cond-mat.mtrl-sci - 材料科学
cs.AI - 人工智能
cs.CG - 计算几何学
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.DB - 数据库
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.MA - 多代理系统
cs.NE - 神经与进化计算
cs.RO - 机器人学
cs.SD - 声音处理
cs.SI - 社交网络与信息网络
eess.AS - 语音处理
math.OC - 优化与控制
math.ST - 统计理论
q-bio.NC - 神经元与认知
quant-ph - 量子物理
stat.ME - 统计方法论
stat.ML - (统计)机器学习
• [cond-mat.mtrl-sci]Graph Dynamical Networks: Unsupervised Learning of Atomic Scale Dynamics in Materials
• [cond-mat.mtrl-sci]Reconstruction of 3-D Atomic Distortions from Electron Microscopy with Deep Learning
• [cs.AI]Emergent Coordination Through Competition
• [cs.AI]Parenting: Safe Reinforcement Learning from Human Input
• [cs.AI]Towards the Next Generation Airline Revenue Management: A Deep Reinforcement Learning Approach to Seat Inventory Control and Overbooking
• [cs.CG]Approximating Continuous Functions on Persistence Diagrams Using Template Functions
• [cs.CL]A Walk-based Model on Entity Graphs for Relation Extraction
• [cs.CL]A novel repetition normalized adversarial reward for headline generation
• [cs.CL]Author Profiling for Hate Speech Detection
• [cs.CL]Classifying textual data: shallow, deep and ensemble methods
• [cs.CL]Contextual Word Representations: A Contextual Introduction
• [cs.CL]Learned In Speech Recognition: Contextual Acoustic Word Embeddings
• [cs.CV]Accurate Segmentation of Dermoscopic Images based on Local Binary Pattern Clustering
• [cs.CV]Air Quality Measurement Based on Double-Channel Convolutional Neural Network Ensemble Learning
• [cs.CV]Anomaly Detection with Adversarial Dual Autoencoders
• [cs.CV]Challenging Environments for Traffic Sign Detection: Reliability Assessment under Inclement Conditions
• [cs.CV]Commodity RGB-D Sensors: Data Acquisition
• [cs.CV]Democratisation of Usable Machine Learning in Computer Vision
• [cs.CV]Detector-in-Detector: Multi-Level Analysis for Human-Parts
• [cs.CV]Directional Regularized Tensor Modeling for Video Rain Streaks Removal
• [cs.CV]Evaluating the Effectiveness of Automated Identity Masking (AIM) Methods with Human Perception
• [cs.CV]FreeLabel: A Publicly Available Annotation Tool based on Freehand Traces
• [cs.CV]Geometry of Deep Generative Models for Disentangled Representations
• [cs.CV]HybridSN: Exploring 3D-2D CNN Feature Hierarchy for Hyperspectral Image Classification
• [cs.CV]LocalNorm: Robust Image Classification through Dynamically Regularized Normalization
• [cs.CV]Motion Equivariant Networks for Event Cameras with the Temporal Normalization Transform
• [cs.CV]Predicting city safety perception based on visual image content
• [cs.CV]Predicting tongue motion in unlabeled ultrasound videos using convolutional LSTM neural network
• [cs.CV]SC-FEGAN: Face Editing Generative Adversarial Network with User's Sketch and Color
• [cs.CV]Using Conditional Generative Adversarial Networks to Generate Ground-Level Views From Overhead Imagery
• [cs.CV]Variational Regularized Transmission Refinement for Image Dehazing
• [cs.CV]WIDER Face and Pedestrian Challenge 2018: Methods and Results
• [cs.CY]Digital Humanities Readiness Assessment Framework: DHuRAF
• [cs.CY]Forecasting the 2017-2018 Yemen Cholera Outbreak with Machine Learning
• [cs.CY]Fusing Visual, Textual and Connectivity Clues for Studying Mental Health
• [cs.CY]Using Crowdsourcing to Identify a Proxy of Socio-Economic status
• [cs.CY]Using Machine Learning to Guide Cognitive Modeling: A Case Study in Moral Reasoning
• [cs.DB]Comparing Apples and Oranges: Measuring Differences between Data Mining Results
• [cs.DB]Finding Robust Itemsets Under Subsampling
• [cs.DC]A Generalised Solution to Distributed Consensus
• [cs.DC]Graph Computing based Distributed Fast Decoupled Power Flow Analysis
• [cs.DC]How much does randomness help with locally checkable problems?
