astro-ph.IM - 仪器仪表和天体物理学方法

    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.DB - 数据库 cs.DC - 分布式、并行与集群计算 cs.DL - 数字图书馆 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.LO - 计算逻辑 cs.MA - 多代理系统 cs.NE - 神经与进化计算 cs.RO - 机器人学 cs.SE - 软件工程 cs.SI - 社交网络与信息网络 econ.TH - 理论经济学 eess.IV - 图像与视频处理 eess.SP - 信号处理 math.NA - 数值分析 math.OC - 优化与控制 math.ST - 统计理论 physics.soc-ph - 物理学与社会 q-bio.QM - 定量方法 stat.AP - 应用统计 stat.CO - 统计计算 stat.ML - (统计)机器学习

    • [astro-ph.IM]AXS: A framework for fast astronomical data processing based on Apache Spark
    • [cs.AI]AI-CARGO: A Data-Driven Air-Cargo Revenue Management System
    • [cs.AI]NTP : A Neural Network Topology Profiler
    • [cs.AI]Neural-Symbolic Argumentation Mining: an Argument in Favour of Deep Learning and Reasoning
    • [cs.CL]A Joint Named-Entity Recognizer for Heterogeneous Tag-setsUsing a Tag Hierarchy
    • [cs.CL]Acoustic-to-Word Models with Conversational Context Information
    • [cs.CL]Augmenting Data with Mixup for Sentence Classification: An Empirical Study
    • [cs.CL]Corpus Augmentation by Sentence Segmentation for Low-Resource Neural Machine Translation
    • [cs.CL]Domain adaptation for part-of-speech tagging of noisy user-generated text
    • [cs.CL]EventKG - the Hub of Event Knowledge on the Web - and Biographical Timeline Generation
    • [cs.CL]FastSpeech: Fast, Robust and Controllable Text to Speech
    • [cs.CL]From web crawled text to project descriptions: automatic summarizing of social innovation projects
    • [cs.CL]Look Again at the Syntax: Relational Graph Convolutional Network for Gendered Ambiguous Pronoun Resolution
    • [cs.CL]Recent Advances in Neural Question Generation
    • [cs.CL]Sample Efficient Text Summarization Using a Single Pre-Trained Transformer
    • [cs.CL]Sentence Length
    • [cs.CL]Simplified Neural Unsupervised Domain Adaptation
    • [cs.CR]DoPa: A Fast and Comprehensive CNN Defense Methodology against Physical Adversarial Attacks
    • [cs.CV]A Comparison of Stereo-Matching Cost between Convolutional Neural Network and Census for Satellite Images
    • [cs.CV]A Comprehensive Study of ImageNet Pre-Training for Historical Document Image Analysis
    • [cs.CV]A Neural-Symbolic Architecture for Inverse Graphics Improved by Lifelong Meta-Learning
    • [cs.CV]Attributes Guided Feature Learning for Vehicle Re-identification
    • [cs.CV]Automated Segmentation for Hyperdense Middle Cerebral Artery Sign of Acute Ischemic Stroke on Non-Contrast CT Images
    • [cs.CV]Borrow from Anywhere: Pseudo Multi-modal Object Detection in Thermal Imagery
    • [cs.CV]Bridging Stereo Matching and Optical Flow via Spatiotemporal Correspondence
    • [cs.CV]Data-Efficient Image Recognition with Contrastive Predictive Coding
    • [cs.CV]Domain Adaptation for Vehicle Detection from Bird’s Eye View LiDAR Point Cloud Data
    • [cs.CV]Dual Active Sampling on Batch-Incremental Active Learning
    • [cs.CV]Efficient Plane-Based Optimization of Geometry and Texture for Indoor RGB-D Reconstruction
    • [cs.CV]End-to-End Learned Random Walker for Seeded Image Segmentation
    • [cs.CV]LapTool-Net: A Contextual Detector of Surgical Tools in Laparoscopic Videos Based on Recurrent Convolutional Neural Networks
    • [cs.CV]Learning Fully Dense Neural Networks for Image Semantic Segmentation
    • [cs.CV]Looking to Relations for Future Trajectory Forecast
    • [cs.CV]Multi-View Large-Scale Bundle Adjustment Method for High-Resolution Satellite Images
    • [cs.CV]Oculum afficit: Ocular Affect Recognition
    • [cs.CV]PEPSI++: Fast and Lightweight Network for Image Inpainting
    • [cs.CV]Robust Motion Segmentation from Pairwise Matches
    • [cs.CV]Segmentation-Aware Hyperspectral Image Classification
    • [cs.CV]Segmentation-Aware Image Denoising without Knowing True Segmentation
    • [cs.CV]Semi-Supervised Learning with Scarce Annotations
    • [cs.CV]Separating Overlapping Tissue Layers from Microscopy Images
    • [cs.CV]Spatial Sampling Network for Fast Scene Understanding
    • [cs.CV]Underwater Color Restoration Using U-Net Denoising Autoencoder
    • [cs.CV]Using Orthophoto for Building Boundary Sharpening in the Digital Surface Model
    • [cs.CV]WPU-Net:Boundary learning by using weighted propagation in convolution network
    • [cs.CV]What Would You Expect? Anticipating Egocentric Actions with Rolling-Unrolling LSTMs and Modality Attention
    • [cs.CY]Data Cooperatives: Towards a Foundation for Decentralized Personal Data Management
    • [cs.CY]Planning Gamification Strategies based on User Characteristics and DM: A Gender-based Case Study
    • [cs.CY]Uniqueness of Medical Data Mining: How the new technologies and data they generate are transforming medicine
    • [cs.DB]ALEX: An Updatable Adaptive Learned Index
    • [cs.DC]Connectivity Lower Bounds in Broadcast Congested Clique
    • [cs.DC]Distributed Pattern Formation in a Ring
    • [cs.DC]Dynamic Algorithms for the Massively Parallel Computation Model
    • [cs.DC]ElfStore: A Resilient Data Storage Service for Federated Edge and Fog Resources
    • [cs.DC]Infinite Grid Exploration by Disoriented Robots
    • [cs.DC]Online Collection and Forecasting of Resource Utilization in Large-Scale Distributed Systems
    • [cs.DC]Online Research Report: rDLB: A Novel Approach for Robust Dynamic Load Balancing of Scientific Applications with Parallel Independent Tasks
    • [cs.DC]Two stage cluster for resource optimization with Apache Mesos
    • [cs.DL]A Scalable Hybrid Research Paper Recommender System for Microsoft Academic
    • [cs.DS]Separating Structure from Noise in Large Graphs Using the Regularity Lemma
    • [cs.GT]Equilibrium Characterization for Data Acquisition Games
    • [cs.HC]Can a Humanoid Robot be part of Organizational Work Force? A User Study leveraging on Sentiment Analysis
    • [cs.HC]Look Who’s Talking Now: Implications of AV’s Explanations on Driver’s Trust, AV Preference, Anxiety and Mental Workload
    • [cs.HC]Multi-agent Attentional Activity Recognition
    • [cs.IR]ANTIQUE: A Non-Factoid Question Answering Benchmark
    • [cs.IR]Deeper Text Understanding for IR with Contextual Neural Language Modeling
