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.GR - 计算机图形学 cs.HC - 人机接口 cs.IR - 信息检索 cs.IT - 信息论 cs.LG - 自动学习 cs.MS - 数学软件 cs.NE - 神经与进化计算 cs.NI - 网络和互联网体系结构 cs.RO - 机器人学 cs.SE - 软件工程 cs.SI - 社交网络与信息网络 eess.IV - 图像与视频处理 math.OC - 优化与控制 math.ST - 统计理论 physics.chem-ph -化学物理 physics.soc-ph - 物理学与社会 q-bio.GN - 基因组学 q-bio.NC - 神经元与认知 q-bio.TO - 组织和器官 q-fin.ST - 统计金融学 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

    • [cs.AI]Ethics of Artificial Intelligence Demarcations
    • [cs.CL]Empirical Evaluation of Leveraging Named Entities for Arabic Sentiment Analysis
    • [cs.CL]GumDrop at the DISRPT2019 Shared Task: A Model Stacking Approach to Discourse Unit Segmentation and Connective Detection
    • [cs.CL]Multi-Task Learning for Argumentation Mining
    • [cs.CR]Android Malicious Application Classification Using Clustering
    • [cs.CR]Flexible Byzantine Fault Tolerance
    • [cs.CV]A Large RGB-D Dataset for Semi-supervised Monocular Depth Estimation
    • [cs.CV]A Novel Multi-layer Framework for Tiny Obstacle Discovery
    • [cs.CV]Adaptive Transform Domain Image Super-resolution Via Orthogonally Regularized Deep Networks
    • [cs.CV]Attention-guided Network for Ghost-free High Dynamic Range Imaging
    • [cs.CV]BIT: Biologically Inspired Tracker
    • [cs.CV]DenseNet Models for Tiny ImageNet Classification
    • [cs.CV]DirectShape: Photometric Alignment of Shape Priors for Visual Vehicle Pose and Shape Estimation
    • [cs.CV]Free-form Video Inpainting with 3D Gated Convolution and Temporal PatchGAN
    • [cs.CV]High-frequency crowd insights for public safety and congestion control
    • [cs.CV]Interpretable and Generalizable Deep Image Matching with Adaptive Convolutions
    • [cs.CV]LBS Autoencoder: Self-supervised Fitting of Articulated Meshes to Point Clouds
    • [cs.CV]Learning Actor Relation Graphs for Group Activity Recognition
    • [cs.CV]Learning Feature-to-Feature Translator by Alternating Back-Propagation for Zero-Shot Learning
    • [cs.CV]Linked Dynamic Graph CNN: Learning on Point Cloud via Linking Hierarchical Features
    • [cs.CV]Lung Nodule Classification using Deep Local-Global Networks
    • [cs.CV]Minimizing Perceived Image Quality Loss Through Adversarial Attack Scoping
    • [cs.CV]Orientation Aware Object Detection with Application to Firearms
    • [cs.CV]Path-Restore: Learning Network Path Selection for Image Restoration
    • [cs.CV]Privacy Preserving Group Membership Verification and Identification
    • [cs.CV]RERERE: Remote Embodied Referring Expressions in Real indoor Environments
    • [cs.CV]Siamese Attentional Keypoint Network for High Performance Visual Tracking
    • [cs.CV]Student Becoming the Master: Knowledge Amalgamation for Joint Scene Parsing, Depth Estimation, and More
    • [cs.CV]Tertiary Eye Movement Classification by a Hybrid Algorithm
    • [cs.CV]Transferable Semi-supervised 3D Object Detection from RGB-D Data
    • [cs.CV]UDFNet: Unsupervised Disparity Fusion with Adversarial Networks
    • [cs.CV]Using Videos to Evaluate Image Model Robustness
    • [cs.CV]VITAMIN-E: VIsual Tracking And MappINg with Extremely Dense Feature Points
    • [cs.CY]Migration patterns under different scenarios of sea level rise
    • [cs.CY]The Profiling Potential of Computer Vision and the Challenge of Computational Empiricism
    • [cs.DC]Characterizing Application Scheduling on Edge, Fog and Cloud Computing Resources
    • [cs.DC]Detection of Silent Data Corruptions in Smoothed Particle Hydrodynamics Simulations
    • [cs.DC]IOArbiter: Dynamic Provisioning of Backend Block Storage in the Cloud
    • [cs.DC]Pangolin: A Fault-Tolerant Persistent Memory Programming Library
    • [cs.DC]Structural Self-adaptation for Decentralized Pervasive Intelligence
    • [cs.GR]Multi-modal 3D Shape Reconstruction Under Calibration Uncertainty using Parametric Level Set Methods
    • [cs.HC]Drishtikon: An advanced navigational aid system for visually impaired people
    • [cs.HC]HAUAR: Home Automation Using Action Recognition
    • [cs.IR]A Neural Influence Diffusion Model for Social Recommendation
    • [cs.IR]Optimizing Search API Queries for Twitter Topic Classifiers Using a Maximum Set Coverage Approach
    • [cs.IR]Sequential modeling of Sessions using Recurrent Neural Networks for Skip Prediction
    • [cs.IT]A Refined Scaling Law for Spatially Coupled LDPC Codes Over the Binary Erasure Channel
    • [cs.IT]Age of Information for Updates with Distortion
    • [cs.IT]Compression is Comprehension, and the Unreasonable Effectiveness of Digital Computation in the Natural World
    • [cs.IT]Enabling Large Intelligent Surfaces with Compressive Sensing and Deep Learning
    • [cs.IT]Fully-/Partially-Connected Hybrid Beamforming Architectures for mmWave MU-MIMO
    • [cs.IT]Phase Retrieval for Binary Signals: Box Relaxation and Uniqueness
    • [cs.IT]Saddlepoint Approximations for Rayleigh Block-Fading Channels
    • [cs.IT]Steane-Enlargement of Quantum Codes from the Hermitian Curve
    • [cs.IT]Sublinear-Time Non-Adaptive Group Testing with $O(k \log n)$ Tests via Bit-Mixing Coding
    • [cs.LG]Bounds in Query Learning
    • [cs.LG]CPM-sensitive AUC for CTR prediction
