansgar Esztermann, Bert L. de Groot, Helmut Grubmüller
http://arxiv.org/abs/1903.05918v1
• [cs.FL]Regular Expressions with Backreferences: Polynomial-Time Matching Techniques
Markus L. Schmid
http://arxiv.org/abs/1903.05896v1
• [cs.HC]A Comprehensive Analysis of 2D&3D Video Watching of EEG Signals by Increasing PLSR and SVM Classification Results
Negin Manshouri, Temel Kayikcioglu
http://arxiv.org/abs/1903.05636v1
• [cs.HC]VRKitchen: an Interactive 3D Virtual Environment for Task-oriented Learning
Xiaofeng Gao, Ran Gong, Tianmin Shu, Xu Xie, Shu Wang, Song-Chun Zhu
http://arxiv.org/abs/1903.05757v1
• [cs.IR]The Skipping Behavior of Users of Music Streaming Services and its Relation to Musical Structure
Nicola Montecchio, Pierre Roy, François Pachet
http://arxiv.org/abs/1903.06008v1
• [cs.IT]Bit-Interleaved Coded Multiple Beamforming in Millimeter-Wave Massive MIMO Systems
Sadjad Sedighi, Ender Ayanoglu
http://arxiv.org/abs/1903.04693v2
• [cs.IT]Channel Estimation and Hybrid Precoding for Distributed Phased Arrays Based MIMO Wireless Communications
Yu Zhang, Yiming Huo, Dongming Wang, Xiaodai Dong, Xiaohu You
http://arxiv.org/abs/1903.05928v1
• [cs.IT]Distributed Detection with Empirically Observed Statistics
Haiyun He, Lin Zhou, Vincent Y. F. Tan
http://arxiv.org/abs/1903.05819v1
• [cs.IT]Energy-Efficient Joint User-RB Association and Power Allocation for Uplink Hybrid NOMA-OMA
Ming Zeng, Animesh Yadav, Octavia A. Dobre, H. Vincent Poor
http://arxiv.org/abs/1903.05756v1
• [cs.IT]Energy-Efficient Power Allocation in Uplink mmWave Massive MIMO with NOMA
Ming Zeng, Wanming Hao, Octavia A. Dobre, H. Vincent Poor
http://arxiv.org/abs/1903.05758v1
• [cs.IT]Large-Scale Beamforming for Massive MIMO via Randomized Sketching
Hayoung Choi, Tao Jiang, Yuanming Shi
http://arxiv.org/abs/1903.05904v1
• [cs.IT]Metrics which turn tilings into binary perfect codes
Gabriella Akemi Miyamoto, Marcelo Firer
http://arxiv.org/abs/1903.05951v1
• [cs.IT]Multi-target detection with application to cryo-electron microscopy
Tamir Bendory, Nicolas Boumal, William Leeb, Eitan Levin, Amit Singer
http://arxiv.org/abs/1903.06022v1
• [cs.IT]Non-Coherent Joint Transmission in Poisson Cellular Networks Under Pilot Contamination
Stelios Stefanatos, Gerhard Wunder
http://arxiv.org/abs/1903.05864v1
• [cs.IT]On power chi expansions of -divergences
Frank Nielsen, Gaëtan Hadjeres
http://arxiv.org/abs/1903.05818v1
• [cs.IT]Queue-Aware Variable-Length Coding for Ultra Reliable Low Latency Communications with Random Arrival
Xiaoyu Zhao, Wei Chen
http://arxiv.org/abs/1903.05804v1
• [cs.IT]Rate Statistics in Cellular Downlink: PHY Rateless vs Adaptive Modulation and Coding
Amogh Rajanna, Carl P. Dettmann
http://arxiv.org/abs/1903.05969v1
• [cs.IT]Reactive Sensing and Multiplicative Frame Super-resolution
John J. Benedetto, Michael R. Dellomo
http://arxiv.org/abs/1903.05677v1
• [cs.IT]Robust Matrix Completion via Maximum Correntropy Criterion and Half Quadratic Optimization
Yicong He, Fei Wang, Yingsong Li, Jing Qin, Badong Chen
http://arxiv.org/abs/1903.06055v1
• [cs.IT]Routing-Based Delivery in Combination-Type Networks with Random Topology
Mozhgan Bayat, Kai Wan, Giuseppe Caire
http://arxiv.org/abs/1903.06082v1