    • [cs.IR]Practice on Long Sequential User Behavior Modeling for Click-Through Rate Prediction
    • [cs.IR]Retrieving Multi-Entity Associations: An Evaluation of Combination Modes for Word Embeddings
    • [cs.IT]Cocktail Intra-Symbol-Codes: Exceeding the Channel Limit of QPSK Input
    • [cs.IT]Codebooks from generalized bent $\mathbb{Z}4$-valued quadratic forms
    • [cs.IT]LAGC: Lazily Aggregated Gradient Coding for Straggler-Tolerant and Communication-Efficient Distributed Learning
    • [cs.IT]On the Restricted Isometry Property of Centered Self Khatri-Rao Products
    • [cs.IT]Opportunistic Temporal Fair Mode Selection and User Scheduling for Full-duplex Systems
    • [cs.LG]A framework for the extraction of Deep Neural Networks by leveraging public data
    • [cs.LG]Adaptive Model Selection Framework: An Application to Airline Pricing
    • [cs.LG]Augmenting Physiological Time Series Data: A Case Study for Sleep Apnea Detection
    • [cs.LG]Blind identification of stochastic block models from dynamical observations
    • [cs.LG]COBRA: Data-Efficient Model-Based RL through Unsupervised Object Discovery and Curiosity-Driven Exploration
    • [cs.LG]Convergence and Margin of Adversarial Training on Separable Data
    • [cs.LG]Deep Reinforcement Learning for Detecting Malicious Websites
    • [cs.LG]Detecting Adversarial Examples and Other Misclassifications in Neural Networks by Introspection
    • [cs.LG]Discovering Hidden Structure in High Dimensional Human Behavioral Data via Tensor Factorization
    • [cs.LG]Ellipsoidal Trust Region Methods and the Marginal Value of Hessian Information for Neural Network Training
    • [cs.LG]Enhancing Domain Word Embedding via Latent Semantic Imputation
    • [cs.LG]Estimating Node Importance in Knowledge Graphs Using Graph Neural Networks
    • [cs.LG]Evaluating recommender systems for AI-driven data science
    • [cs.LG]Explainable Machine Learning for Scientific Insights and Discoveries
    • [cs.LG]Fine-grained Optimization of Deep Neural Networks
    • [cs.LG]Hierarchical Reinforcement Learning for Quadruped Locomotion
    • [cs.LG]Joint Information Preservation for Heterogeneous Domain Adaptation
    • [cs.LG]Learning Networked Exponential Families with Network Lasso
    • [cs.LG]Learning Robust Options by Conditional Value at Risk Optimization
    • [cs.LG]Learning to Confuse: Generating Training Time Adversarial Data with Auto-Encoder
    • [cs.LG]Maximum Entropy-Regularized Multi-Goal Reinforcement Learning
    • [cs.LG]Measuring the effects of confounders in medical supervised classification problems: the Confounding Index (CI)
    • [cs.LG]Multi-Task Kernel Null-Space for One-Class Classification
    • [cs.LG]Optimal Decision Making Under Strategic Behavior
    • [cs.LG]Recurring Concept Meta-learning for Evolving Data Streams
    • [cs.LG]Revisiting hard thresholding for DNN pruning
    • [cs.LG]Scalable Gromov-Wasserstein Learning for Graph Partitioning and Matching
    • [cs.LG]Simulation and Augmentation of Social Networks for Building Deep Learning Models
    • [cs.LG]Thresholding Graph Bandits with GrAPL
    • [cs.LG]Time-Smoothed Gradients for Online Forecasting
    • [cs.LG]Unsupervised Linear and Nonlinear Channel Equalization and Decoding using Variational Autoencoders
    • [cs.LO]A Note on Reasoning on $\textit{DL-Lite}    {\cal R}$ with Defeasibility
    • [cs.LO]Properties and Extensions of Alternating Path Relevance - I
    • [cs.MA]Asynchronous Scattering
    • [cs.NE]Evolving neural networks to follow trajectories of arbitrary complexity
    • [cs.RO]A Deep Reinforcement Learning Driving Policy for Autonomous Road Vehicles
    • [cs.RO]Automated shapeshifting for function recovery in damaged robots
    • [cs.RO]Globally Optimal Joint Search of Topology and Trajectory for Planar Linkages
    • [cs.RO]Practical Robot Learning from Demonstrations using Deep End-to-End Training
    • [cs.RO]The Problem of Adhesion Methods and Locomotion Mechanism Development for Wall-Climbing Robots
    • [cs.RO]ZJUNlict Extended Team Description Paper for RoboCup 2019
    • [cs.SE]The Machine Learning Bazaar: Harnessing the ML Ecosystem for Effective System Development
    • [cs.SI]Demographic Differentials in Facebook Usage Around the World
    • [cs.SI]IdeoTrace: A Framework for Ideology Tracing with a Case Study on the 2016 U.S. Presidential Election
    • [econ.TH]Cheating in Ranking Systems
    • [eess.IV]Automated Pupillary Light Reflex Test on a Portable Platform
    • [eess.SP]Latency Analysis for Sequential Detection in Low-Complexity Binary Radio Systems
    • [eess.SP]MIST: A Novel Training Strategy for Low-latencyScalable Neural Net Decoders
    • [eess.SP]Source Localization and Tracking for Dynamic Radio Cartography using Directional Antennas
    • [math.NA]Heavy Hitters and Bernoulli Convolutions
    • [math.OC]Beyond Alternating Updates for Matrix Factorization with Inertial Bregman Proximal Gradient Algorithms
    • [math.ST]On LSE in regression model for long-range dependent random fields on spheres
    • [math.ST]Super-Consistent Estimation of Points of Impact in Nonparametric Regression with Functional Predictors
    • [physics.soc-ph]Optimal interlayer structure for information spreading on multilayer networks
    • [q-bio.QM]Selection of a Minimal Number of Significant Porcine SNPs by an Information Gain and Genetic Algorithm Hybrid Model
    • [stat.AP]A stochastic model for the lifecycle and track of extreme extratropical cyclones in the North Atlantic
    • [stat.AP]Measuring Average Treatment Effect from Heavy-tailed Data
    • [stat.AP]The perils of automated fitting of datasets: the case of a wind turbine cost model
    • [stat.CO]Application of the interacting particle system method to piecewise deterministic Markov processes used in reliability
    • [stat.ML]Distributionally Robust Formulation and Model Selection for the Graphical Lasso
    • [stat.ML]On the minimax optimality and superiority of deep neural network learning over sparse parameter spaces
    • [stat.ML]Survival Function Matching for Calibrated Time-to-Event Predictions