    • [cs.LG]DAG-GNN: DAG Structure Learning with Graph Neural Networks
    • [cs.LG]Distributed Differentially Private Computation of Functions with Correlated Noise
    • [cs.LG]End-to-end Sleep Staging with Raw Single Channel EEG using Deep Residual ConvNets
    • [cs.LG]Exploring Graph Learning for Semi-Supervised Classification Beyond Euclidean Data
    • [cs.LG]Gender specific and Age dependent classification model for improved diagnosis in Parkinson’s disease
    • [cs.LG]Generated Loss, Augmented Training, and Multiscale VAE
    • [cs.LG]GraphNAS: Graph Neural Architecture Search with Reinforcement Learning
    • [cs.LG]Identifying cross country skiing techniques using power meters in ski poles
    • [cs.LG]Improving benchmarks for autonomous vehicles testing using synthetically generated images
    • [cs.LG]Latent Variable Algorithms for Multimodal Learning and Sensor Fusion
    • [cs.LG]Learning Feature Sparse Principal Components
    • [cs.LG]Multiview Hessian Regularization for Image Annotation
    • [cs.LG]Non-Stationary Markov Decision Processes a Worst-Case Approach using Model-Based Reinforcement Learning
    • [cs.LG]Non-convex Penalty for Tensor Completion and Robust PCA
    • [cs.LG]Quaternion Knowledge Graph Embedding
    • [cs.LG]Read classification using semi-supervised deep learning
    • [cs.LG]Relevant feature extraction for statistical inference
    • [cs.LG]Semi-Cyclic Stochastic Gradient Descent
    • [cs.LG]Spatio-temporal crop classification of low-resolution satellite imagery with capsule layers and distributed attention
    • [cs.LG]Statistical Learning and Estimation of Piano Fingering
    • [cs.LG]Stochastic Primal-Dual Algorithms with Faster Convergence than $O(1/\sqrt{T})$ for Problems without Bilinear Structure
    • [cs.LG]The MineRL Competition on Sample Efficient Reinforcement Learning using Human Priors
    • [cs.LG]Wasserstein-Fisher-Rao Document Distance
    • [cs.MS]A Flexible Framework for Parallel Multi-Dimensional DFTs
    • [cs.MS]Big Math and the One-Brain Barrier A Position Paper and Architecture Proposal
    • [cs.NE]Improving Differentiable Neural Computers Through Memory Masking, De-allocation, and Link Distribution Sharpness Control
    • [cs.NI]Analysis and Visualization of Deep Neural Networks in Device-Free Wi-Fi Indoor Localization
    • [cs.NI]Benefits of Coding on Age of Information in Broadcast Networks
    • [cs.NI]Discharged Payment Channels: Quantifying the Lightning Network’s Resilience to Topology-Based Attacks
    • [cs.RO]Bold Hearts Team Description for RoboCup 2019 (Humanoid Kid Size League)
    • [cs.RO]Driving Decision and Control for Autonomous Lane Change based on Deep Reinforcement Learning
    • [cs.RO]Estimating Forces of Robotic Pouring Using a LSTM RNN
    • [cs.RO]Estimating Risk Levels of Driving Scenarios through Analysis of Driving Styles for Autonomous Vehicles
    • [cs.RO]Monte-Carlo Tree Search for Efficient Visually Guided Rearrangement Planning
    • [cs.SE]Risk Structures: Towards Engineering Risk-aware Autonomous Systems
    • [cs.SI]Link Prediction in Multiplex Networks based on Interlayer Similarity
    • [eess.IV]A new Edge Detector Based on Parametric Surface Model: Regression Surface Descriptor
    • [eess.IV]Reducing the Hausdorff Distance in Medical Image Segmentation with Convolutional Neural Networks
    • [math.OC]Low-rank matrix recovery with composite optimization: good conditioning and rapid convergence
    • [math.ST]On the Kullback-Leibler divergence between location-scale densities
    • [math.ST]Seasonal FIEGARCH Processes
    • [physics.chem-ph]A survey on Big Data and Machine Learning for Chemistry
    • [physics.soc-ph]Identification of intrinsic long-range degree correlations in complex networks
    • [q-bio.GN]MinCall - MinION end2end convolutional deep learning basecaller
    • [q-bio.NC]Spike-Based Winner-Take-All Computation: Fundamental Limits and Order-Optimal Circuits
    • [q-bio.TO]Can fallopian tube anatomy predict pregnancy and pregnancy outcomes after tubal reversal surgery?
    • [q-fin.ST]Copula estimation for nonsynchronous financial data
    • [quant-ph]$α$-Logarithmic negativity
    • [quant-ph]Quantum boomerang capacity
    • [stat.AP]Amazon Forest Fires Between 2001 and 2006 and Birth Weight in Porto Velho
    • [stat.AP]How Many Customers Does a Retail Chain Have?
    • [stat.AP]Identifying Precipitation Regimes in China Using Model-Based Clustering of Spatial Functional Data
    • [stat.CO]ssMousetrack: Analysing computerized tracking data via Bayesian state-space models in {R}
    • [stat.ME]A distribution-free smoothed combination method of biomarkers to improve diagnostic accuracy in multi-category classification
    • [stat.ME]Deep Learning for Survival Outcomes
    • [stat.ME]Exponential Random Graph models for Little Networks
    • [stat.ME]From one environment to many: The problem of replicability of statistical inferences
    • [stat.ME]Heterofusion: Fusing genomics data of different measurement scales
    • [stat.ME]Model based functional clustering of varved lake sediments
    • [stat.ML]Regression-Enhanced Random Forests

