cond-mat.stat-mech - 统计数学
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.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.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.SI - 社交网络与信息网络
eess.SP - 信号处理
math.RT - 表象理论
math.ST - 统计理论
physics.comp-ph - 计算物理学
physics.data-an - 数据分析、 统计和概率
physics.soc-ph - 物理学与社会
q-bio.TO - 组织和器官
q-fin.CP -计算金融学
quant-ph - 量子物理
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
• [cond-mat.stat-mech]Machine learning method for single trajectory characterization
• [cs.AI]Can Sophisticated Dispatching Strategy Acquired by Reinforcement Learning? - A Case Study in Dynamic Courier Dispatching System
• [cs.AI]Composite Event Recognition for Maritime Monitoring: Industry Paper
• [cs.AI]Concurrent Meta Reinforcement Learning
• [cs.AI]Lifted Weight Learning of Markov Logic Networks Revisited
• [cs.CL]A Character-Level Approach to the Text Normalization Problem Based on a New Causal Encoder
• [cs.CL]Active and Semi-Supervised Learning in ASR: Benefits on the Acoustic and Language Models
• [cs.CL]Arabic natural language processing: An overview
• [cs.CL]Creation and Evaluation of Datasets for Distributional Semantics Tasks in the Digital Humanities Domain
• [cs.CL]Imposing Label-Relational Inductive Bias for Extremely Fine-Grained Entity Typing
• [cs.CL]Learning to Speak and Act in a Fantasy Text Adventure Game
• [cs.CL]Multi-Instance Learning for End-to-End Knowledge Base Question Answering
• [cs.CL]Neural Language Modeling with Visual Features
• [cs.CL]Option Comparison Network for Multiple-choice Reading Comprehension
• [cs.CL]Predicting Research Trends From Arxiv
• [cs.CL]SemEval 2019 Task 1: Cross-lingual Semantic Parsing with UCCA
• [cs.CL]Sentence Embedding Alignment for Lifelong Relation Extraction
• [cs.CL]Small-world networks for summarization of biomedical articles
• [cs.CR]Attack Graph Obfuscation
• [cs.CR]Detection of Advanced Malware by Machine Learning Techniques
• [cs.CV]Active Scene Learning
• [cs.CV]Alternating Phase Projected Gradient Descent with Generative Priors for Solving Compressive Phase Retrieval
• [cs.CV]Attack Type Agnostic Perceptual Enhancement of Adversarial Images
• [cs.CV]CE-Net: Context Encoder Network for 2D Medical Image Segmentation
• [cs.CV]COIN: A Large-scale Dataset for Comprehensive Instructional Video Analysis
• [cs.CV]Clear Skies Ahead: Towards Real-Time Automatic Sky Replacement in Video
• [cs.CV]Correction of Electron Back-scattered Diffraction datasets using an evolutionary algorithm
• [cs.CV]Discovering Visual Patterns in Art Collections with Spatially-consistent Feature Learning
• [cs.CV]Exploit fully automatic low-level segmented PET data for training high-level deep learning algorithms for the corresponding CT data
• [cs.CV]Graphical Contrastive Losses for Scene Graph Generation
• [cs.CV]Hair Segmentation on Time-of-Flight RGBD Images
• [cs.CV]IMEXnet: A Forward Stable Deep Neural Network
• [cs.CV]Label Embedded Dictionary Learning for Image Classification
• [cs.CV]Learning deep neural networks in blind deblurring framework
• [cs.CV]Novel quantitative indicators of digital ophthalmoscopy image quality
• [cs.CV]RAVEN: A Dataset for Relational and Analogical Visual rEasoNing
• [cs.CV]Robust Semantic Segmentation By Dense Fusion Network On Blurred VHR Remote Sensing Images
• [cs.CV]SR-LSTM: State Refinement for LSTM towards Pedestrian Trajectory Prediction
• [cs.CV]Stratified Labeling for Surface Consistent Parallax Correction and Occlusion Completion
• [cs.CV]Synthetic Human Model Dataset for Skeleton Driven Non-rigid Motion Tracking and 3D Reconstruction
• [cs.CV]Temporal Registration in Application to In-utero MRI Time Series
• [cs.CV]Ultrasound Image Representation Learning by Modeling Sonographer Visual Attention
• [cs.CV]Understanding Ancient Coin Images
• [cs.CV]Using DP Towards A Shortest Path Problem-Related Application
• [cs.CV]Weakly Supervised Complementary Parts Models for Fine-Grained Image Classification from the Bottom Up