    ·····································

    • [astro-ph.IM]AXS: A framework for fast astronomical data processing based on Apache Spark
    Petar Zečević, Colin T. Slater, Mario Jurić, Andrew J. Connolly, Sven Lončarić, Eric C. Bellm, V. Zach Golkhou, Krzysztof Suberlack
    http://arxiv.org/abs/1905.09034v1

    • [cs.AI]AI-CARGO: A Data-Driven Air-Cargo Revenue Management System
    Stefano Giovanni Rizzo, Ji Lucas, Zoi Kaoudi, Jorge-Arnulfo Quiane-Ruiz, Sanjay Chawla
    http://arxiv.org/abs/1905.09130v1

    • [cs.AI]NTP : A Neural Network Topology Profiler
    Raghavendra Bhat, Pravin Chandran, Juby Jose, Viswanath Dibbur, Prakash Sirra Ajith
    http://arxiv.org/abs/1905.09063v1

    • [cs.AI]Neural-Symbolic Argumentation Mining: an Argument in Favour of Deep Learning and Reasoning
    Andrea Galassi, Kristian Kersting, Marco Lippi, Xiaoting Shao, Paolo Torroni
    http://arxiv.org/abs/1905.09103v1

    • [cs.CL]A Joint Named-Entity Recognizer for Heterogeneous Tag-setsUsing a Tag Hierarchy
    Genady Beryozkin, Yoel Drori, Oren Gilon, Tzvika Hartman, Idan Szpektor
    http://arxiv.org/abs/1905.09135v1

    • [cs.CL]Acoustic-to-Word Models with Conversational Context Information
    Suyoun Kim, Florian Metze
    http://arxiv.org/abs/1905.08796v1

    • [cs.CL]Augmenting Data with Mixup for Sentence Classification: An Empirical Study
    Hongyu Guo, Yongyi Mao, Richong Zhang
    http://arxiv.org/abs/1905.08941v1

    • [cs.CL]Corpus Augmentation by Sentence Segmentation for Low-Resource Neural Machine Translation
    Jinyi Zhang, Tadahiro Matsumoto
    http://arxiv.org/abs/1905.08945v1

    • [cs.CL]Domain adaptation for part-of-speech tagging of noisy user-generated text
    Luisa März, Dietrich Trautmann, Benjamin Roth
    http://arxiv.org/abs/1905.08920v1

    • [cs.CL]EventKG - the Hub of Event Knowledge on the Web - and Biographical Timeline Generation
    Simon Gottschalk, Elena Demidova
    http://arxiv.org/abs/1905.08794v1

    • [cs.CL]FastSpeech: Fast, Robust and Controllable Text to Speech
    Yi Ren, Yangjun Ruan, Xu Tan, Tao Qin, Sheng Zhao, Zhou Zhao, Tie-Yan Liu
    http://arxiv.org/abs/1905.09263v1

    • [cs.CL]From web crawled text to project descriptions: automatic summarizing of social innovation projects
    Nikola Milosevic, Dimitar Marinov, Abdullah Gok, Goran Nenadic
    http://arxiv.org/abs/1905.09086v1

    • [cs.CL]Look Again at the Syntax: Relational Graph Convolutional Network for Gendered Ambiguous Pronoun Resolution
    Yinchuan Xu, Junlin Yang
    http://arxiv.org/abs/1905.08868v1

    • [cs.CL]Recent Advances in Neural Question Generation
    Liangming Pan, Wenqiang Lei, Tat-Seng Chua, Min-Yen Kan
    http://arxiv.org/abs/1905.08949v1