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

    • [cs.AI]Ethics of Artificial Intelligence Demarcations
    Anders Braarud Hanssen, Stefano Nichele
    http://arxiv.org/abs/1904.10239v1

    • [cs.CL]Empirical Evaluation of Leveraging Named Entities for Arabic Sentiment Analysis
    Hala Mulki, Hatem Haddad, Mourad Gridach, Ismail Babaoglu
    http://arxiv.org/abs/1904.10195v1

    • [cs.CL]GumDrop at the DISRPT2019 Shared Task: A Model Stacking Approach to Discourse Unit Segmentation and Connective Detection
    Yue Yu, Yilun Zhu, Yang Liu, Yan Liu, Siyao Peng, Mackenzie Gong, Amir Zeldes
    http://arxiv.org/abs/1904.10419v1

    • [cs.CL]Multi-Task Learning for Argumentation Mining
    Tobias Kahse
    http://arxiv.org/abs/1904.10162v1

    • [cs.CR]Android Malicious Application Classification Using Clustering
    Hemant Rathore, Sanjay K. Sahay, Palash Chaturvedi, Mohit Sewak
    http://arxiv.org/abs/1904.10142v1

    • [cs.CR]Flexible Byzantine Fault Tolerance
    Dahlia Malkhi, Kartik Nayak, Ling Ren
    http://arxiv.org/abs/1904.10067v1