• [cs.DC]Layering Data Structures over Skip Graphs for Increased NUMA Locality
• [cs.DC]Optimizing Network Performance for Distributed DNN Training on GPU Clusters: ImageNet/AlexNet Training in 1.5 Minutes
• [cs.DC]Towards a Scaled IoT Pub/Sub Architecture for 5G Networks: the Case of Multiaccess Edge Computing
• [cs.DC]Who started this rumor? Quantifying the natural differential privacy guarantees of gossip protocols
• [cs.DC]Zest: REST over ZeroMQ
• [cs.DS]Hardness of exact distance queries in sparse graphs through hub labeling
• [cs.GT]Bayesian Exploration with Heterogeneous Agents
• [cs.HC]Cybercrime Investigators are Users Too! Understanding the Socio-Technical Challenges Faced by Law Enforcement
• [cs.IR]Collaborative Similarity Embedding for Recommender Systems
• [cs.IT]Channel Extrapolation in FDD Massive MIMO: Theoretical Analysis and Numerical Validation
• [cs.IT]Distributed Learning for Channel Allocation Over a Shared Spectrum
• [cs.IT]Downlink NOMA in Multi-UAV Networks over Bivariate Rician Shadowed Fading Channels
• [cs.IT]Effective Capacity and Power Allocation for Machine-Type Communication
• [cs.IT]Few-Bit CSI Acquisition for Centralized Cell-Free Massive MIMO with Spatial Correlation
• [cs.IT]Jamming Suppression in Massive MIMO Systems
• [cs.IT]Multi-Antenna Covert Communications in Random Wireless Networks
• [cs.IT]Towards Hardware Implementation of Neural Network-based Communication Algorithms
• [cs.IT]Wireless Key Generation from Imperfect Channel State Information: Performance Analysis and Improvements
• [cs.LG]Adaptive Cross-Modal Few-Shot Learning
• [cs.LG]An entropic feature selection method in perspective of Turing formula
• [cs.LG]DEDPUL: Method for Mixture Proportion Estimation and Positive-Unlabeled Classification based on Density Estimation
• [cs.LG]Data augmentation for low resource sentiment analysis using generative adversarial networks
• [cs.LG]Deep Learning Based Autoencoder for Interference Channel
• [cs.LG]Evaluating model calibration in classification
• [cs.LG]Explaining a black-box using Deep Variational Information Bottleneck Approach
• [cs.LG]Fast Compressive Sensing Recovery Using Generative Models with Structured Latent Variables
• [cs.LG]Global Convergence of Adaptive Gradient Methods for An Over-parameterized Neural Network
• [cs.LG]Identifying nonlinear dynamical systems via generative recurrent neural networks with applications to fMRI
• [cs.LG]Investigating Generalisation in Continuous Deep Reinforcement Learning
• [cs.LG]Label-Removed Generative Adversarial Networks Incorporating with K-Means
• [cs.LG]Learning Task Agnostic Sufficiently Accurate Models
• [cs.LG]Learning to Generalize from Sparse and Underspecified Rewards
• [cs.LG]Low-bit Quantization of Neural Networks for Efficient Inference
• [cs.LG]Measuring Compositionality in Representation Learning
• [cs.LG]Nutrition and Health Data for Cost-Sensitive Learning
• [cs.LG]On the Convergence of EM for truncated mixtures of two Gaussians
• [cs.LG]Proper-Composite Loss Functions in Arbitrary Dimensions
• [cs.LG]Recovery of a mixture of Gaussians by sum-of-norms clustering
• [cs.LG]Regularizing Black-box Models for Improved Interpretability
• [cs.LG]Seven Myths in Machine Learning Research
• [cs.LG]Simplifying Graph Convolutional Networks
• [cs.LG]There are No Bit Parts for Sign Bits in Black-Box Attacks
• [cs.MA]On Voting Strategies and Emergent Communication
• [cs.NE]Evolutionary Neural AutoML for Deep Learning
• [cs.RO]2D LiDAR Map Prediction via Estimating Motion Flow with GRU
• [cs.RO]A Soft High Force Hand Exoskeleton for Rehabilitation and Assistance of Spinal Cord Injury and Stroke Individuals
• [cs.RO]Design and Control of a Quasi-Direct Drive Soft Hybrid Knee Exoskeleton for Injury Prevention during Squatting
• [cs.RO]Efficient Obstacle Rearrangement for Object Manipulation Tasks in Cluttered Environments
• [cs.RO]Improving dual-arm assembly by master-slave compliance
• [cs.RO]Multi-view Incremental Segmentation of 3D Point Clouds for Mobile Robots
• [cs.RO]Nonlinear Model Predictive Control for Robust Bipedal Locomotion Exploring CoM Height and Angular Momentum Changes
• [cs.RO]Virtual Border Teaching Using a Network Robot System
• [cs.SD]End-to-end Lyrics Alignment for Polyphonic Music Using an Audio-to-Character Recognition Model
• [cs.SI]Do zealots increase or decrease the polarization in social networks?