    • [cs.CL]Sample Efficient Text Summarization Using a Single Pre-Trained Transformer
    Urvashi Khandelwal, Kevin Clark, Dan Jurafsky, Lukasz Kaiser
    http://arxiv.org/abs/1905.08836v1

    • [cs.CL]Sentence Length
    Gábor Borbély, András Kornai
    http://arxiv.org/abs/1905.09139v1

    • [cs.CL]Simplified Neural Unsupervised Domain Adaptation
    Timothy A Miller
    http://arxiv.org/abs/1905.09153v1

    • [cs.CR]DoPa: A Fast and Comprehensive CNN Defense Methodology against Physical Adversarial Attacks
    Zirui Xu, Fuxun Yu, Xiang Chen
    http://arxiv.org/abs/1905.08790v1

    • [cs.CV]A Comparison of Stereo-Matching Cost between Convolutional Neural Network and Census for Satellite Images
    Bihe Chen, Rongjun Qin, Xu Huang, Shuang Song, Xiaohu Lu
    http://arxiv.org/abs/1905.09147v1

    • [cs.CV]A Comprehensive Study of ImageNet Pre-Training for Historical Document Image Analysis
    Linda Studer, Michele Alberti, Vinaychandran Pondenkandath, Pinar Goktepe, Thomas Kolonko, Andreas Fischer, Marcus Liwicki, Rolf Ingold
    http://arxiv.org/abs/1905.09113v1

    • [cs.CV]A Neural-Symbolic Architecture for Inverse Graphics Improved by Lifelong Meta-Learning
    Michael Kissner, Helmut Mayer
    http://arxiv.org/abs/1905.08910v1

    • [cs.CV]Attributes Guided Feature Learning for Vehicle Re-identification
    Aihua Zheng, Xianmin Lin, Chenglong Li, Ran He, Jin Tang
    http://arxiv.org/abs/1905.08997v1

    • [cs.CV]Automated Segmentation for Hyperdense Middle Cerebral Artery Sign of Acute Ischemic Stroke on Non-Contrast CT Images
    Jia You, Philip L. H. Yu, Anderson C. O. Tsang, Eva L. H. Tsui, Pauline P. S. Woo, Gilberto K. K. Leung
    http://arxiv.org/abs/1905.09049v1

    • [cs.CV]Borrow from Anywhere: Pseudo Multi-modal Object Detection in Thermal Imagery
    Chaitanya Devaguptapu, Ninad Akolekar, Manuj M Sharma, Vineeth N Balasubramanian
    http://arxiv.org/abs/1905.08789v1

    • [cs.CV]Bridging Stereo Matching and Optical Flow via Spatiotemporal Correspondence
    Hsueh-Ying Lai, Yi-Hsuan Tsai, Wei-Chen Chiu
    http://arxiv.org/abs/1905.09265v1

    • [cs.CV]Data-Efficient Image Recognition with Contrastive Predictive Coding
    Olivier J. Hénaff, Ali Razavi, Carl Doersch, S. M. Ali Eslami, Aaron van den Oord
    http://arxiv.org/abs/1905.09272v1

    • [cs.CV]Domain Adaptation for Vehicle Detection from Bird’s Eye View LiDAR Point Cloud Data
    Khaled Saleh, Ahmed Abobakr, Mohammed Attia, Julie Iskander, Darius Nahavandi, Mohammed Hossny
    http://arxiv.org/abs/1905.08955v1

    • [cs.CV]Dual Active Sampling on Batch-Incremental Active Learning
    Johan Phan, Massimiliano Ruocco, Francesco Scibilia
    http://arxiv.org/abs/1905.09247v1

    • [cs.CV]Efficient Plane-Based Optimization of Geometry and Texture for Indoor RGB-D Reconstruction
    Chao Wang, Xiaohu Guo
    http://arxiv.org/abs/1905.08853v1

    • [cs.CV]End-to-End Learned Random Walker for Seeded Image Segmentation
    Lorenzo Cerrone, Alexander Zeilmann, Fred A. Hamprecht
    http://arxiv.org/abs/1905.09045v1

    • [cs.CV]LapTool-Net: A Contextual Detector of Surgical Tools in Laparoscopic Videos Based on Recurrent Convolutional Neural Networks
    Babak Namazi, Ganesh Sankaranarayanan, Venkat Devarajan
    http://arxiv.org/abs/1905.08983v1

    • [cs.CV]Learning Fully Dense Neural Networks for Image Semantic Segmentation
    Mingmin Zhen, Jinglu Wang, Lei Zhou, Tian Fang, Long Quan
    http://arxiv.org/abs/1905.08929v1

    • [cs.CV]Looking to Relations for Future Trajectory Forecast
    Chiho Choi, Behzad Dariush
    http://arxiv.org/abs/1905.08855v1

    • [cs.CV]Multi-View Large-Scale Bundle Adjustment Method for High-Resolution Satellite Images
    Xu Huang, Rongjun Qin
    http://arxiv.org/abs/1905.09152v1

    • [cs.CV]Oculum afficit: Ocular Affect Recognition
    Elmar Langholz
    http://arxiv.org/abs/1905.09240v1

    • [cs.CV]PEPSI++: Fast and Lightweight Network for Image Inpainting
    Yong-Goo Shin, Min-Cheol Sagong, Yoon-Jae Yeo, Seung-Wook Kim, Sung-Jea Ko
    http://arxiv.org/abs/1905.09010v1

    • [cs.CV]Robust Motion Segmentation from Pairwise Matches
    Federica Arrigoni, Tomas Pajdla
    http://arxiv.org/abs/1905.09043v1

    • [cs.CV]Segmentation-Aware Hyperspectral Image Classification
    Berkan Demirel, Omer Ozdil, Yunus Emre Esin, Safak Ozturk
    http://arxiv.org/abs/1905.09211v1