    • [cs.CV]A Large RGB-D Dataset for Semi-supervised Monocular Depth Estimation
    Jaehoon Cho, Dongbo Min, Youngjung Kim, Kwanghoon Sohn
    http://arxiv.org/abs/1904.10230v1

    • [cs.CV]A Novel Multi-layer Framework for Tiny Obstacle Discovery
    Feng Xue, Anlong Ming, Menghan Zhou, Yu Zhou
    http://arxiv.org/abs/1904.10161v1

    • [cs.CV]Adaptive Transform Domain Image Super-resolution Via Orthogonally Regularized Deep Networks
    Tiantong Guo, Hojjat S. Mousavi, Vishal Monga
    http://arxiv.org/abs/1904.10082v1

    • [cs.CV]Attention-guided Network for Ghost-free High Dynamic Range Imaging
    Qingsen Yan, Dong Gong, Qinfeng Shi, Anton van den Hengel, Chunhua Shen, Ian Reid, Yanning Zhang
    http://arxiv.org/abs/1904.10293v1

    • [cs.CV]BIT: Biologically Inspired Tracker
    Bolun Cai, Xiangmin Xu, Xiaofen Xing, Kui Jia, Jie Miao, Dacheng Tao
    http://arxiv.org/abs/1904.10411v1

    • [cs.CV]DenseNet Models for Tiny ImageNet Classification
    Zoheb Abai, Nishad Rajmalwar
    http://arxiv.org/abs/1904.10429v1

    • [cs.CV]DirectShape: Photometric Alignment of Shape Priors for Visual Vehicle Pose and Shape Estimation
    Rui Wang, Nan Yang, Joerg Stueckler, Daniel Cremers
    http://arxiv.org/abs/1904.10097v1

    • [cs.CV]Free-form Video Inpainting with 3D Gated Convolution and Temporal PatchGAN
    Ya-Liang Chang, Zhe Yu Liu, Winston Hsu
    http://arxiv.org/abs/1904.10247v1

    • [cs.CV]High-frequency crowd insights for public safety and congestion control
    Karthik Nandakumar, Sebastien Blandin, Laura Wynter
    http://arxiv.org/abs/1904.10180v1

    • [cs.CV]Interpretable and Generalizable Deep Image Matching with Adaptive Convolutions
    Shengcai Liao, Ling Shao
    http://arxiv.org/abs/1904.10424v1

    • [cs.CV]LBS Autoencoder: Self-supervised Fitting of Articulated Meshes to Point Clouds
    Chun-Liang Li, Tomas Simon, Jason Saragih, Barnabás Póczos, Yaser Sheikh
    http://arxiv.org/abs/1904.10037v1

    • [cs.CV]Learning Actor Relation Graphs for Group Activity Recognition
    Jianchao Wu, Limin Wang, Li Wang, Jie Guo, Gangshan Wu
    http://arxiv.org/abs/1904.10117v1

    • [cs.CV]Learning Feature-to-Feature Translator by Alternating Back-Propagation for Zero-Shot Learning
    Yizhe Zhu, Jianwen Xie, Bingchen Liu, Ahmed Elgammal
    http://arxiv.org/abs/1904.10056v1

    • [cs.CV]Linked Dynamic Graph CNN: Learning on Point Cloud via Linking Hierarchical Features
    Kuangen Zhang, Ming Hao, Jing Wang, Clarence W. de Silva, Chenglong Fu
    http://arxiv.org/abs/1904.10014v1

    • [cs.CV]Lung Nodule Classification using Deep Local-Global Networks
    Mundher Al-Shabi, Boon Leong Lan, Wai Yee Chan, Kwan-Hoong Ng, Maxine Tan
    http://arxiv.org/abs/1904.10126v1

    • [cs.CV]Minimizing Perceived Image Quality Loss Through Adversarial Attack Scoping
    Kostiantyn Khabarlak, Larysa Koriashkina
    http://arxiv.org/abs/1904.10390v1

    • [cs.CV]Orientation Aware Object Detection with Application to Firearms
    Javed Iqbal, Muhammad Akhtar Munir, Arif Mahmood, Afsheen Rafaqat Ali, Mohsen Ali
    http://arxiv.org/abs/1904.10032v1

    • [cs.CV]Path-Restore: Learning Network Path Selection for Image Restoration
    Ke Yu, Xintao Wang, Chao Dong, Xiaoou Tang, Chen Change Loy
    http://arxiv.org/abs/1904.10343v1