• [cs.SI]Estimating Network Effects Using Naturally Occurring Peer Notification Queue Counterfactuals
• [eess.AS]A spelling correction model for end-to-end speech recognition
• [math.OC]2-Wasserstein Approximation via Restricted Convex Potentials with Application to Improved Training for GANs
• [math.ST]Asymptotic Theory of Eigenvectors for Large Random Matrices
• [math.ST]Efficiency requires innovation
• [math.ST]New statistical methodology for second level global sensitivity analysis
• [math.ST]Penultimate Analysis of the Conditional Multivariate Extremes Tail Model
• [math.ST]Statistical inference for a partially observed interacting system of Hawkes processes
• [math.ST]The KLR-theorem revisited
• [math.ST]Universality of Computational Lower Bounds for Submatrix Detection
• [q-bio.NC]A computational model for grid maps in neural populations
• [q-bio.NC]Large-scale directed network inference with multivariate transfer entropy and hierarchical statistical testing
• [quant-ph]Probabilistic Modeling with Matrix Product States
• [stat.ME]A primer on statistically validated networks
• [stat.ME]Employing latent variable models to improve efficiency in composite endpoint analysis
• [stat.ME]Exact Kalman Filter for Binary Time Series
• [stat.ME]Penalized basis models for very large spatial datasets
• [stat.ME]Simulation study of estimating between-study variance and overall effect in meta-analysis of odds-ratios
• [stat.ML]Hyperbolic Discounting and Learning over Multiple Horizons
• [stat.ML]Multifidelity Bayesian Optimization for Binomial Output
• [stat.ML]On the Impact of the Activation Function on Deep Neural Networks Training
• [stat.ML]On the consistency of supervised learning with missing values
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• [cond-mat.mtrl-sci]Graph Dynamical Networks: Unsupervised Learning of Atomic Scale Dynamics in Materials
Tian Xie, Arthur France-Lanord, Yanming Wang, Yang Shao-Horn, Jeffrey C. Grossman
http://arxiv.org/abs/1902.06836v1
• [cond-mat.mtrl-sci]Reconstruction of 3-D Atomic Distortions from Electron Microscopy with Deep Learning
Nouamane Laanait, Qian He, Albina Y. Borisevich
http://arxiv.org/abs/1902.06876v1
• [cs.AI]Emergent Coordination Through Competition
Siqi Liu, Guy Lever, Josh Merel, Saran Tunyasuvunakool, Nicolas Heess, Thore Graepel
http://arxiv.org/abs/1902.07151v1
• [cs.AI]Parenting: Safe Reinforcement Learning from Human Input
Christopher Frye, Ilya Feige
http://arxiv.org/abs/1902.06766v1
• [cs.AI]Towards the Next Generation Airline Revenue Management: A Deep Reinforcement Learning Approach to Seat Inventory Control and Overbooking
Syed Arbab Mohd Shihab, Caleb Logemann, Deepak-George Thomas, Peng Wei
http://arxiv.org/abs/1902.06824v1
• [cs.CG]Approximating Continuous Functions on Persistence Diagrams Using Template Functions
Jose A. Perea, Elizabeth Munch, Firas A. Khasawneh
http://arxiv.org/abs/1902.07190v1
• [cs.CL]A Walk-based Model on Entity Graphs for Relation Extraction
Fenia Christopoulou, Makoto Miwa, Sophia Ananiadou
http://arxiv.org/abs/1902.07023v1
• [cs.CL]A novel repetition normalized adversarial reward for headline generation
Peng Xu, Pascale Fung
http://arxiv.org/abs/1902.07110v1
• [cs.CL]Author Profiling for Hate Speech Detection
Pushkar Mishra, Marco Del Tredici, Helen Yannakoudakis, Ekaterina Shutova
http://arxiv.org/abs/1902.06734v1
• [cs.CL]Classifying textual data: shallow, deep and ensemble methods
Laura Anderlucci, Lucia Guastadisegni, Cinzia Viroli
http://arxiv.org/abs/1902.07068v1
• [cs.CL]Contextual Word Representations: A Contextual Introduction
Noah A. Smith
http://arxiv.org/abs/1902.06006v2
• [cs.CL]Learned In Speech Recognition: Contextual Acoustic Word Embeddings
Shruti Palaskar, Vikas Raunak, Florian Metze
http://arxiv.org/abs/1902.06833v1