• [cs.CY]Engaging Users with Educational Games: The Case of Phishing
• [cs.CY]Seniors' Media Preference for Receiving Internet Security Information: A Pilot Study
• [cs.DC]An Introduction to hpxMP -- A Modern OpenMP Implementation Leveraging Asynchronous Many-Tasking System
• [cs.DC]Invariant Safety for Distributed Applications
• [cs.DC]Neighborhood Mutual Remainder: Self-Stabilizing Implementation of Look-Compute-Move Robots (Extended Abstract)
• [cs.DC]Towards a Uniform Architecture for the Efficient Implementation of 2D and 3D Deconvolutional Neural Networks on FPGAs
• [cs.GT]Selling Multiple Items via Social Networks
• [cs.HC]Integrating Artificial and Human Intelligence for Efficient Translation
• [cs.IR]ViTOR: Learning to Rank Webpages Based on Visual Features
• [cs.IT]A General Construction and Encoder Implementation of Polar Codes
• [cs.IT]A Scalable Max-Consensus Protocol For Noisy Ultra-Dense Networks
• [cs.IT]Deep Learning based End-to-End Wireless Communication Systems with Conditional GAN as Unknown Channel
• [cs.IT]Deep Learning for Channel Coding via Neural Mutual Information Estimation
• [cs.IT]Deep Learning for UL/DL Channel Calibration in Generic Massive MIMO Systems
• [cs.IT]IoT-U: Cellular Internet-of-Things Networks over Unlicensed Spectrum
• [cs.IT]Joint Dynamic Pricing and Radio Resource Allocation Framework for IoT Services
• [cs.IT]Non-Stationarities in Extra-Large Scale Massive MIMO
• [cs.IT]On the security of a Loidreau's rank metric code based encryption scheme
• [cs.IT]Rigorous Analysis of Spectral Methods for Random Orthogonal Matrices
• [cs.LG]A Rank-1 Sketch for Matrix Multiplicative Weights
• [cs.LG]Accurate inference of crowdsourcing properties when using efficient allocation strategies
• [cs.LG]Analysis Dictionary Learning: An Efficient and Discriminative Solution
• [cs.LG]Doubly Aligned Incomplete Multi-view Clustering
• [cs.LG]Efficient and Effective Quantization for Sparse DNNs
• [cs.LG]Fast Graph Representation Learning with PyTorch Geometric
• [cs.LG]Fast Parallel Algorithms for Feature Selection
• [cs.LG]GanDef: A GAN based Adversarial Training Defense for Neural Network Classifier
• [cs.LG]Generative Graph Convolutional Network for Growing Graphs
• [cs.LG]Improving Generalization of Deep Networks for Inverse Reconstruction of Image Sequences
• [cs.LG]Interpretable Deep Learning in Drug Discovery
• [cs.LG]Limiting Network Size within Finite Bounds for Optimization
• [cs.LG]Mean-field Analysis of Batch Normalization
• [cs.LG]Meta-Dataset: A Dataset of Datasets for Learning to Learn from Few Examples
• [cs.LG]Only sparsity based loss function for learning representations
• [cs.LG]Out-domain examples for generative models
• [cs.LG]Quantum Latent Semantic Analysis
• [cs.LG]RLOC: Neurobiologically Inspired Hierarchical Reinforcement Learning Algorithm for Continuous Control of Nonlinear Dynamical Systems
• [cs.LG]Robust and Communication-Efficient Federated Learning from Non-IID Data
• [cs.LG]Self-Tuning Networks: Bilevel Optimization of Hyperparameters using Structured Best-Response Functions
• [cs.LG]The Privacy Blanket of the Shuffle Model
• [cs.LG]The Variational Predictive Natural Gradient
• [cs.LG]Using World Models for Pseudo-Rehearsal in Continual Learning
• [cs.LG]When random search is not enough: Sample-Efficient and Noise-Robust Blackbox Optimization of RL Policies
• [cs.MA]Convergence of Multi-Agent Learning with a Finite Step Size in General-Sum Games
• [cs.NE]A Novel Neural Network Structure Constructed according to Logical Relations
• [cs.NE]jMetalPy: a Python Framework for Multi-Objective Optimization with Metaheuristics
• [cs.NI]Allocation of Computation-Intensive Graph Jobs over Vehicular Clouds
• [cs.RO]A Lane-Change Path Planner and its application with a monocular camera
• [cs.RO]An Inverting-Tube Clutching Contractile Soft Pneumatic Actuator
• [cs.RO]Deep Visual MPC-Policy Learning for Navigation
• [cs.RO]Locating Transparent Objects to Millimetre Accuracy