    • [cs.CV]Segmentation-Aware Image Denoising without Knowing True Segmentation
    Sicheng Wang, Bihan Wen, Junru Wu, Dacheng Tao, Zhangyang Wang
    http://arxiv.org/abs/1905.08965v1

    • [cs.CV]Semi-Supervised Learning with Scarce Annotations
    Sylvestre-Alvise Rebuffi, Sebastien Ehrhardt, Kai Han, Andrea Vedaldi, Andrew Zisserman
    http://arxiv.org/abs/1905.08845v1

    • [cs.CV]Separating Overlapping Tissue Layers from Microscopy Images
    Zahra Montazeri, Gopi M
    http://arxiv.org/abs/1905.09231v1

    • [cs.CV]Spatial Sampling Network for Fast Scene Understanding
    Davide Mazzini, Raimondo Schettini
    http://arxiv.org/abs/1905.09033v1

    • [cs.CV]Underwater Color Restoration Using U-Net Denoising Autoencoder
    Yousif Hashisho, Mohamad Albadawi, Tom Krause, Uwe Freiherr von Lukas
    http://arxiv.org/abs/1905.09000v1

    • [cs.CV]Using Orthophoto for Building Boundary Sharpening in the Digital Surface Model
    Xiaohu Lu, Rongjun Qin, Xu Huang
    http://arxiv.org/abs/1905.09150v1

    • [cs.CV]WPU-Net:Boundary learning by using weighted propagation in convolution network
    Boyuan Ma, Chuni Liu, Xiaoyan Wei, Mingfei Gao, Xiaojuan Ban, Hao Wang, Haiyou Huang, Weihua Xue
    http://arxiv.org/abs/1905.09226v1

    • [cs.CV]What Would You Expect? Anticipating Egocentric Actions with Rolling-Unrolling LSTMs and Modality Attention
    Antonino Furnari, Giovanni Maria Farinella
    http://arxiv.org/abs/1905.09035v1

    • [cs.CY]Data Cooperatives: Towards a Foundation for Decentralized Personal Data Management
    Thomas Hardjono, Alex Pentland
    http://arxiv.org/abs/1905.08819v1

    • [cs.CY]Planning Gamification Strategies based on User Characteristics and DM: A Gender-based Case Study
    Armando M. Toda, Wilk Oliveira, Lei Shi, Ig Ibert Bittencourt, Seiji Isotani, Alexandra Cristea
    http://arxiv.org/abs/1905.09146v1

    • [cs.CY]Uniqueness of Medical Data Mining: How the new technologies and data they generate are transforming medicine
    Krzysztof J. Cios, Bartosz Krawczyk, Jacquelyne Cios, Kevin J. Staley
    http://arxiv.org/abs/1905.09203v1

    • [cs.DB]ALEX: An Updatable Adaptive Learned Index
    Jialin Ding, Umar Farooq Minhas, Hantian Zhang, Yinan Li, Chi Wang, Badrish Chandramouli, Johannes Gehrke, Donald Kossmann, David Lomet
    http://arxiv.org/abs/1905.08898v1

    • [cs.DC]Connectivity Lower Bounds in Broadcast Congested Clique
    Shreyas Pai, Sriram V. Pemmaraju
    http://arxiv.org/abs/1905.09016v1

    • [cs.DC]Distributed Pattern Formation in a Ring
    Anne-Laure Ehresmann, Manuel Lafond, Lata Narayanan, Jaroslav Opatrny
    http://arxiv.org/abs/1905.08856v1

    • [cs.DC]Dynamic Algorithms for the Massively Parallel Computation Model
    Giuseppe F. Italiano, Silvio Lattanzi, Vahab S. Mirrokni, Nikos Parotsidis
    http://arxiv.org/abs/1905.09175v1

    • [cs.DC]ElfStore: A Resilient Data Storage Service for Federated Edge and Fog Resources
    Sumit Kumar Monga, Sheshadri K R, Yogesh Simmhan
    http://arxiv.org/abs/1905.08932v1

    • [cs.DC]Infinite Grid Exploration by Disoriented Robots
    Quentin Bramas, Stephane Devismes, Pascal Lafourcade
    http://arxiv.org/abs/1905.09271v1

    • [cs.DC]Online Collection and Forecasting of Resource Utilization in Large-Scale Distributed Systems
    Tiffany Tuor, Shiqiang Wang, Kin K. Leung, Bong Jun Ko
    http://arxiv.org/abs/1905.09219v1

    • [cs.DC]Online Research Report: rDLB: A Novel Approach for Robust Dynamic Load Balancing of Scientific Applications with Parallel Independent Tasks
    Ali Mohammed, Aurelien Cavelan, Florina M. Ciorba
    http://arxiv.org/abs/1905.08073v2

    • [cs.DC]Two stage cluster for resource optimization with Apache Mesos
    Gourav Rattihalli, Pankaj Saha, Madhusudhan Govindaraju, Devesh Tiwari
    http://arxiv.org/abs/1905.09166v1

    • [cs.DL]A Scalable Hybrid Research Paper Recommender System for Microsoft Academic
    Anshul Kanakia, Zhihong Shen, Darrin Eide, Kuansan Wang
    http://arxiv.org/abs/1905.08880v1

    • [cs.DS]Separating Structure from Noise in Large Graphs Using the Regularity Lemma
    Marco Fiorucci, Francesco Pelosin, Marcello Pelillo
    http://arxiv.org/abs/1905.06917v3

    • [cs.GT]Equilibrium Characterization for Data Acquisition Games
    Jinshuo Dong, Hadi Elzayn, Shahin Jabbari, Michael Kearns, Zachary Schutzman
    http://arxiv.org/abs/1905.08909v1