• [cs.CV]Accurate Segmentation of Dermoscopic Images based on Local Binary Pattern Clustering
Pedro M. M. Pereira, Rui Fonseca-Pinto, Rui Pedro Paiva, Luis M. N. Tavora, Pedro A. A. Assuncao, Sergio M. M. de Faria
http://arxiv.org/abs/1902.06347v2
• [cs.CV]Air Quality Measurement Based on Double-Channel Convolutional Neural Network Ensemble Learning
Zhenyu Wang, Wei Zheng, Chunfeng Song
http://arxiv.org/abs/1902.06942v1
• [cs.CV]Anomaly Detection with Adversarial Dual Autoencoders
Ha Son Vu, Daisuke Ueta, Kiyoshi Hashimoto, Kazuki Maeno, Sugiri Pranata, Sheng Mei Shen
http://arxiv.org/abs/1902.06924v1
• [cs.CV]Challenging Environments for Traffic Sign Detection: Reliability Assessment under Inclement Conditions
Dogancan Temel, Tariq Alshawi, Min-Hung Chen, Ghassan AlRegib
http://arxiv.org/abs/1902.06857v1
• [cs.CV]Commodity RGB-D Sensors: Data Acquisition
Michael Zollhöfer
http://arxiv.org/abs/1902.06835v1
• [cs.CV]Democratisation of Usable Machine Learning in Computer Vision
Raymond Bond, Ansgar Koene, Alan Dix, Jennifer Boger, Maurice D. Mulvenna, Mykola Galushka, Bethany Waterhouse Bradley, Fiona Browne, Hui Wang, Alexander Wong
http://arxiv.org/abs/1902.06804v1
• [cs.CV]Detector-in-Detector: Multi-Level Analysis for Human-Parts
Xiaojie Li, Lu Yang, Qing Song, Fuqiang Zhou
http://arxiv.org/abs/1902.07017v1
• [cs.CV]Directional Regularized Tensor Modeling for Video Rain Streaks Removal
Zhaoyang Sun, Shengwu Xiong, Ryan Wen Liu
http://arxiv.org/abs/1902.07090v1
• [cs.CV]Evaluating the Effectiveness of Automated Identity Masking (AIM) Methods with Human Perception
Kimberley D. Orsten-Hooge, Asal Baragchizadeh, Thomas P. Karnowski, David S. Bolme, Regina Ferrell, Parisa R. Jesudasen, Alice J. O'Toole
http://arxiv.org/abs/1902.06967v1
• [cs.CV]FreeLabel: A Publicly Available Annotation Tool based on Freehand Traces
Philipe A. Dias, Zhou Shen, Amy Tabb, Henry Medeiros
http://arxiv.org/abs/1902.06806v1
• [cs.CV]Geometry of Deep Generative Models for Disentangled Representations
Ankita Shukla, Shagun Uppal, Sarthak Bhagat, Saket Anand, Pavan Turaga
http://arxiv.org/abs/1902.06964v1
• [cs.CV]HybridSN: Exploring 3D-2D CNN Feature Hierarchy for Hyperspectral Image Classification
Swalpa Kumar Roy, Gopal Krishna, Shiv Ram Dubey, Bidyut B. Chaudhuri
http://arxiv.org/abs/1902.06701v2
• [cs.CV]LocalNorm: Robust Image Classification through Dynamically Regularized Normalization
Bojian Yin, Siebren Schaafsma, Henk Corporaal, H. Steven Scholte, Sander M. Bohte
http://arxiv.org/abs/1902.06550v2
• [cs.CV]Motion Equivariant Networks for Event Cameras with the Temporal Normalization Transform
Alex Zihao Zhu, Ziyun Wang, Kostas Daniilidis
http://arxiv.org/abs/1902.06820v1
• [cs.CV]Predicting city safety perception based on visual image content
Sergio Acosta, Jorge E. Camargo
http://arxiv.org/abs/1902.06871v1
• [cs.CV]Predicting tongue motion in unlabeled ultrasound videos using convolutional LSTM neural network
Chaojie Zhao, Peng Zhang, Jian Zhu, Chengrui Wu, Huaimin Wang, Kele Xu
http://arxiv.org/abs/1902.06927v1
• [cs.CV]SC-FEGAN: Face Editing Generative Adversarial Network with User's Sketch and Color
Youngjoo Jo, Jongyoul Park
http://arxiv.org/abs/1902.06838v1
• [cs.CV]Using Conditional Generative Adversarial Networks to Generate Ground-Level Views From Overhead Imagery
Xueqing Deng, Yi Zhu, Shawn Newsam
http://arxiv.org/abs/1902.06923v1
• [cs.CV]Variational Regularized Transmission Refinement for Image Dehazing
Qiaoling Shu, Chuansheng Wu, Zhe Xiao, Ryan Wen Liu
http://arxiv.org/abs/1902.07069v1
• [cs.CV]WIDER Face and Pedestrian Challenge 2018: Methods and Results