• [cs.RO]Robust Execution of Contact-Rich Motion Plans by Hybrid Force-Velocity Control
• [cs.RO]Towards Better Human Robot Collaboration with Robust Plan Recognition and Trajectory Prediction
• [cs.SI]HEAT: Hyperbolic Embedding of Attributed Networks
• [cs.SI]Learning Graphs from Noisy Epidemic Cascades
• [cs.SI]Structure-Preserving Community In A Multilayer Network: Definition, Detection, And Analysis
• [cs.SI]Twitter Speaks: A Case of National Disaster Situational Awareness
• [eess.SP]Scattering Mechanisms and Modeling for Terahertz Wireless Communications
• [math.RT]Reparameterizing Distributions on Lie Groups
• [math.ST]Integral Transform Methods in Goodness-of-Fit Testing, II: The Wishart Distributions
• [math.ST]Nonparametric Change Point Detection in Regression
• [math.ST]Solutions to Sparse Multilevel Matrix Problems
• [math.ST]Tutorial: Deriving The Efficient Influence Curve for Large Models
• [physics.comp-ph]Deep learning observables in computational fluid dynamics
• [physics.data-an]Accurate reconstruction of EBSD datasets by a multimodal data approach using an evolutionary algorithm
• [physics.soc-ph]Trip Centrality: walking on a temporal multiplex with non-instantaneous link travel time
• [q-bio.TO]Characterization of Posidonia Oceanica Seagrass Aerenchyma through Whole Slide Imaging: A Pilot Study
• [q-fin.CP]Learning the population dynamics of technical trading strategies
• [quant-ph]Quantum hardness of learning shallow classical circuits
• [stat.AP]Estimating a pressure dependent thermal conductivity coefficient with applications in food technology
• [stat.AP]Semi-Supervised Non-Parametric Bayesian Modelling of Spatial Proteomics
• [stat.CO]Estimation and uncertainty quantification for the output from quantum simulators
• [stat.ME]A comment on "New non-parametric inferences for low-income proportions" by Shan Luo and Gengsheng Qin
• [stat.ME]Relaxing the Assumptions of Knockoffs by Conditioning
• [stat.ME]Simultaneous Prediction Intervals for Small Area Parameter
• [stat.ML]Adversarial Mixup Resynthesizers
• [stat.ML]Deep Random Splines for Point Process Intensity Estimation
• [stat.ML]GRATIS: GeneRAting TIme Series with diverse and controllable characteristics
• [stat.ML]Multi-output Bus Travel Time Prediction with Convolutional LSTM Neural Network
• [stat.ML]On Convergence Rate of the Gaussian Belief Propagation Algorithm for Markov Networks
• [stat.ML]On Transformations in Stochastic Gradient MCMC
·····································
• [cond-mat.stat-mech]Machine learning method for single trajectory characterization
Gorka Muñoz-Gil, Miguel Angel Garcia-March, Carlo Manzo, José D. Martín-Guerrero, Maciej Lewenstein
http://arxiv.org/abs/1903.02850v1
• [cs.AI]Can Sophisticated Dispatching Strategy Acquired by Reinforcement Learning? - A Case Study in Dynamic Courier Dispatching System
Yujie Chen, Yu Qian, Yichen Yao, Zili Wu, Rongqi Li, Yinzhi Zhou, Haoyuan Hu, Yinghui Xu
http://arxiv.org/abs/1903.02716v1
• [cs.AI]Composite Event Recognition for Maritime Monitoring: Industry Paper
Manolis Pitsikalis, Alexander Artikis, Richard Dreo, Cyril Ray, Elena Camossi, Anne-Laure Jousselme
http://arxiv.org/abs/1903.03078v1
• [cs.AI]Concurrent Meta Reinforcement Learning
Emilio Parisotto, Soham Ghosh, Sai Bhargav Yalamanchi, Varsha Chinnaobireddy, Yuhuai Wu, Ruslan Salakhutdinov
http://arxiv.org/abs/1903.02710v1
• [cs.AI]Lifted Weight Learning of Markov Logic Networks Revisited
Ondrej Kuzelka, Vyacheslav Kungurtsev
http://arxiv.org/abs/1903.03099v1
• [cs.CL]A Character-Level Approach to the Text Normalization Problem Based on a New Causal Encoder
Adrián Javaloy Bornás, Ginés García Mateos
http://arxiv.org/abs/1903.02642v1
• [cs.CL]Active and Semi-Supervised Learning in ASR: Benefits on the Acoustic and Language Models
Thomas Drugman, Janne Pylkkonen, Reinhard Kneser
http://arxiv.org/abs/1903.02852v1
• [cs.CL]Arabic natural language processing: An overview
Imane Guellil, Houda Saâdane, Faical Azouaou, Billel Gueni, Damien Nouvel
http://arxiv.org/abs/1903.02784v1