    • [cs.CV]Privacy Preserving Group Membership Verification and Identification
    Marzieh Gheisari, Teddy Furon, Laurent Amsaleg
    http://arxiv.org/abs/1904.10327v1

    • [cs.CV]RERERE: Remote Embodied Referring Expressions in Real indoor Environments
    Yuankai Qi, Qi Wu, Peter Anderson, Marco Liu, Chunhua Shen, Anton van den Hengel
    http://arxiv.org/abs/1904.10151v1

    • [cs.CV]Siamese Attentional Keypoint Network for High Performance Visual Tracking
    Peng Gao, Yipeng Ma, Ruyue Yuan, Liyi Xiao, Fei Wang
    http://arxiv.org/abs/1904.10128v1

    • [cs.CV]Student Becoming the Master: Knowledge Amalgamation for Joint Scene Parsing, Depth Estimation, and More
    Jingwen Ye, Yixin Ji, Xinchao Wang, Kairi Ou, Dapeng Tao, Mingli Song
    http://arxiv.org/abs/1904.10167v1

    • [cs.CV]Tertiary Eye Movement Classification by a Hybrid Algorithm
    Samuel-Hunter Berndt, Douglas Kirkpatrick, Timothy Taviano, Oleg Komogortsev
    http://arxiv.org/abs/1904.10085v1

    • [cs.CV]Transferable Semi-supervised 3D Object Detection from RGB-D Data
    Yew Siang Tang, Gim Hee Lee
    http://arxiv.org/abs/1904.10300v1

    • [cs.CV]UDFNet: Unsupervised Disparity Fusion with Adversarial Networks
    Can Pu, Robert B. Fisher
    http://arxiv.org/abs/1904.10044v1

    • [cs.CV]Using Videos to Evaluate Image Model Robustness
    Keren Gu, Brandon Yang, Jiquan Ngiam, Quoc Le, Jonathan Shlens
    http://arxiv.org/abs/1904.10076v1

    • [cs.CV]VITAMIN-E: VIsual Tracking And MappINg with Extremely Dense Feature Points
    Masashi Yokozuka, Shuji Oishi, Thompson Simon, Atsuhiko Banno
    http://arxiv.org/abs/1904.10324v1

    • [cs.CY]Migration patterns under different scenarios of sea level rise
    Caleb Robinson, Bistra Dilkina, Juan Moreno-Cruz
    http://arxiv.org/abs/1904.10160v1

    • [cs.CY]The Profiling Potential of Computer Vision and the Challenge of Computational Empiricism
    Jake Goldenfein
    http://arxiv.org/abs/1904.10016v1

    • [cs.DC]Characterizing Application Scheduling on Edge, Fog and Cloud Computing Resources
    Prateeksha Varshney, Yogesh Simmhan
    http://arxiv.org/abs/1904.10125v1

    • [cs.DC]Detection of Silent Data Corruptions in Smoothed Particle Hydrodynamics Simulations
    Aurélien Cavelan, Rubén M. Cabezón, Florina M. Ciorba
    http://arxiv.org/abs/1904.10221v1

    • [cs.DC]IOArbiter: Dynamic Provisioning of Backend Block Storage in the Cloud
    Moo-Ryong Ra, Hee Won Lee
    http://arxiv.org/abs/1904.09984v1

    • [cs.DC]Pangolin: A Fault-Tolerant Persistent Memory Programming Library
    Lu Zhang, Steven Swanson
    http://arxiv.org/abs/1904.10083v1

    • [cs.DC]Structural Self-adaptation for Decentralized Pervasive Intelligence
    Jovan Nikolic, Evangelos Pournaras
    http://arxiv.org/abs/1904.09681v2

    • [cs.GR]Multi-modal 3D Shape Reconstruction Under Calibration Uncertainty using Parametric Level Set Methods
    Moshe Eliasof, Andrei Sharf, Eran Treister
    http://arxiv.org/abs/1904.10379v1

    • [cs.HC]Drishtikon: An advanced navigational aid system for visually impaired people
    Shashank Kotyan, Nishant Kumar, Pankaj Kumar Sahu, Venkanna Udutalapally
    http://arxiv.org/abs/1904.10351v1