• [cs.IT]Secure and Efficient Compressed Sensing Based Encryption With Sparse Matrices
Wonwoo Cho, Nam Yul Yu
http://arxiv.org/abs/1903.05436v2
• [cs.IT]Securing Downlink Massive MIMO-NOMA Networks with Artificial Noise
Ming Zeng, Nam-Phong Nguyen, Octavia A. Dobre, H. Vincent Poor
http://arxiv.org/abs/1903.05752v1
• [cs.IT]Wiretap Secret Key Capacity of Tree-PIN
Alireza Poostindouz, Reihaneh Safavi-Naini
http://arxiv.org/abs/1903.06134v1
• [cs.LG]Attribution-driven Causal Analysis for Detection of Adversarial Examples
Susmit Jha, Sunny Raj, Steven Lawrence Fernandes, Sumit Kumar Jha, Somesh Jha, Gunjan Verma, Brian Jalaian, Ananthram Swami
http://arxiv.org/abs/1903.05821v1
• [cs.LG]AutoML @ NeurIPS 2018 challenge: Design and Results
Hugo Jair Escalante, Wei-Wei Tu, Isabelle Guyon, Daniel L. Silver, Evelyne Viegas, Yuqiang Chen, Wenyuan Dai, Qiang Yang
http://arxiv.org/abs/1903.05263v2
• [cs.LG]Deep Reinforcement Learning with Feedback-based Exploration
Jan Scholten, Daan Wout, Carlos Celemin, Jens Kober
http://arxiv.org/abs/1903.06151v1
• [cs.LG]Deep Switch Networks for Generating Discrete Data and Language
Payam Delgosha, Naveen Goela
http://arxiv.org/abs/1903.06135v1
• [cs.LG]Diagnosing and Enhancing VAE Models
Bin Dai, David Wipf
http://arxiv.org/abs/1903.05789v1
• [cs.LG]Functional Variational Bayesian Neural Networks
Shengyang Sun, Guodong Zhang, Jiaxin Shi, Roger Grosse
http://arxiv.org/abs/1903.05779v1
• [cs.LG]Fuzzy Rough Set Feature Selection to Enhance Phishing Attack Detection
Mahdieh Zabihimayvan, Derek Doran
http://arxiv.org/abs/1903.05675v1
• [cs.LG]High-Throughput CNN Inference on Embedded ARM big.LITTLE Multi-Core Processors
Siqi Wang, Gayathri Ananthanarayanan, Yifan Zeng, Neeraj Goel, Anuj Pathania, Tulika Mitra
http://arxiv.org/abs/1903.05898v1
• [cs.LG]Improving Prostate Cancer Detection with Breast Histopathology Images
Umair Akhtar Hasan Khan, Carolin Stürenberg, Oguzhan Gencoglu, Kevin Sandeman, Timo Heikkinen, Antti Rannikko, Tuomas Mirtti
http://arxiv.org/abs/1903.05769v1
• [cs.LG]Inferring Personalized Bayesian Embeddings for Learning from Heterogeneous Demonstration
Rohan Paleja, Matthew Gombolay
http://arxiv.org/abs/1903.06047v1
• [cs.LG]Learning Fast Algorithms for Linear Transforms Using Butterfly Factorizations
Tri Dao, Albert Gu, Matthew Eichhorn, Atri Rudra, Christopher Ré
http://arxiv.org/abs/1903.05895v1
• [cs.LG]Low-rank Kernel Learning for Graph-based Clustering
Zhao Kang, Liangjian Wen, Wenyu Chen, Zenglin Xu
http://arxiv.org/abs/1903.05962v1
• [cs.LG]On Applications of Bootstrap in Continuous Space Reinforcement Learning
Mohamad Kazem Shirani Faradonbeh, Ambuj Tewari, George Michailidis
http://arxiv.org/abs/1903.05803v1
• [cs.LG]Rectified Decision Trees: Towards Interpretability, Compression and Empirical Soundness
Jiawang Bai, Yiming Li, Jiawei Li, Yong Jiang, Shutao Xia
http://arxiv.org/abs/1903.05965v1
• [cs.LG]Reinforcement Learning with Dynamic Boltzmann Softmax Updates
Ling Pan, Qingpeng Cai, Qi Meng, Longbo Huang, Tie-Yan Liu
http://arxiv.org/abs/1903.05926v1
• [cs.LG]Stochastic Beams and Where to Find Them: The Gumbel-Top-k Trick for Sampling Sequences Without Replacement
Wouter Kool, Herke van Hoof, Max Welling
http://arxiv.org/abs/1903.06059v1