• [cs.CL]Creation and Evaluation of Datasets for Distributional Semantics Tasks in the Digital Humanities Domain
Gerhard Wohlgenannt, Ariadna Barinova, Dmitry Ilvovsky, Ekaterina Chernyak
http://arxiv.org/abs/1903.02671v1
• [cs.CL]Imposing Label-Relational Inductive Bias for Extremely Fine-Grained Entity Typing
Wenhan Xiong, Jiawei Wu, Deren Lei, Mo Yu, Shiyu Chang, Xiaoxiao Guo, William Yang Wang
http://arxiv.org/abs/1903.02591v1
• [cs.CL]Learning to Speak and Act in a Fantasy Text Adventure Game
Jack Urbanek, Angela Fan, Siddharth Karamcheti, Saachi Jain, Samuel Humeau, Emily Dinan, Tim Rocktäschel, Douwe Kiela, Arthur Szlam, Jason Weston
http://arxiv.org/abs/1903.03094v1
• [cs.CL]Multi-Instance Learning for End-to-End Knowledge Base Question Answering
Mengxi Wei, Yifan He, Qiong Zhang, Luo Si
http://arxiv.org/abs/1903.02652v1
• [cs.CL]Neural Language Modeling with Visual Features
Antonios Anastasopoulos, Shankar Kumar, Hank Liao
http://arxiv.org/abs/1903.02930v1
• [cs.CL]Option Comparison Network for Multiple-choice Reading Comprehension
Qiu Ran, Peng Li, Weiwei Hu, Jie Zhou
http://arxiv.org/abs/1903.03033v1
• [cs.CL]Predicting Research Trends From Arxiv
Steffen Eger, Chao Li, Florian Netzer, Iryna Gurevych
http://arxiv.org/abs/1903.02831v1
• [cs.CL]SemEval 2019 Task 1: Cross-lingual Semantic Parsing with UCCA
Daniel Hershcovich, Zohar Aizenbud, Leshem Choshen, Elior Sulem, Ari Rappoport, Omri Abend
http://arxiv.org/abs/1903.02953v1
• [cs.CL]Sentence Embedding Alignment for Lifelong Relation Extraction
Hong Wang, Wenhan Xiong, Mo Yu, Xiaoxiao Guo, Shiyu Chang, William Yang Wang
http://arxiv.org/abs/1903.02588v1
• [cs.CL]Small-world networks for summarization of biomedical articles
Milad Moradi
http://arxiv.org/abs/1903.02861v1
• [cs.CR]Attack Graph Obfuscation
Rami Puzis, Hadar Polad, Bracha Shapira
http://arxiv.org/abs/1903.02601v1
• [cs.CR]Detection of Advanced Malware by Machine Learning Techniques
Sanjay Sharma, C. Rama Krishna, Sanjay K. Sahay
http://arxiv.org/abs/1903.02966v1
• [cs.CV]Active Scene Learning
Erelcan Yanik, Tevfik Metin Sezgin
http://arxiv.org/abs/1903.02832v1
• [cs.CV]Alternating Phase Projected Gradient Descent with Generative Priors for Solving Compressive Phase Retrieval
Rakib Hyder, Viraj Shah, Chinmay Hegde, M. Salman Asif
http://arxiv.org/abs/1903.02707v1
• [cs.CV]Attack Type Agnostic Perceptual Enhancement of Adversarial Images
Bilgin Aksoy, Alptekin Temizel
http://arxiv.org/abs/1903.03029v1
• [cs.CV]CE-Net: Context Encoder Network for 2D Medical Image Segmentation
Zaiwang Gu, Jun Cheng, Huazhu Fu, Kang Zhou, Huaying Hao, Yitian Zhao, Tianyang Zhang, Shenghua Gao, Jiang Liu
http://arxiv.org/abs/1903.02740v1
• [cs.CV]COIN: A Large-scale Dataset for Comprehensive Instructional Video Analysis
Yansong Tang, Dajun Ding, Yongming Rao, Yu Zheng, Danyang Zhang, Lili Zhao, Jiwen Lu, Jie Zhou
http://arxiv.org/abs/1903.02874v1
• [cs.CV]Clear Skies Ahead: Towards Real-Time Automatic Sky Replacement in Video
Tavi Halperin, Harel Cain, Ofir Bibi, Michael Werman
http://arxiv.org/abs/1903.02582v1
• [cs.CV]Correction of Electron Back-scattered Diffraction datasets using an evolutionary algorithm
Florian Strub, Marie-Agathe Charpagne, Tresa M. Pollock
http://arxiv.org/abs/1903.02982v1
• [cs.CV]Discovering Visual Patterns in Art Collections with Spatially-consistent Feature Learning
Xi Shen, Alexei A. Efros, Aubry Mathieu
http://arxiv.org/abs/1903.02678v1
• [cs.CV]Exploit fully automatic low-level segmented PET data for training high-level deep learning algorithms for the corresponding CT data
Christina Gsaxner, Peter M. Roth, Jürgen Wallner, Jan Egger
http://arxiv.org/abs/1903.02871v1
• [cs.CV]Graphical Contrastive Losses for Scene Graph Generation
Ji Zhang, Kevin J. Shih, Ahmed Elgammal, Andrew Tao, Bryan Catanzaro
http://arxiv.org/abs/1903.02728v1
• [cs.CV]Hair Segmentation on Time-of-Flight RGBD Images
Yuanxi Ma, Cen Wan, Guli Zhang, Qilei Jiang, Shiying Li, Jingyi Yu
http://arxiv.org/abs/1903.02775v1