    • [cs.HC]Can a Humanoid Robot be part of Organizational Work Force? A User Study leveraging on Sentiment Analysis
    Nidhi Mishra, Manoj Ramanathan, Ranjan Satapathy, Erik Cambria, Nadia Magnenat-Thalmann
    http://arxiv.org/abs/1905.08937v1

    • [cs.HC]Look Who’s Talking Now: Implications of AV’s Explanations on Driver’s Trust, AV Preference, Anxiety and Mental Workload
    Na Du, Jacob Haspiel, Qiaoning Zhang, Dawn Tilbury, Anuj K. Pradhan, X. Jessie Yang, Lionel P. Robert Jr
    http://arxiv.org/abs/1905.08878v1

    • [cs.HC]Multi-agent Attentional Activity Recognition
    Kaixuan Chen, Lina Yao, Dalin Zhang, Bin Guo, Zhiwen Yu
    http://arxiv.org/abs/1905.08948v1

    • [cs.IR]ANTIQUE: A Non-Factoid Question Answering Benchmark
    Helia Hashemi, Mohammad Aliannejadi, Hamed Zamani, W. Bruce Croft
    http://arxiv.org/abs/1905.08957v1

    • [cs.IR]Deeper Text Understanding for IR with Contextual Neural Language Modeling
    Zhuyun Dai, Jamie Callan
    http://arxiv.org/abs/1905.09217v1

    • [cs.IR]Practice on Long Sequential User Behavior Modeling for Click-Through Rate Prediction
    Qi Pi, Weijie Bian, Guorui Zhou, Xiaoqiang Zhu, Kun Gai
    http://arxiv.org/abs/1905.09248v1

    • [cs.IR]Retrieving Multi-Entity Associations: An Evaluation of Combination Modes for Word Embeddings
    Gloria Feher, Andreas Spitz, Michael Gertz
    http://arxiv.org/abs/1905.09052v1

    • [cs.IT]Cocktail Intra-Symbol-Codes: Exceeding the Channel Limit of QPSK Input
    Bingli Jiao, Mingxi Yin, Yuli Yang
    http://arxiv.org/abs/1905.08915v1

    • [cs.IT]Codebooks from generalized bent $\mathbb{Z}_4$-valued quadratic forms
    Yanfeng Qi, Sihem Mesnager, Chunming Tang
    http://arxiv.org/abs/1905.08834v1

    • [cs.IT]LAGC: Lazily Aggregated Gradient Coding for Straggler-Tolerant and Communication-Efficient Distributed Learning
    Jingjing Zhang, Osvaldo Simeone
    http://arxiv.org/abs/1905.09148v1

    • [cs.IT]On the Restricted Isometry Property of Centered Self Khatri-Rao Products
    Alexander Fengler, Peter Jung
    http://arxiv.org/abs/1905.09245v1

    • [cs.IT]Opportunistic Temporal Fair Mode Selection and User Scheduling for Full-duplex Systems
    Shahram Shahsavari, Farhad Shirani, Mohammad A Khojastepour, Elza Erkip
    http://arxiv.org/abs/1905.08992v1

    • [cs.LG]A framework for the extraction of Deep Neural Networks by leveraging public data
    Soham Pal, Yash Gupta, Aditya Shukla, Aditya Kanade, Shirish Shevade, Vinod Ganapathy
    http://arxiv.org/abs/1905.09165v1

    • [cs.LG]Adaptive Model Selection Framework: An Application to Airline Pricing
    Naman Shukla, Arinbjörn Kolbeinsson, Lavanya Marla, Kartik Yellepeddi
    http://arxiv.org/abs/1905.08874v1

    • [cs.LG]Augmenting Physiological Time Series Data: A Case Study for Sleep Apnea Detection
    Konstantinos Nikolaidis, Stein Kristiansen, Vera Goebel, Thomas Plagemann, Knut Liestøl, Mohan Kankanhalli
    http://arxiv.org/abs/1905.09068v1

    • [cs.LG]Blind identification of stochastic block models from dynamical observations
    Michael T. Schaub, Santiago Segarra, John N. Tsitsiklis
    http://arxiv.org/abs/1905.09107v1

    • [cs.LG]COBRA: Data-Efficient Model-Based RL through Unsupervised Object Discovery and Curiosity-Driven Exploration
    Nicholas Watters, Loic Matthey, Matko Bosnjak, Christopher P. Burgess, Alexander Lerchner
    http://arxiv.org/abs/1905.09275v1

    • [cs.LG]Convergence and Margin of Adversarial Training on Separable Data
    Zachary Charles, Shashank Rajput, Stephen Wright, Dimitris Papailiopoulos
    http://arxiv.org/abs/1905.09209v1

    • [cs.LG]Deep Reinforcement Learning for Detecting Malicious Websites
    Moitrayee Chatterjee, Akbar Siami Namin
    http://arxiv.org/abs/1905.09207v1

    • [cs.LG]Detecting Adversarial Examples and Other Misclassifications in Neural Networks by Introspection
    Jonathan Aigrain, Marcin Detyniecki
    http://arxiv.org/abs/1905.09186v1

    • [cs.LG]Discovering Hidden Structure in High Dimensional Human Behavioral Data via Tensor Factorization
    Homa Hosseinmardi, Hsien-Te Kao, Kristina Lerman, Emilio Ferrara
    http://arxiv.org/abs/1905.08846v1

    • [cs.LG]Ellipsoidal Trust Region Methods and the Marginal Value of Hessian Information for Neural Network Training
    Leonard Adolphs, Jonas Kohler, Aurelien Lucchi
    http://arxiv.org/abs/1905.09201v1

    • [cs.LG]Enhancing Domain Word Embedding via Latent Semantic Imputation
    Shibo Yao, Dantong Yu, Keli Xiao
    http://arxiv.org/abs/1905.08900v1