Chen Change Loy, Dahua Lin, Wanli Ouyang, Yuanjun Xiong, Shuo Yang, Qingqiu Huang, Dongzhan Zhou, Wei Xia, Quanquan Li, Ping Luo, Junjie Yan, Jianfeng Wang, Zuoxin Li, Ye Yuan, Boxun Li, Shuai Shao, Gang Yu, Fangyun Wei, Xiang Ming, Dong Chen, Shifeng Zhang, Cheng Chi, Zhen Lei, Stan Z. Li, Hongkai Zhang, Bingpeng Ma, Hong Chang, Shiguang Shan, Xilin Chen, Wu Liu, Boyan Zhou, Huaxiong Li, Peng Cheng, Tao Mei, Artem Kukharenko, Artem Vasenin, Nikolay Sergievskiy, Hua Yang, Liangqi Li, Qiling Xu, Yuan Hong, Lin Chen, Mingjun Sun, Yirong Mao, Shiying Luo, Yongjun Li, Ruiping Wang, Qiaokang Xie, Ziyang Wu, Lei Lu, Yiheng Liu, Wengang Zhou
http://arxiv.org/abs/1902.06854v1
• [cs.CY]Digital Humanities Readiness Assessment Framework: DHuRAF
Hossein Hassani, Emir Turajlić, Kemal Taljanović
http://arxiv.org/abs/1902.06532v2
• [cs.CY]Forecasting the 2017-2018 Yemen Cholera Outbreak with Machine Learning
Rohil Badkundri, Victor Valbuena, Srikusmanjali Pinnamareddy, Brittney Cantrell, Janet Standeven
http://arxiv.org/abs/1902.06739v1
• [cs.CY]Fusing Visual, Textual and Connectivity Clues for Studying Mental Health
Amir Hossein Yazdavar, Mohammad Saeid Mahdavinejad, Goonmeet Bajaj, William Romine, Amirhassan Monadjemi, Krishnaprasad Thirunarayan, Amit Sheth, Jyotishman Pathak
http://arxiv.org/abs/1902.06843v1
• [cs.CY]Using Crowdsourcing to Identify a Proxy of Socio-Economic status
Adil E. Rajput, Akila Sarirete, Tamer F. Desouky
http://arxiv.org/abs/1902.06914v1
• [cs.CY]Using Machine Learning to Guide Cognitive Modeling: A Case Study in Moral Reasoning
Mayank Agrawal, Joshua C. Peterson, Thomas L. Griffiths
http://arxiv.org/abs/1902.06744v1
• [cs.DB]Comparing Apples and Oranges: Measuring Differences between Data Mining Results
Nikolaj Tatti, Jilles Vreeken
http://arxiv.org/abs/1902.07165v1
• [cs.DB]Finding Robust Itemsets Under Subsampling
Nikolaj Tatti, Fabian Moerchen
http://arxiv.org/abs/1902.06743v1
• [cs.DC]A Generalised Solution to Distributed Consensus
Heidi Howard, Richard Mortier
http://arxiv.org/abs/1902.06776v1
• [cs.DC]Graph Computing based Distributed Fast Decoupled Power Flow Analysis
Chen Yuan, Yi Lu, Wei Feng, Guangyi Liu, Renchang Dai, Yachen Tang, Zhiwei Wang
http://arxiv.org/abs/1902.06893v1
• [cs.DC]How much does randomness help with locally checkable problems?
Alkida Balliu, Sebastian Brandt, Dennis Olivetti, Jukka Suomela
http://arxiv.org/abs/1902.06803v1
• [cs.DC]Layering Data Structures over Skip Graphs for Increased NUMA Locality
Samuel Thomas, Hammurabi Mendes
http://arxiv.org/abs/1902.06891v1
• [cs.DC]Optimizing Network Performance for Distributed DNN Training on GPU Clusters: ImageNet/AlexNet Training in 1.5 Minutes
Peng Sun, Wansen Feng, Ruobing Han, Shengen Yan, Yonggang Wen
http://arxiv.org/abs/1902.06855v1
• [cs.DC]Towards a Scaled IoT Pub/Sub Architecture for 5G Networks: the Case of Multiaccess Edge Computing
Alessandro E. C. Redondi, Andrés Arcia-Moret, Pietro Manzoni
http://arxiv.org/abs/1902.07022v1
• [cs.DC]Who started this rumor? Quantifying the natural differential privacy guarantees of gossip protocols
Aurélien Bellet, Rachid Guerraoui, Hadrien Hendrikx
http://arxiv.org/abs/1902.07138v1
• [cs.DC]Zest: REST over ZeroMQ
John Moore, Andrés Arcia-Moret, Poonam Yadav, Richard Mortier, Anthony Brown, Derek McAuley, Andy Crabtree, Chris Greenhalgh, Hamed Haddadi, Yousef Amar
http://arxiv.org/abs/1902.07009v1
• [cs.DS]Hardness of exact distance queries in sparse graphs through hub labeling
Adrian Kosowski, Przemysław Uznański, Laurent Viennot
http://arxiv.org/abs/1902.07055v1
• [cs.GT]Bayesian Exploration with Heterogeneous Agents
Nicole Immorlica, Jieming Mao, Aleksandrs Slivkins, Zhiwei Steven Wu
http://arxiv.org/abs/1902.07119v1
• [cs.HC]Cybercrime Investigators are Users Too! Understanding the Socio-Technical Challenges Faced by Law Enforcement
Mariam Nouh, Jason R. C. Nurse, Helena Webb, Michael Goldsmith
http://arxiv.org/abs/1902.06961v1
• [cs.IR]Collaborative Similarity Embedding for Recommender Systems
Chih-Ming Chen, Chuan-Ju Wang, Ming-Feng Tsai, Yi-Hsuan Yang