• [cs.CV]IMEXnet: A Forward Stable Deep Neural Network
Eldad Haber, Keegan Lensink, Eran Triester, Lars Ruthotto
http://arxiv.org/abs/1903.02639v1
• [cs.CV]Label Embedded Dictionary Learning for Image Classification
Shuai Shao, Yan-Jiang Wang, Bao-Di Liu, Weifeng Liu
http://arxiv.org/abs/1903.03087v1
• [cs.CV]Learning deep neural networks in blind deblurring framework
Junde Wu, Xiaoguang Di, Jiehao Huang, Yu Zhang
http://arxiv.org/abs/1903.02731v1
• [cs.CV]Novel quantitative indicators of digital ophthalmoscopy image quality
Chris von Csefalvay
http://arxiv.org/abs/1903.02695v1
• [cs.CV]RAVEN: A Dataset for Relational and Analogical Visual rEasoNing
Chi Zhang, Feng Gao, Baoxiong Jia, Yixin Zhu, Song-Chun Zhu
http://arxiv.org/abs/1903.02741v1
• [cs.CV]Robust Semantic Segmentation By Dense Fusion Network On Blurred VHR Remote Sensing Images
Yi Peng, Shihao Sun, Yining Pan, Ruirui Li
http://arxiv.org/abs/1903.02702v1
• [cs.CV]SR-LSTM: State Refinement for LSTM towards Pedestrian Trajectory Prediction
Pu Zhang, Wanli Ouyang, Pengfei Zhang, Jianru Xue, Nanning Zheng
http://arxiv.org/abs/1903.02793v1
• [cs.CV]Stratified Labeling for Surface Consistent Parallax Correction and Occlusion Completion
Jie Chen, Lap-Pui Chau, Junhui Hou
http://arxiv.org/abs/1903.02688v1
• [cs.CV]Synthetic Human Model Dataset for Skeleton Driven Non-rigid Motion Tracking and 3D Reconstruction
Shafeeq Elanattil, Peyman Moghadam
http://arxiv.org/abs/1903.02679v1
• [cs.CV]Temporal Registration in Application to In-utero MRI Time Series
Ruizhi Liao, Esra A. Turk, Miaomiao Zhang, Jie Luo, Elfar Adalsteinsson, P. Ellen Grant, Polina Golland
http://arxiv.org/abs/1903.02959v1
• [cs.CV]Ultrasound Image Representation Learning by Modeling Sonographer Visual Attention
Richard Droste, Yifan Cai, Harshita Sharma, Pierre Chatelain, Lior Drukker, Aris T. Papageorghiou, J. Alison Noble
http://arxiv.org/abs/1903.02974v1
• [cs.CV]Understanding Ancient Coin Images
Jessica Cooper, Ognjen Arandjelovic
http://arxiv.org/abs/1903.02665v1
• [cs.CV]Using DP Towards A Shortest Path Problem-Related Application
Jianhao Jiao, Rui Fan, Han Ma, Ming Liu
http://arxiv.org/abs/1903.02765v1
• [cs.CV]Weakly Supervised Complementary Parts Models for Fine-Grained Image Classification from the Bottom Up
Weifeng Ge, Xiangru Lin, Yizhou Yu
http://arxiv.org/abs/1903.02827v1
• [cs.CY]Engaging Users with Educational Games: The Case of Phishing
Matt Dixon, Nalin Asanka Gamagedara Arachchilage, James Nicholson
http://arxiv.org/abs/1903.03019v1
• [cs.CY]Seniors' Media Preference for Receiving Internet Security Information: A Pilot Study
Yousra Javed, Boyd Davis, Mohamed Shehab
http://arxiv.org/abs/1903.02618v1
• [cs.DC]An Introduction to hpxMP -- A Modern OpenMP Implementation Leveraging Asynchronous Many-Tasking System
Tianyi Zhang, Shahrzad Shirzad, Patrick Diehl, R. Tohid, Weile Wei, Hartmut Kaiser
http://arxiv.org/abs/1903.03023v1
• [cs.DC]Invariant Safety for Distributed Applications
Sreeja Nair, Gustavo Petri, Marc Shapiro
http://arxiv.org/abs/1903.02759v1
• [cs.DC]Neighborhood Mutual Remainder: Self-Stabilizing Implementation of Look-Compute-Move Robots (Extended Abstract)
Shlomi Dolev, Sayaka Kamei, Yoshiaki Katayama, Fukuhito Ooshita, Koichi Wada
http://arxiv.org/abs/1903.02843v1
• [cs.DC]Towards a Uniform Architecture for the Efficient Implementation of 2D and 3D Deconvolutional Neural Networks on FPGAs
Deguang Wang, Junzhong Shen, Mei Wen, Chunyuan Zhang
http://arxiv.org/abs/1903.02550v1
• [cs.GT]Selling Multiple Items via Social Networks
Dengji Zhao, Bin Li, Junping Xu, Dong Hao, Nicholas R. Jennings
http://arxiv.org/abs/1903.02703v1
• [cs.HC]Integrating Artificial and Human Intelligence for Efficient Translation
Nico Herbig, Santanu Pal, Josef van Genabith, Antonio Krüger
http://arxiv.org/abs/1903.02978v1
• [cs.IR]ViTOR: Learning to Rank Webpages Based on Visual Features
Bram van den Akker, Ilya Markov, Maarten de Rijke
http://arxiv.org/abs/1903.02939v1
• [cs.IT]A General Construction and Encoder Implementation of Polar Codes