• [cs.LG]Tucker Tensor Layer in Fully Connected Neural Networks
Giuseppe G. Calvi, Ahmad Moniri, Mahmoud Mahfouz, Zeyang Yu, Qibin Zhao, Danilo P. Mandic
http://arxiv.org/abs/1903.06133v1
• [cs.LG]Understanding Straight-Through Estimator in Training Activation Quantized Neural Nets
Penghang Yin, Jiancheng Lyu, Shuai Zhang, Stanley Osher, Yingyong Qi, Jack Xin
http://arxiv.org/abs/1903.05662v1
• [cs.MA]Simulating Emergent Properties of Human Driving Behavior Using Multi-Agent Reward Augmented Imitation Learning
Raunak P. Bhattacharyya, Derek J. Phillips, Changliu Liu, Jayesh K. Gupta, Katherine Driggs-Campbell, Mykel J. Kochenderfer
http://arxiv.org/abs/1903.05766v1
• [cs.NE]Water Distribution System Design Using Multi-Objective Particle Swarm Optimisation
Mahesh B. Patil, M. Naveen Naidu, A. Vasan, Murari R. R. Varma
http://arxiv.org/abs/1903.06127v1
• [cs.NI]Cardinality Estimation in a Virtualized Network Device Using Online Machine Learning
Reuven Cohen, Yuval Nezri
http://arxiv.org/abs/1903.05728v1
• [cs.RO]Cooperative decentralized circumnavigation with application to algal bloom tracking
Joana Fonseca, Jieqiang Wei, Karl H. Johansson, Tor Arne Johansen
http://arxiv.org/abs/1903.05993v1
• [cs.RO]Detection and Tracking of Small Objects in Sparse 3D Laser Range Data
Jan Razlaw, Jan Quenzel, Sven Behnke
http://arxiv.org/abs/1903.05889v1
• [cs.RO]Inferring 3D Shapes of Unknown Rigid Objects in Clutter through Inverse Physics Reasoning
Changkyu Song, Abdeslam Boularias
http://arxiv.org/abs/1903.05749v1
• [cs.RO]Multi-Robot Routing for Persistent Monitoring with Latency Constraints
Ahmad Bilal Asghar, Stephen L. Smith, Shreyas Sundaram
http://arxiv.org/abs/1903.06105v1
• [cs.RO]Nuclear Environments Inspection with Micro Aerial Vehicles: Algorithms and Experiments
Dinesh Thakur, Giuseppe Loianno, Wenxin Liu, Vijay Kumar
http://arxiv.org/abs/1903.06111v1
• [cs.RO]Persistification of Robotic Tasks
Gennaro Notomista, Magnus Egerstedt
http://arxiv.org/abs/1903.05810v1
• [cs.RO]Sequence Planner - Automated Planning and Control for ROS2-based Collaborative and Intelligent Automation Systems
Martin Dahl, Endre Erös, Atieh Hanna, Kristofer Bengtsson, Petter Falkman
http://arxiv.org/abs/1903.05850v1
• [cs.RO]Spatiotemporal Decoupling Based LiDAR-Camera Calibration under Arbitrary Configurations
Bo Fu, Yue Wang, Yanmei Jiao, Xiaqing Ding, Li Tang, Rong Xiong
http://arxiv.org/abs/1903.06141v1
• [cs.RO]Uncertainty Aware Learning from Demonstrations in Multiple Contexts using Bayesian Neural Networks
Sanjay Thakur, Herke van Hoof, Juan Camilo Gamboa Higuera, Doina Precup, David Meger
http://arxiv.org/abs/1903.05697v1
• [cs.SE]A Novel Re-Targetable Application Development Platform for Healthcare Mobile Applications
Chae Ho Cho, Fatemehsadat Tabei, Tra Nguyen Phan, Yeesock Kim, Jo Woon Chong
http://arxiv.org/abs/1903.05783v1
• [cs.SE]Maybe Deep Neural Networks are the Best Choice for Modeling Source Code
Rafael-Michael Karampatsis, Charles Sutton
http://arxiv.org/abs/1903.05734v1
• [cs.SE]What Makes Research Software Sustainable? An Interview Study With Research Software Engineers
Mario Rosado de Souza, Robert Haines, Markel Vigo, Caroline Jay
http://arxiv.org/abs/1903.06039v1
• [cs.SI]Covert Networks: How Hard is It to Hide?