http://arxiv.org/abs/1902.06188v2
• [cs.IT]Channel Extrapolation in FDD Massive MIMO: Theoretical Analysis and Numerical Validation
François Rottenberg, Rui Wang, Jianzhong Zhang, Andreas F. Molisch
http://arxiv.org/abs/1902.06844v1
• [cs.IT]Distributed Learning for Channel Allocation Over a Shared Spectrum
S. M. Zafaruddin, Ilai Bistritz, Amir Leshem, Dusit Niyato
http://arxiv.org/abs/1902.06353v2
• [cs.IT]Downlink NOMA in Multi-UAV Networks over Bivariate Rician Shadowed Fading Channels
Tan Zheng Hui Ernest, A S Madhukumar, Rajendra Prasad Sirigina, Anoop Kumar Krishna
http://arxiv.org/abs/1902.06869v1
• [cs.IT]Effective Capacity and Power Allocation for Machine-Type Communication
Mohammad Shehab, Hirley Alves, Matti Latva-aho
http://arxiv.org/abs/1902.07064v1
• [cs.IT]Few-Bit CSI Acquisition for Centralized Cell-Free Massive MIMO with Spatial Correlation
Dick Maryopi, Alister Burr
http://arxiv.org/abs/1902.07118v1
• [cs.IT]Jamming Suppression in Massive MIMO Systems
Hossein Akhlaghpasand, Emil Björnson, S. Mohammad Razavizadeh
http://arxiv.org/abs/1902.07053v1
• [cs.IT]Multi-Antenna Covert Communications in Random Wireless Networks
Tong-Xing Zheng, Hui-Ming Wang, Derrick Wing Kwan Ng, Jinhong Yuan
http://arxiv.org/abs/1902.06936v1
• [cs.IT]Towards Hardware Implementation of Neural Network-based Communication Algorithms
Fayçal Ait Aoudia, Jakob Hoydis
http://arxiv.org/abs/1902.06939v1
• [cs.IT]Wireless Key Generation from Imperfect Channel State Information: Performance Analysis and Improvements
Xinrong Guan, Ning Ding, Yueming Cai, Weiwei Yang
http://arxiv.org/abs/1902.07050v1
• [cs.LG]Adaptive Cross-Modal Few-Shot Learning
Chen Xing, Negar Rostamzadeh, Boris N. Oreshkin, Pedro O. Pinheiro
http://arxiv.org/abs/1902.07104v1
• [cs.LG]An entropic feature selection method in perspective of Turing formula
Jingyi Shi, Jialin Zhang, Yaorong Ge
http://arxiv.org/abs/1902.07115v1
• [cs.LG]DEDPUL: Method for Mixture Proportion Estimation and Positive-Unlabeled Classification based on Density Estimation
Dmitry Ivanov
http://arxiv.org/abs/1902.06965v1
• [cs.LG]Data augmentation for low resource sentiment analysis using generative adversarial networks
Rahul Gupta
http://arxiv.org/abs/1902.06818v1
• [cs.LG]Deep Learning Based Autoencoder for Interference Channel
Dehao Wu, Maziar Nekovee, Yue Wang
http://arxiv.org/abs/1902.06841v1
• [cs.LG]Evaluating model calibration in classification
Juozas Vaicenavicius, David Widmann, Carl Andersson, Fredrik Lindsten, Jacob Roll, Thomas B. Schön
http://arxiv.org/abs/1902.06977v1
• [cs.LG]Explaining a black-box using Deep Variational Information Bottleneck Approach
Seojin Bang, Pengtao Xie, Wei Wu, Eric Xing
http://arxiv.org/abs/1902.06918v1
• [cs.LG]Fast Compressive Sensing Recovery Using Generative Models with Structured Latent Variables
Shaojie Xu, Sihan Zeng, Justin Romberg
http://arxiv.org/abs/1902.06913v1
• [cs.LG]Global Convergence of Adaptive Gradient Methods for An Over-parameterized Neural Network
Xiaoxia Wu, Simon S. Du, Rachel Ward
http://arxiv.org/abs/1902.07111v1
• [cs.LG]Identifying nonlinear dynamical systems via generative recurrent neural networks with applications to fMRI
Georgia Koppe, Hazem Toutounji, Peter Kirsch, Stefanie Lis, Daniel Durstewitz
http://arxiv.org/abs/1902.07186v1
• [cs.LG]Investigating Generalisation in Continuous Deep Reinforcement Learning
Chenyang Zhao, Olivier Siguad, Freek Stulp, Timothy M. Hospedales
http://arxiv.org/abs/1902.07015v1
• [cs.LG]Label-Removed Generative Adversarial Networks Incorporating with K-Means
Ce Wang, Zhangling Chen, Kun Shang
http://arxiv.org/abs/1902.06938v1
• [cs.LG]Learning Task Agnostic Sufficiently Accurate Models
Clark Zhang, Arbaaz Khan, Santiago Paternain, Vijay Kumar, Alejandro Ribeiro
http://arxiv.org/abs/1902.06862v1
• [cs.LG]Learning to Generalize from Sparse and Underspecified Rewards