Wei Song, Yifei Shen, Liping Li, Kai Niu, Chuan Zhang
http://arxiv.org/abs/1903.02899v1
• [cs.IT]A Scalable Max-Consensus Protocol For Noisy Ultra-Dense Networks
Navneet Agrawal, Matthias Frey, Slawomir Stanczak
http://arxiv.org/abs/1903.02885v1
• [cs.IT]Deep Learning based End-to-End Wireless Communication Systems with Conditional GAN as Unknown Channel
Hao Ye, Le Liang, Geoffrey Ye Li, Biing-Hwang Fred Juang
http://arxiv.org/abs/1903.02551v1
• [cs.IT]Deep Learning for Channel Coding via Neural Mutual Information Estimation
Rick Fritschek, Rafael F. Schaefer, Gerhard Wunder
http://arxiv.org/abs/1903.02865v1
• [cs.IT]Deep Learning for UL/DL Channel Calibration in Generic Massive MIMO Systems
Chongwen Huang, George C. Alexandropoulos, Alessio Zappone, Chau Yuen, Mérouane Debbah
http://arxiv.org/abs/1903.02875v1
• [cs.IT]IoT-U: Cellular Internet-of-Things Networks over Unlicensed Spectrum
Hongliang Zhang, Boya Di, Kaigui Bian, Lingyang Song
http://arxiv.org/abs/1903.02686v1
• [cs.IT]Joint Dynamic Pricing and Radio Resource Allocation Framework for IoT Services
Mohammad Moltafet, Atefeh Rezaei, Nader Mokari, Mohammad Reza Javan, Hamid Saeedi, Hossein Pishro Nik
http://arxiv.org/abs/1903.02928v1
• [cs.IT]Non-Stationarities in Extra-Large Scale Massive MIMO
Elisabeth De Carvalho, Anum Ali, Abolfazl Amiri, Marko Angjelichinoski, Robert W. Heath Jr
http://arxiv.org/abs/1903.03085v1
• [cs.IT]On the security of a Loidreau's rank metric code based encryption scheme
Daniel Coggia, Alain Couvreur
http://arxiv.org/abs/1903.02933v1
• [cs.IT]Rigorous Analysis of Spectral Methods for Random Orthogonal Matrices
Rishabh Dudeja, Milad Bakhshizadeh, Junjie Ma, Arian Maleki
http://arxiv.org/abs/1903.02676v1
• [cs.LG]A Rank-1 Sketch for Matrix Multiplicative Weights
Yair Carmon, John C. Duchi, Aaron Sidford, Kevin Tian
http://arxiv.org/abs/1903.02675v1
• [cs.LG]Accurate inference of crowdsourcing properties when using efficient allocation strategies
Abigail Hotaling, James P. Bagrow
http://arxiv.org/abs/1903.03104v1
• [cs.LG]Analysis Dictionary Learning: An Efficient and Discriminative Solution
Wen Tang, Ashkan Panahi, Hamid Krim, Liyi Dai
http://arxiv.org/abs/1903.03058v1
• [cs.LG]Doubly Aligned Incomplete Multi-view Clustering
Menglei Hu, Songcan Chen
http://arxiv.org/abs/1903.02785v1
• [cs.LG]Efficient and Effective Quantization for Sparse DNNs
Yiren Zhao, Xitong Gao, Daniel Bates, Robert Mullins, Cheng-Zhong Xu
http://arxiv.org/abs/1903.03046v1
• [cs.LG]Fast Graph Representation Learning with PyTorch Geometric
Matthias Fey, Jan Eric Lenssen
http://arxiv.org/abs/1903.02428v2
• [cs.LG]Fast Parallel Algorithms for Feature Selection
Sharon Qian, Yaron Singer
http://arxiv.org/abs/1903.02656v1
• [cs.LG]GanDef: A GAN based Adversarial Training Defense for Neural Network Classifier
Guanxiong Liu, Issa Khalil, Abdallah Khreishah
http://arxiv.org/abs/1903.02585v1
• [cs.LG]Generative Graph Convolutional Network for Growing Graphs
Da Xu, Chuanwei Ruan, Kamiya Motwani, Evren Korpeoglu, Sushant Kumar, Kannan Achan
http://arxiv.org/abs/1903.02640v1
• [cs.LG]Improving Generalization of Deep Networks for Inverse Reconstruction of Image Sequences
Sandesh Ghimire, Prashnna Kumar Gyawali, Jwala Dhamala, John L Sapp, Milan Horacek, Linwei Wang
http://arxiv.org/abs/1903.02948v1
• [cs.LG]Interpretable Deep Learning in Drug Discovery
Kristina Preuer, Günter Klambauer, Friedrich Rippmann, Sepp Hochreiter, Thomas Unterthiner
http://arxiv.org/abs/1903.02788v1
• [cs.LG]Limiting Network Size within Finite Bounds for Optimization
Linu Pinto, Dr. Sasi Gopalan
http://arxiv.org/abs/1903.02809v1
• [cs.LG]Mean-field Analysis of Batch Normalization
Mingwei Wei, James Stokes, David J Schwab
http://arxiv.org/abs/1903.02606v1
• [cs.LG]Meta-Dataset: A Dataset of Datasets for Learning to Learn from Few Examples
Eleni Triantafillou, Tyler Zhu, Vincent Dumoulin, Pascal Lamblin, Kelvin Xu, Ross Goroshin, Carles Gelada, Kevin Swersky, Pierre-Antoine Manzagol, Hugo Larochelle