Palash Dey, Sourav Medya
http://arxiv.org/abs/1903.05832v1
• [cs.SI]HopRank: How Semantic Structure Influences Teleportation in PageRank (A Case Study on BioPortal)
Lisette Espín-Noboa, Simon Walk, Markus Strohmaier, Mark A. Musen
http://arxiv.org/abs/1903.05704v1
• [cs.SI]Interacting spreading processes in multilayer networks
Piotr Bródka, Katarzyna Musial, Jarosław Jankowski
http://arxiv.org/abs/1903.05932v1
• [cs.SY]A New Approach for Distributed Hypothesis Testing with Extensions to Byzantine-Resilience
Aritra Mitra, John A. Richards, Shreyas Sundaram
http://arxiv.org/abs/1903.05817v1
• [eess.AS]Generative adversarial network-based glottal waveform model for statistical parametric speech synthesis
Bajibabu Bollepalli, Lauri Juvela, Paavo Alku
http://arxiv.org/abs/1903.05955v1
• [eess.IV]On Learning from Ghost Imaging without Imaging
Issei Sato
http://arxiv.org/abs/1903.06009v1
• [eess.SP]Accessing From The Sky: A Tutorial on UAV Communications for 5G and Beyond
Yong Zeng, Qingqing Wu, Rui Zhang
http://arxiv.org/abs/1903.05289v2
• [math.OC]Forecasting Spatio-Temporal Renewable Scenarios: a Deep Generative Approach
Congmei Jiang, Yize Chen, Yongfang Mao, Yi Chai, Mingbiao Yu
http://arxiv.org/abs/1903.05274v1
• [math.ST]Bayesian/Graphoid intersection property for factorisation models
Grégoire Sergeant-Perthuis
http://arxiv.org/abs/1903.06026v1
• [math.ST]Discrete Statistical Models with Rational Maximum Likelihood Estimator
Eliana Duarte, Orlando Marigliano, Bernd Sturmfels
http://arxiv.org/abs/1903.06110v1
• [math.ST]High-dimensional nonparametric density estimation via symmetry and shape constraints
Min Xu, Richard J. Samworth
http://arxiv.org/abs/1903.06092v1
• [physics.soc-ph]Accumulation charts for instant-runoff elections
Bridget Eileen Tenner, Gregory S. Warrington
http://arxiv.org/abs/1903.06095v1
• [physics.soc-ph]Demarcating Geographic Regions using Community Detection in Commuting Networks
Mark He, Joseph Glasser, Nathaniel Pritchard, Shankar Bhamidi, Nikhil Kaza
http://arxiv.org/abs/1903.06029v1
• [q-bio.NC]Recurrence required to capture the dynamic computations of the human ventral visual stream
Tim C Kietzmann, Courtney J Spoerer, Lynn Sörensen, Radoslaw M Cichy, Olaf Hauk, Nikolaus Kriegeskorte
http://arxiv.org/abs/1903.05946v1
• [q-bio.QM]Who and When to Screen: Multi-Round Active Screening for Recurrent Infectious Diseases Under Uncertainty
Han-Ching Ou, Arunesh Sinha, Sze-Chuan Suen, Andrew Perrault, Milind Tambe
http://arxiv.org/abs/1903.06113v1
• [stat.AP]Implementation of Frequency-Severity Association in BMS Ratemaking
Rosy Oh, Peng Shi, Jae Youn Ahn
http://arxiv.org/abs/1903.05851v1
• [stat.CO]A Multi-armed Bandit MCMC, with applications in sampling from doubly intractable posterior
Wang Guanyang
http://arxiv.org/abs/1903.05726v1
• [stat.CO]HCmodelSets: An R package for specifying sets of well-fitting models in regression with a large number of potential explanatory variables
Henrique Helfer Hoeltgebaum, Heather Battey
http://arxiv.org/abs/1903.05715v1
• [stat.ME]Detecting causality in multivariate time series via non-uniform embedding
Ziyu Jia, Youfang Lin, Yunxiao Liu, Zehui Jiao, Yan Ma, Jing Wang
http://arxiv.org/abs/1903.05842v1
• [stat.ME]Distributionally Robust Selection of the Best
Weiwei Fan, L. Jeff Hong, Xiaowei Zhang
http://arxiv.org/abs/1903.05828v1
• [stat.ME]Rejoinder: "Gene Hunting with Hidden Markov Model Knockoffs"
Matteo Sesia, Chiara Sabatti, Emmanuel J. Candès
http://arxiv.org/abs/1903.05701v1
• [stat.ME]Simultaneous Confidence Band for Stationary Covariance Function of Dense Functional Data
Jiangyan Wang, Guanqun Cao, Li Wang, Lijian Yang
http://arxiv.org/abs/1903.05522v2
• [stat.ML]Deep Distribution Regression
Rui Li, Howard D. Bondell, Brian J. Reich
http://arxiv.org/abs/1903.06023v1
• [stat.ML]Learning Dependency Structures for Weak Supervision Models
Paroma Varma, Frederic Sala, Ann He, Alexander Ratner, Christopher Ré
http://arxiv.org/abs/1903.05844v1
• [stat.ML]Trajectory Optimization for Unknown Constrained Systems using Reinforcement Learning
Kei Ota, Devesh K. Jha, Tomoaki Oiki, Mamoru Miura, Takashi Nammoto, Daniel Nikovski, Toshisada Mariyama
http://arxiv.org/abs/1903.05751v1
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