    • [cs.HC]HAUAR: Home Automation Using Action Recognition
    Shashank Kotyan, Nishant Kumar, Pankaj Kumar Sahu, Venkanna Udutalapally
    http://arxiv.org/abs/1904.10354v1

    • [cs.IR]A Neural Influence Diffusion Model for Social Recommendation
    Le Wu, Peijie Sun, Yanjie Fu, Richang Hong, Xiting Wang, Meng Wang
    http://arxiv.org/abs/1904.10322v1

    • [cs.IR]Optimizing Search API Queries for Twitter Topic Classifiers Using a Maximum Set Coverage Approach
    Kasra Safari, Scott Sanner
    http://arxiv.org/abs/1904.10403v1

    • [cs.IR]Sequential modeling of Sessions using Recurrent Neural Networks for Skip Prediction
    Sainath Adapa
    http://arxiv.org/abs/1904.10273v1

    • [cs.IT]A Refined Scaling Law for Spatially Coupled LDPC Codes Over the Binary Erasure Channel
    Roman Sokolovskii, Fredrik Brännström, Alexandre Graell i Amat
    http://arxiv.org/abs/1904.10410v1

    • [cs.IT]Age of Information for Updates with Distortion
    Melih Bastopcu, Sennur Ulukus
    http://arxiv.org/abs/1904.10444v1

    • [cs.IT]Compression is Comprehension, and the Unreasonable Effectiveness of Digital Computation in the Natural World
    Hector Zenil
    http://arxiv.org/abs/1904.10258v1

    • [cs.IT]Enabling Large Intelligent Surfaces with Compressive Sensing and Deep Learning
    Abdelrahman Taha, Muhammad Alrabeiah, Ahmed Alkhateeb
    http://arxiv.org/abs/1904.10136v1

    • [cs.IT]Fully-/Partially-Connected Hybrid Beamforming Architectures for mmWave MU-MIMO
    Xiaoshen Song, Thomas Kühne, Giuseppe Caire
    http://arxiv.org/abs/1904.10276v1

    • [cs.IT]Phase Retrieval for Binary Signals: Box Relaxation and Uniqueness
    Wing Hong Wong, Yifei Lou, Tieyong Zeng
    http://arxiv.org/abs/1904.10157v1

    • [cs.IT]Saddlepoint Approximations for Rayleigh Block-Fading Channels
    Alejandro Lancho, Jöhan Ostman, Giuseppe Durisi, Tobias Koch, Gonzalo Vazquez-Vilar
    http://arxiv.org/abs/1904.10442v1

    • [cs.IT]Steane-Enlargement of Quantum Codes from the Hermitian Curve
    René Bødker Christensen, Olav Geil
    http://arxiv.org/abs/1904.10007v1

    • [cs.IT]Sublinear-Time Non-Adaptive Group Testing with $O(k \log n)$ Tests via Bit-Mixing Coding
    Steffen Bondorf, Binbin Chen, Jonathan Scarlett, Haifeng Yu, Yuda Zhao
    http://arxiv.org/abs/1904.10102v1

    • [cs.LG]Bounds in Query Learning
    Hunter Chase, James Freitag
    http://arxiv.org/abs/1904.10122v1

    • [cs.LG]CPM-sensitive AUC for CTR prediction
    Zhaocheng Liu, Guangxue Yin
    http://arxiv.org/abs/1904.10272v1

    • [cs.LG]DAG-GNN: DAG Structure Learning with Graph Neural Networks
    Yue Yu, Jie Chen, Tian Gao, Mo Yu
    http://arxiv.org/abs/1904.10098v1

    • [cs.LG]Distributed Differentially Private Computation of Functions with Correlated Noise
    Hafiz Imtiaz, Jafar Mohammadi, Anand D. Sarwate
    http://arxiv.org/abs/1904.10059v1

    • [cs.LG]End-to-end Sleep Staging with Raw Single Channel EEG using Deep Residual ConvNets
    Ahmed Imtiaz Humayun, Asif Shahriyar Sushmit, Taufiq Hasan, Mohammed Imamul Hassan Bhuiyan
    http://arxiv.org/abs/1904.10255v1

    • [cs.LG]Exploring Graph Learning for Semi-Supervised Classification Beyond Euclidean Data
    Xiang Gao, Wei Hu, Zongming Guo
    http://arxiv.org/abs/1904.10146v1

    • [cs.LG]Gender specific and Age dependent classification model for improved diagnosis in Parkinson’s disease
    Ujjwal Gupta, Hritik Bansal, Deepak Joshi
    http://arxiv.org/abs/1904.09651v2