http://arxiv.org/abs/1903.03096v1
• [cs.LG]Only sparsity based loss function for learning representations
Vivek Bakaraju, Kishore Reddy Konda
http://arxiv.org/abs/1903.02893v1
• [cs.LG]Out-domain examples for generative models
Dario Pasquini, Marco Mingione, Massimo Bernaschi
http://arxiv.org/abs/1903.02926v1
• [cs.LG]Quantum Latent Semantic Analysis
Fabio A. González, Juan C. Caicedo
http://arxiv.org/abs/1903.03082v1
• [cs.LG]RLOC: Neurobiologically Inspired Hierarchical Reinforcement Learning Algorithm for Continuous Control of Nonlinear Dynamical Systems
Ekaterina Abramova, Luke Dickens, Daniel Kuhn, Aldo Faisal
http://arxiv.org/abs/1903.03064v1
• [cs.LG]Robust and Communication-Efficient Federated Learning from Non-IID Data
Felix Sattler, Simon Wiedemann, Klaus-Robert Müller, Wojciech Samek
http://arxiv.org/abs/1903.02891v1
• [cs.LG]Self-Tuning Networks: Bilevel Optimization of Hyperparameters using Structured Best-Response Functions
Matthew MacKay, Paul Vicol, Jon Lorraine, David Duvenaud, Roger Grosse
http://arxiv.org/abs/1903.03088v1
• [cs.LG]The Privacy Blanket of the Shuffle Model
Borja Balle, James Bell, Adria Gascon, Kobbi Nissim
http://arxiv.org/abs/1903.02837v1
• [cs.LG]The Variational Predictive Natural Gradient
Da Tang, Rajesh Ranganath
http://arxiv.org/abs/1903.02984v1
• [cs.LG]Using World Models for Pseudo-Rehearsal in Continual Learning
Nicholas Ketz, Soheil Kolouri, Praveen Pilly
http://arxiv.org/abs/1903.02647v1
• [cs.LG]When random search is not enough: Sample-Efficient and Noise-Robust Blackbox Optimization of RL Policies
Krzysztof Choromanski, Aldo Pacchiano, Jack Parker-Holder, Jasmine Hsu, Atil Iscen, Deepali Jain, Vikas Sindhwani
http://arxiv.org/abs/1903.02993v1
• [cs.MA]Convergence of Multi-Agent Learning with a Finite Step Size in General-Sum Games
Xinliang Song, Tonghan Wang, Chongjie Zhang
http://arxiv.org/abs/1903.02868v1
• [cs.NE]A Novel Neural Network Structure Constructed according to Logical Relations
Wang Gang
http://arxiv.org/abs/1903.02683v1
• [cs.NE]jMetalPy: a Python Framework for Multi-Objective Optimization with Metaheuristics
Antonio Benitez-Hidalgo, Antonio J. Nebro, Jose Garcia-Nieto, Izaskun Oregi, Javier Del Ser
http://arxiv.org/abs/1903.02915v1
• [cs.NI]Allocation of Computation-Intensive Graph Jobs over Vehicular Clouds
Minghui LiWang, Seyyedali Hosseinalipour, Zhibin Gao, Yuliang Tang, Lianfen Huang, Huaiyu Dai
http://arxiv.org/abs/1903.02724v1
• [cs.RO]A Lane-Change Path Planner and its application with a monocular camera
Yunlong Huang
http://arxiv.org/abs/1903.02552v1
• [cs.RO]An Inverting-Tube Clutching Contractile Soft Pneumatic Actuator
Wyatt Felt
http://arxiv.org/abs/1903.02725v1
• [cs.RO]Deep Visual MPC-Policy Learning for Navigation
Noriaki Hirose, Fei Xia, Roberto Martin-Martin, Amir Sadeghian, Silvio Savarese
http://arxiv.org/abs/1903.02749v1
• [cs.RO]Locating Transparent Objects to Millimetre Accuracy
Nicholas Adrian, Quang-Cuong Pham
http://arxiv.org/abs/1903.02908v1
• [cs.RO]Robust Execution of Contact-Rich Motion Plans by Hybrid Force-Velocity Control
Yifan Hou, Matthew T. Mason
http://arxiv.org/abs/1903.02715v1
• [cs.RO]Towards Better Human Robot Collaboration with Robust Plan Recognition and Trajectory Prediction
Yujiao Cheng, Liting Sun, Masayoshi Tomizuka
http://arxiv.org/abs/1903.02199v2
• [cs.SI]HEAT: Hyperbolic Embedding of Attributed Networks
David McDonald, Shan He
http://arxiv.org/abs/1903.03036v1
• [cs.SI]Learning Graphs from Noisy Epidemic Cascades
Jessica Hoffmann, Constantine Caramanis
http://arxiv.org/abs/1903.02650v1
• [cs.SI]Structure-Preserving Community In A Multilayer Network: Definition, Detection, And Analysis
Abhishek Santra, Kanthi Sannappa Komar, Sanjukta Bhowmick, Sharma Chakravarthy
http://arxiv.org/abs/1903.02641v1
• [cs.SI]Twitter Speaks: A Case of National Disaster Situational Awareness
Amir Karami, Vishal Shah, Reza Vaezi, Amit Bansal
http://arxiv.org/abs/1903.02706v1
• [eess.SP]Scattering Mechanisms and Modeling for Terahertz Wireless Communications