Rishabh Agarwal, Chen Liang, Dale Schuurmans, Mohammad Norouzi
http://arxiv.org/abs/1902.07198v1
• [cs.LG]Low-bit Quantization of Neural Networks for Efficient Inference
Yoni Choukroun, Eli Kravchik, Pavel Kisilev
http://arxiv.org/abs/1902.06822v1
• [cs.LG]Measuring Compositionality in Representation Learning
Jacob Andreas
http://arxiv.org/abs/1902.07181v1
• [cs.LG]Nutrition and Health Data for Cost-Sensitive Learning
Mohammad Kachuee, Kimmo Karkkainen, Orpaz Goldstein, Davina Zamanzadeh, Majid Sarrafzadeh
http://arxiv.org/abs/1902.07102v1
• [cs.LG]On the Convergence of EM for truncated mixtures of two Gaussians
Sai Ganesh Nagarajan, Ioannis Panageas
http://arxiv.org/abs/1902.06958v1
• [cs.LG]Proper-Composite Loss Functions in Arbitrary Dimensions
Zac Cranko, Robert C. Williamson, Richard Nock
http://arxiv.org/abs/1902.06881v1
• [cs.LG]Recovery of a mixture of Gaussians by sum-of-norms clustering
Tao Jiang, Stephen Vavasis, Chen Wen Zhai
http://arxiv.org/abs/1902.07137v1
• [cs.LG]Regularizing Black-box Models for Improved Interpretability
Gregory Plumb, Maruan Al-Shedivat, Eric Xing, Ameet Talwalkar
http://arxiv.org/abs/1902.06787v1
• [cs.LG]Seven Myths in Machine Learning Research
Oscar Chang, Hod Lipson
http://arxiv.org/abs/1902.06789v1
• [cs.LG]Simplifying Graph Convolutional Networks
Felix Wu, Tianyi Zhang, Amauri Holanda de Souza Jr., Christopher Fifty, Tao Yu, Kilian Q. Weinberger
http://arxiv.org/abs/1902.07153v1
• [cs.LG]There are No Bit Parts for Sign Bits in Black-Box Attacks
Abdullah Al-Dujaili, Una-May O'Reilly
http://arxiv.org/abs/1902.06894v1
• [cs.MA]On Voting Strategies and Emergent Communication
Shubham Gupta, Ambedkar Dukkipati
http://arxiv.org/abs/1902.06897v1
• [cs.NE]Evolutionary Neural AutoML for Deep Learning
Jason Liang, Elliot Meyerson, Babak Hodjat, Dan Fink, Karl Mutch, Risto Miikkulainen
http://arxiv.org/abs/1902.06827v1
• [cs.RO]2D LiDAR Map Prediction via Estimating Motion Flow with GRU
Yafei Song, Yonghong Tian, Gang Wang, Mingyang Li
http://arxiv.org/abs/1902.06919v1
• [cs.RO]A Soft High Force Hand Exoskeleton for Rehabilitation and Assistance of Spinal Cord Injury and Stroke Individuals
Shuangyue Yu, Hadia Perez, James Barkas, Mohamed Mohamed, Mohamed Eldaly, Tzu-Hao Huang, Xiaolong Yang, Hao Su, Maria del Mar Cortes, Dylan J. Edwards
http://arxiv.org/abs/1902.07112v1
• [cs.RO]Design and Control of a Quasi-Direct Drive Soft Hybrid Knee Exoskeleton for Injury Prevention during Squatting
Shuangyue Yu, Tzu-Hao Huang, Dianpeng Wang, Brian Lynn, Dina Sayd, Viktor Silivanov, Young Soo Park, Yingli Tian, Fellow, IEEE, Hao Su, Member, IEEE
http://arxiv.org/abs/1902.07106v1
• [cs.RO]Efficient Obstacle Rearrangement for Object Manipulation Tasks in Cluttered Environments
Jinhwi Lee, Younggil Cho, Changjoo Nam, Jonghyeon Park, Changhwan Kim
http://arxiv.org/abs/1902.06907v1
• [cs.RO]Improving dual-arm assembly by master-slave compliance
Markku Suomalainen, Sylvain Calinon, Emmanuel Pignat, Ville Kyrki
http://arxiv.org/abs/1902.07007v1
• [cs.RO]Multi-view Incremental Segmentation of 3D Point Clouds for Mobile Robots
Jingdao Chen, Yong K. Cho, Zsolt Kira
http://arxiv.org/abs/1902.06768v1
• [cs.RO]Nonlinear Model Predictive Control for Robust Bipedal Locomotion Exploring CoM Height and Angular Momentum Changes
Jiatao Ding, Chengxu Zhou, Songyan Xin, Xiaohui Xiao, Nikos Tsagarakis
http://arxiv.org/abs/1902.06770v1
• [cs.RO]Virtual Border Teaching Using a Network Robot System
Dennis Sprute, Klaus Tönnies, Matthias König
http://arxiv.org/abs/1902.06997v1
• [cs.SD]End-to-end Lyrics Alignment for Polyphonic Music Using an Audio-to-Character Recognition Model
Daniel Stoller, Simon Durand, Sebastian Ewert
http://arxiv.org/abs/1902.06797v1
• [cs.SI]Do zealots increase or decrease the polarization in social networks?