    • [cs.LG]Generated Loss, Augmented Training, and Multiscale VAE
    Jason Chou, Gautam Hathi
    http://arxiv.org/abs/1904.10446v1

    • [cs.LG]GraphNAS: Graph Neural Architecture Search with Reinforcement Learning
    Yang Gao, Hong Yang, Peng Zhang, Chuan Zhou, Yue Hu
    http://arxiv.org/abs/1904.09981v1

    • [cs.LG]Identifying cross country skiing techniques using power meters in ski poles
    Moa Johansson, Marie Korneliusson, Nickey Lizbat Lawrence
    http://arxiv.org/abs/1904.10359v1

    • [cs.LG]Improving benchmarks for autonomous vehicles testing using synthetically generated images
    Aleksander Lukashou
    http://arxiv.org/abs/1904.10261v1

    • [cs.LG]Latent Variable Algorithms for Multimodal Learning and Sensor Fusion
    Lijiang Guo
    http://arxiv.org/abs/1904.10450v1

    • [cs.LG]Learning Feature Sparse Principal Components
    Lai Tian, Feiping Nie, Xuelong Li
    http://arxiv.org/abs/1904.10155v1

    • [cs.LG]Multiview Hessian Regularization for Image Annotation
    Weifeng Liu, Dacheng Tao
    http://arxiv.org/abs/1904.10100v1

    • [cs.LG]Non-Stationary Markov Decision Processes a Worst-Case Approach using Model-Based Reinforcement Learning
    Erwan Lecarpentier, Emmanuel Rachelson
    http://arxiv.org/abs/1904.10090v1

    • [cs.LG]Non-convex Penalty for Tensor Completion and Robust PCA
    Tao Li, Jinwen Ma
    http://arxiv.org/abs/1904.10165v1

    • [cs.LG]Quaternion Knowledge Graph Embedding
    Shuai Zhang, Yi Tay, Lina Yao, Qi Liu
    http://arxiv.org/abs/1904.10281v1

    • [cs.LG]Read classification using semi-supervised deep learning
    Tomislav Šebrek, Jan Tomljanović, Josip Krapac, Mile Šikić
    http://arxiv.org/abs/1904.10353v1

    • [cs.LG]Relevant feature extraction for statistical inference
    Cédric Bény
    http://arxiv.org/abs/1904.10387v1

    • [cs.LG]Semi-Cyclic Stochastic Gradient Descent
    Hubert Eichner, Tomer Koren, H. Brendan McMahan, Nathan Srebro, Kunal Talwar
    http://arxiv.org/abs/1904.10120v1

    • [cs.LG]Spatio-temporal crop classification of low-resolution satellite imagery with capsule layers and distributed attention
    John Brandt
    http://arxiv.org/abs/1904.10130v1

    • [cs.LG]Statistical Learning and Estimation of Piano Fingering
    Eita Nakamura, Yasuyuki Saito, Kazuyoshi Yoshii
    http://arxiv.org/abs/1904.10237v1

    • [cs.LG]Stochastic Primal-Dual Algorithms with Faster Convergence than $O(1/\sqrt{T})$ for Problems without Bilinear Structure
    Yan Yan, Yi Xu, Qihang Lin, Lijun Zhang, Tianbao Yang
    http://arxiv.org/abs/1904.10112v1

    • [cs.LG]The MineRL Competition on Sample Efficient Reinforcement Learning using Human Priors
    William H. Guss, Cayden Codel, Katja Hofmann, Brandon Houghton, Noboru Kuno, Stephanie Milani, Sharada Mohanty, Diego Perez Liebana, Ruslan Salakhutdinov, Nicholay Topin, Manuela Veloso, Phillip Wang
    http://arxiv.org/abs/1904.10079v1

    • [cs.LG]Wasserstein-Fisher-Rao Document Distance
    Zihao Wang, Datong Zhou, Yong Zhang, Hao Wu, Chenglong Bao
    http://arxiv.org/abs/1904.10294v1

    • [cs.MS]A Flexible Framework for Parallel Multi-Dimensional DFTs
    Doru Thom Popovici, Martin D. Schatz, Franz Franchetti, Tze Meng Low
    http://arxiv.org/abs/1904.10119v1