Shihao Ju, Syed Hashim Ali Shah, Muhammad Affan Javed, Jun Li, Girish Palteru, Jyotish Robin, Yunchou Xing, Ojas Kanhere, Theodore S. Rappaport
http://arxiv.org/abs/1903.02657v1
• [math.RT]Reparameterizing Distributions on Lie Groups
Luca Falorsi, Pim de Haan, Tim R. Davidson, Patrick Forré
http://arxiv.org/abs/1903.02958v1
• [math.ST]Integral Transform Methods in Goodness-of-Fit Testing, II: The Wishart Distributions
Elena Hadjicosta, Donald Richards
http://arxiv.org/abs/1903.02653v1
• [math.ST]Nonparametric Change Point Detection in Regression
Valeriy Avanesov
http://arxiv.org/abs/1903.02603v1
• [math.ST]Solutions to Sparse Multilevel Matrix Problems
Tui H. Nolan, Matt P. Wand
http://arxiv.org/abs/1903.03089v1
• [math.ST]Tutorial: Deriving The Efficient Influence Curve for Large Models
Jonathan Levy
http://arxiv.org/abs/1903.01706v2
• [physics.comp-ph]Deep learning observables in computational fluid dynamics
Kjetil O. Lye, Siddhartha Mishra, Deep Ray
http://arxiv.org/abs/1903.03040v1
• [physics.data-an]Accurate reconstruction of EBSD datasets by a multimodal data approach using an evolutionary algorithm
Marie-Agathe Charpagne, Florian Strub, Tresa M. Pollocka
http://arxiv.org/abs/1903.02988v1
• [physics.soc-ph]Trip Centrality: walking on a temporal multiplex with non-instantaneous link travel time
Silvia Zaoli, Piero Mazzarisi, Fabrizio Lillo
http://arxiv.org/abs/1903.02815v1
• [q-bio.TO]Characterization of Posidonia Oceanica Seagrass Aerenchyma through Whole Slide Imaging: A Pilot Study
Olivier Debeir, Justine Allard, Christine Decaestecker, Jean-Pierre Hermand
http://arxiv.org/abs/1903.03044v1
• [q-fin.CP]Learning the population dynamics of technical trading strategies
Nicholas Murphy, Tim Gebbie
http://arxiv.org/abs/1903.02228v1
• [quant-ph]Quantum hardness of learning shallow classical circuits
Srinivasan Arunachalam, Alex B. Grilo, Aarthi Sundaram
http://arxiv.org/abs/1903.02840v1
• [stat.AP]Estimating a pressure dependent thermal conductivity coefficient with applications in food technology
Marcos A Capistran, Juan Antonio Infante del Rio
http://arxiv.org/abs/1903.02830v1
• [stat.AP]Semi-Supervised Non-Parametric Bayesian Modelling of Spatial Proteomics
Oliver M. Crook, Kathryn S. Lilley, Laurent Gatto, Paul D. W. Kirk
http://arxiv.org/abs/1903.02909v1
• [stat.CO]Estimation and uncertainty quantification for the output from quantum simulators
Ryan Bennink, Ajay Jasra, Kody J. H. Law, Pavel Lougovski
http://arxiv.org/abs/1903.02964v1
• [stat.ME]A comment on "New non-parametric inferences for low-income proportions" by Shan Luo and Gengsheng Qin
Wojciech Zieliński
http://arxiv.org/abs/1903.02973v1
• [stat.ME]Relaxing the Assumptions of Knockoffs by Conditioning
Dongming Huang, Lucas Janson
http://arxiv.org/abs/1903.02806v1
• [stat.ME]Simultaneous Prediction Intervals for Small Area Parameter
Katarzyna Reluga, María José Lombardía, Stefan Andreas Sperlich
http://arxiv.org/abs/1903.02774v1
• [stat.ML]Adversarial Mixup Resynthesizers
Christopher Beckham, Sina Honari, Alex Lamb, Vikas Verma, Farnoosh Ghadiri, R Devon Hjelm, Christopher Pal
http://arxiv.org/abs/1903.02709v1
• [stat.ML]Deep Random Splines for Point Process Intensity Estimation
Gabriel Loaiza-Ganem, John P. Cunningham
http://arxiv.org/abs/1903.02610v1
• [stat.ML]GRATIS: GeneRAting TIme Series with diverse and controllable characteristics
Yanfei Kang, Rob J Hyndman, Feng Li
http://arxiv.org/abs/1903.02787v1
• [stat.ML]Multi-output Bus Travel Time Prediction with Convolutional LSTM Neural Network
Niklas Christoffer Petersen, Filipe Rodrigues, Francisco Camara Pereira
http://arxiv.org/abs/1903.02791v1
• [stat.ML]On Convergence Rate of the Gaussian Belief Propagation Algorithm for Markov Networks
Zhaorong Zhang, Minyue Fu
http://arxiv.org/abs/1903.02658v1
• [stat.ML]On Transformations in Stochastic Gradient MCMC
Soma Yokoi, Takuma Otsuka, Issei Sato
http://arxiv.org/abs/1903.02750v1
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