    • [cs.LG]Estimating Node Importance in Knowledge Graphs Using Graph Neural Networks
    Namyong Park, Andrey Kan, Xin Luna Dong, Tong Zhao, Christos Faloutsos
    http://arxiv.org/abs/1905.08865v1

    • [cs.LG]Evaluating recommender systems for AI-driven data science
    William La Cava, Heather Williams, Weixuan Fu, Jason H. Moore
    http://arxiv.org/abs/1905.09205v1

    • [cs.LG]Explainable Machine Learning for Scientific Insights and Discoveries
    Ribana Roscher, Bastian Bohn, Marco F. Duarte, Jochen Garcke
    http://arxiv.org/abs/1905.08883v1

    • [cs.LG]Fine-grained Optimization of Deep Neural Networks
    Mete Ozay
    http://arxiv.org/abs/1905.09054v1

    • [cs.LG]Hierarchical Reinforcement Learning for Quadruped Locomotion
    Deepali Jain, Atil Iscen, Ken Caluwaerts
    http://arxiv.org/abs/1905.08926v1

    • [cs.LG]Joint Information Preservation for Heterogeneous Domain Adaptation
    Peng Xu, Zhaohong Deng, Kup-Sze Choi, Jun Wang, Shitong Wang
    http://arxiv.org/abs/1905.08924v1

    • [cs.LG]Learning Networked Exponential Families with Network Lasso
    Alexander Jung
    http://arxiv.org/abs/1905.09056v1

    • [cs.LG]Learning Robust Options by Conditional Value at Risk Optimization
    Takuya Hiraoka, Takahisa Imagawa, Tatsuya Mori, Takashi Onishi, Yoshimasa Tsuruoka
    http://arxiv.org/abs/1905.09191v1

    • [cs.LG]Learning to Confuse: Generating Training Time Adversarial Data with Auto-Encoder
    Ji Feng, Qi-Zhi Cai, Zhi-Hua Zhou
    http://arxiv.org/abs/1905.09027v1

    • [cs.LG]Maximum Entropy-Regularized Multi-Goal Reinforcement Learning
    Rui Zhao, Xudong Sun, Volker Tresp
    http://arxiv.org/abs/1905.08786v1

    • [cs.LG]Measuring the effects of confounders in medical supervised classification problems: the Confounding Index (CI)
    Elisa Ferrari, Alessandra Retico, Davide Bacciu
    http://arxiv.org/abs/1905.08871v1

    • [cs.LG]Multi-Task Kernel Null-Space for One-Class Classification
    Shervin Rahimzadeh Arashloo, Josef Kittler
    http://arxiv.org/abs/1905.09173v1

    • [cs.LG]Optimal Decision Making Under Strategic Behavior
    Moein Khajehnejad, Behzad Tabibian, Bernhard Schölkopf, Adish Singla, Manuel Gomez-Rodriguez
    http://arxiv.org/abs/1905.09239v1

    • [cs.LG]Recurring Concept Meta-learning for Evolving Data Streams
    Robert Anderson, Yun Sing Koh, Gillian Dobbie, Albert Bifet
    http://arxiv.org/abs/1905.08848v1

    • [cs.LG]Revisiting hard thresholding for DNN pruning
    Konstantinos Pitas, Mike Davies, Pierre Vandergheynst
    http://arxiv.org/abs/1905.08793v1

    • [cs.LG]Scalable Gromov-Wasserstein Learning for Graph Partitioning and Matching
    Hongteng Xu, Dixin Luo, Lawrence Carin
    http://arxiv.org/abs/1905.07645v2

    • [cs.LG]Simulation and Augmentation of Social Networks for Building Deep Learning Models
    Akanda Wahid -Ul- Ashraf, Marcin Budka, Katarzyna Musial
    http://arxiv.org/abs/1905.09087v1

    • [cs.LG]Thresholding Graph Bandits with GrAPL
    Daniel LeJeune, Gautam Dasarathy, Richard G. Baraniuk
    http://arxiv.org/abs/1905.09190v1

    • [cs.LG]Time-Smoothed Gradients for Online Forecasting
    Tianhao Zhu, Sergul Aydore
    http://arxiv.org/abs/1905.08850v1

    • [cs.LG]Unsupervised Linear and Nonlinear Channel Equalization and Decoding using Variational Autoencoders
    Avi Caciularu, David Burshtein
    http://arxiv.org/abs/1905.08795v1

    • [cs.LO]A Note on Reasoning on $\textit{DL-Lite}_{\cal R}$ with Defeasibility
    Loris Bozzato, Thomas Eiter, Luciano Serafini
    http://arxiv.org/abs/1905.09221v1

    • [cs.LO]Properties and Extensions of Alternating Path Relevance - I
    David A. Plaisted
    http://arxiv.org/abs/1905.08842v1

    • [cs.MA]Asynchronous Scattering
    Ulysse Léchine, Sébastien Tixeuil
    http://arxiv.org/abs/1905.09177v1

    • [cs.NE]Evolving neural networks to follow trajectories of arbitrary complexity
    Benjamin Inden, Jürgen Jost
    http://arxiv.org/abs/1905.08885v1

    • [cs.RO]A Deep Reinforcement Learning Driving Policy for Autonomous Road Vehicles
    Konstantinos Makantasis, Maria Kontorinaki, Ioannis Nikolos
    http://arxiv.org/abs/1905.09046v1

    • [cs.RO]Automated shapeshifting for function recovery in damaged robots
    Sam Kriegman, Stephanie Walker, Dylan Shah, Michael Levin, Rebecca Kramer-Bottiglio, Josh Bongard
    http://arxiv.org/abs/1905.09264v1

    • [cs.RO]Globally Optimal Joint Search of Topology and Trajectory for Planar Linkages
    Zherong Pan, Min Liu, Xifeng Gao, Kai Xu, Dinesh Manocha
    http://arxiv.org/abs/1905.08956v1