Snehal M. Shekatkar
http://arxiv.org/abs/1902.07084v1
• [cs.SI]Estimating Network Effects Using Naturally Occurring Peer Notification Queue Counterfactuals
Craig Tutterow, Guillaume Saint-Jacques
http://arxiv.org/abs/1902.07133v1
• [eess.AS]A spelling correction model for end-to-end speech recognition
Jinxi Guo, Tara N. Sainath, Ron J. Weiss
http://arxiv.org/abs/1902.07178v1
• [math.OC]2-Wasserstein Approximation via Restricted Convex Potentials with Application to Improved Training for GANs
Amirhossein Taghvaei, Amin Jalali
http://arxiv.org/abs/1902.07197v1
• [math.ST]Asymptotic Theory of Eigenvectors for Large Random Matrices
Jianqing Fan, Yingying Fan, Xiao Han, Jinchi Lv
http://arxiv.org/abs/1902.06846v1
• [math.ST]Efficiency requires innovation
Abram M. Kagan
http://arxiv.org/abs/1902.06802v1
• [math.ST]New statistical methodology for second level global sensitivity analysis
Anouar Meynaoui, Amandine Marrel, Béatrice Laurent
http://arxiv.org/abs/1902.07030v1
• [math.ST]Penultimate Analysis of the Conditional Multivariate Extremes Tail Model
Thomas Lugrin, Anthony C. Davison, Jonathan A. Tawn
http://arxiv.org/abs/1902.06972v1
• [math.ST]Statistical inference for a partially observed interacting system of Hawkes processes
Chenguang Liu
http://arxiv.org/abs/1902.07062v1
• [math.ST]The KLR-theorem revisited
Abram M. Kagan
http://arxiv.org/abs/1902.06800v1
• [math.ST]Universality of Computational Lower Bounds for Submatrix Detection
Matthew Brennan, Guy Bresler, Wasim Huleihel
http://arxiv.org/abs/1902.06916v1
• [q-bio.NC]A computational model for grid maps in neural populations
Fabio Anselmi, Benedetta Franceschiello, Micah M. Murray, Lorenzo Rosasco
http://arxiv.org/abs/1902.06553v2
• [q-bio.NC]Large-scale directed network inference with multivariate transfer entropy and hierarchical statistical testing
Leonardo Novelli, Patricia Wollstadt, Pedro Mediano, Michael Wibral, Joseph T. Lizier
http://arxiv.org/abs/1902.06828v1
• [quant-ph]Probabilistic Modeling with Matrix Product States
James Stokes, John Terilla
http://arxiv.org/abs/1902.06888v1
• [stat.ME]A primer on statistically validated networks
Salvatore Miccichè, Rosario Nunzio Mantegna
http://arxiv.org/abs/1902.07074v1
• [stat.ME]Employing latent variable models to improve efficiency in composite endpoint analysis
Martina McMenamin, Jessica K. Barrett, Anna Berglind, James M. S. Wason
http://arxiv.org/abs/1902.07037v1
• [stat.ME]Exact Kalman Filter for Binary Time Series
Augusto Fasano, Giovanni Rebaudo, Daniele Durante, Sonia Petrone
http://arxiv.org/abs/1902.06994v1
• [stat.ME]Penalized basis models for very large spatial datasets
Mitchell Krock, William Kleiber, Stephen Becker
http://arxiv.org/abs/1902.06877v1
• [stat.ME]Simulation study of estimating between-study variance and overall effect in meta-analysis of odds-ratios
Ilyas Bakbergenuly, David C. Hoaglin, Elena Kulinskaya
http://arxiv.org/abs/1902.07154v1
• [stat.ML]Hyperbolic Discounting and Learning over Multiple Horizons
William Fedus, Carles Gelada, Yoshua Bengio, Marc G. Bellemare, Hugo Larochelle
http://arxiv.org/abs/1902.06865v1
• [stat.ML]Multifidelity Bayesian Optimization for Binomial Output
Leonid Matyushin, Alexey Zaytsev, Oleg Alenkin, Andrey Ustuzhanin
http://arxiv.org/abs/1902.06937v1
• [stat.ML]On the Impact of the Activation Function on Deep Neural Networks Training
Soufiane Hayou, Arnaud Doucet, Judith Rousseau
http://arxiv.org/abs/1902.06853v1
• [stat.ML]On the consistency of supervised learning with missing values
Julie Josse, Nicolas Prost, Erwan Scornet, Gaël Varoquaux
http://arxiv.org/abs/1902.06931v1
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