    • [cs.RO]Practical Robot Learning from Demonstrations using Deep End-to-End Training
    Akansel Cosgun, Thomas Rowntree, Ian Reid, Tom Drummond
    http://arxiv.org/abs/1905.09025v1

    • [cs.RO]The Problem of Adhesion Methods and Locomotion Mechanism Development for Wall-Climbing Robots
    Nataly S. Vlasova, Nikita V. Bykov
    http://arxiv.org/abs/1905.09214v1

    • [cs.RO]ZJUNlict Extended Team Description Paper for RoboCup 2019
    Zheyuan Huang, Lingyun Chen, Jiacheng Li, Yunkai Wang, Zexi Chen, Licheng Wen, Jianyang Gu, Peng Hu, Rong Xiong
    http://arxiv.org/abs/1905.09157v1

    • [cs.SE]The Machine Learning Bazaar: Harnessing the ML Ecosystem for Effective System Development
    Micah J. Smith, Carles Sala, James Max Kanter, Kalyan Veeramachaneni
    http://arxiv.org/abs/1905.08942v1

    • [cs.SI]Demographic Differentials in Facebook Usage Around the World
    Sofia Gil-Clavel, Emilio Zagheni
    http://arxiv.org/abs/1905.09105v1

    • [cs.SI]IdeoTrace: A Framework for Ideology Tracing with a Case Study on the 2016 U.S. Presidential Election
    Indu Manickam, Andrew S. Lan, Gautam Dasarathy, Richard G. Baraniuk
    http://arxiv.org/abs/1905.08831v1

    • [econ.TH]Cheating in Ranking Systems
    Lihi Dery, Dror Hermel, Artyom Jelnov
    http://arxiv.org/abs/1905.09116v1

    • [eess.IV]Automated Pupillary Light Reflex Test on a Portable Platform
    Dogancan Temel, Melvin J. Mathew, Ghassan AlRegib, Yousuf M. Khalifa
    http://arxiv.org/abs/1905.08886v1

    • [eess.SP]Latency Analysis for Sequential Detection in Low-Complexity Binary Radio Systems
    Manuel S. Stein, Michael Fauß
    http://arxiv.org/abs/1905.08749v2

    • [eess.SP]MIST: A Novel Training Strategy for Low-latencyScalable Neural Net Decoders
    Kumar Yashashwi, Deepak Anand, Sibi Raj B Pillai, Prasanna Chaporkar, K Ganesh
    http://arxiv.org/abs/1905.08990v1

    • [eess.SP]Source Localization and Tracking for Dynamic Radio Cartography using Directional Antennas
    Mohsen Joneidi, Hassan Yazdani, Azadeh Vosoughi, Nazanin Rahnavard
    http://arxiv.org/abs/1905.08869v1

    • [math.NA]Heavy Hitters and Bernoulli Convolutions
    Alexander Kushkuley
    http://arxiv.org/abs/1905.08930v1

    • [math.OC]Beyond Alternating Updates for Matrix Factorization with Inertial Bregman Proximal Gradient Algorithms
    Mahesh Chandra Mukkamala, Peter Ochs
    http://arxiv.org/abs/1905.09050v1

    • [math.ST]On LSE in regression model for long-range dependent random fields on spheres
    Vo Anh, Andriy Olenko, Volodymyr Vaskovych
    http://arxiv.org/abs/1905.09123v1

    • [math.ST]Super-Consistent Estimation of Points of Impact in Nonparametric Regression with Functional Predictors
    Dominik Poß, Dominik Liebl, Alois Kneip, Hedwig Eisenbarth, Tor D. Wager, Lisa Feldman Barrett
    http://arxiv.org/abs/1905.09021v1

    • [physics.soc-ph]Optimal interlayer structure for information spreading on multilayer networks
    Liming Pan, Wei Wang, Shimin Cai, Tao Zhou
    http://arxiv.org/abs/1905.09176v1

    • [q-bio.QM]Selection of a Minimal Number of Significant Porcine SNPs by an Information Gain and Genetic Algorithm Hybrid Model
    Wanthanee Rathasamuth, Kitsuchart Pasupa, Sissades Tongsima
    http://arxiv.org/abs/1905.09059v1

    • [stat.AP]A stochastic model for the lifecycle and track of extreme extratropical cyclones in the North Atlantic
    Paul Sharkey, Jonathan A. Tawn, Simon J. Brown
    http://arxiv.org/abs/1905.08840v1

    • [stat.AP]Measuring Average Treatment Effect from Heavy-tailed Data
    Jason, Wang, Pauline Burke
    http://arxiv.org/abs/1905.09252v1

    • [stat.AP]The perils of automated fitting of datasets: the case of a wind turbine cost model
    Claude Klöckl, Katharina Gruber, Peter Regner, Sebastian Wehrle, Johannes Schmidt
    http://arxiv.org/abs/1905.08870v1

    • [stat.CO]Application of the interacting particle system method to piecewise deterministic Markov processes used in reliability
    H. Chraibi, A. Dutfoy, T. Galtier, J. Garnier
    http://arxiv.org/abs/1905.09044v1

    • [stat.ML]Distributionally Robust Formulation and Model Selection for the Graphical Lasso
    Pedro Cisneros-Velarde, Sang-Yun Oh, Alexander Petersen
    http://arxiv.org/abs/1905.08975v1

    • [stat.ML]On the minimax optimality and superiority of deep neural network learning over sparse parameter spaces
    Satoshi Hayakawa, Taiji Suzuki
    http://arxiv.org/abs/1905.09195v1

    • [stat.ML]Survival Function Matching for Calibrated Time-to-Event Predictions
    Paidamoyo Chapfuwa, Chenyang Tao, Lawrence Carin, Ricardo Henao
    http://arxiv.org/abs/1905.08838v1