    • [cs.MS]Big Math and the One-Brain Barrier A Position Paper and Architecture Proposal
    Jacques Carette, William M. Farmer, Michael Kohlhase, Florian Rabe
    http://arxiv.org/abs/1904.10405v1

    • [cs.NE]Improving Differentiable Neural Computers Through Memory Masking, De-allocation, and Link Distribution Sharpness Control
    Róbert Csordás, Jürgen Schmidhuber
    http://arxiv.org/abs/1904.10278v1

    • [cs.NI]Analysis and Visualization of Deep Neural Networks in Device-Free Wi-Fi Indoor Localization
    Shing-Jiuan Liu, Ronald Y. Chang, Feng-Tsun Chien
    http://arxiv.org/abs/1904.10154v1

    • [cs.NI]Benefits of Coding on Age of Information in Broadcast Networks
    Xingran Chen, Shirin Saeedi Bidokhti
    http://arxiv.org/abs/1904.10077v1

    • [cs.NI]Discharged Payment Channels: Quantifying the Lightning Network’s Resilience to Topology-Based Attacks
    Elias Rohrer, Julian Malliaris, Florian Tschorsch
    http://arxiv.org/abs/1904.10253v1

    • [cs.RO]Bold Hearts Team Description for RoboCup 2019 (Humanoid Kid Size League)
    Marcus M. Scheunemann, Sander G. van Dijk, Rebecca Miko, Daniel Barry, George M. Evans, Alessandra Rossi, Daniel Polani
    http://arxiv.org/abs/1904.10066v1

    • [cs.RO]Driving Decision and Control for Autonomous Lane Change based on Deep Reinforcement Learning
    Tianyu Shi, Pin Wang, Xuxin Cheng, Ching-Yao Chan
    http://arxiv.org/abs/1904.10171v1

    • [cs.RO]Estimating Forces of Robotic Pouring Using a LSTM RNN
    Kyle Mott
    http://arxiv.org/abs/1904.09980v1

    • [cs.RO]Estimating Risk Levels of Driving Scenarios through Analysis of Driving Styles for Autonomous Vehicles
    Songlin Xu, Jiacheng Zhu
    http://arxiv.org/abs/1904.10176v1

    • [cs.RO]Monte-Carlo Tree Search for Efficient Visually Guided Rearrangement Planning
    Sergey Zagoruyko, Yann Labbé, Igor Kalevatykh, Ivan Laptev, Justin Carpentier, Mathieu Aubry, Josef Sivic
    http://arxiv.org/abs/1904.10348v1

    • [cs.SE]Risk Structures: Towards Engineering Risk-aware Autonomous Systems
    Mario Gleirscher
    http://arxiv.org/abs/1904.10386v1

    • [cs.SI]Link Prediction in Multiplex Networks based on Interlayer Similarity
    Shaghayegh Najari, Mostafa Salehi, Vahid Ranjbar, Mahdi Jalili
    http://arxiv.org/abs/1904.10169v1

    • [eess.IV]A new Edge Detector Based on Parametric Surface Model: Regression Surface Descriptor
    Rémi Cogranne, Rémi Slysz, Laurence Moreau, Houman Borouchaki
    http://arxiv.org/abs/1904.10235v1

    • [eess.IV]Reducing the Hausdorff Distance in Medical Image Segmentation with Convolutional Neural Networks
    Davood Karimi, Septimiu E. Salcudean
    http://arxiv.org/abs/1904.10030v1

    • [math.OC]Low-rank matrix recovery with composite optimization: good conditioning and rapid convergence
    Vasileios Charisopoulos, Yudong Chen, Damek Davis, Mateo Díaz, Lijun Ding, Dmitriy Drusvyatskiy
    http://arxiv.org/abs/1904.10020v1

    • [math.ST]On the Kullback-Leibler divergence between location-scale densities
    Frank Nielsen
    http://arxiv.org/abs/1904.10428v1

    • [math.ST]Seasonal FIEGARCH Processes
    Sílvia Regina Costa Lopes, Taiane Schaedler Prass
    http://arxiv.org/abs/1904.10114v1

    • [physics.chem-ph]A survey on Big Data and Machine Learning for Chemistry
    Jose F Rodrigues Jr, Larisa Florea, Maria C F de Oliveira, Dermot Diamond, Osvaldo N Oliveira Jr
    http://arxiv.org/abs/1904.10370v1

    • [physics.soc-ph]Identification of intrinsic long-range degree correlations in complex networks
    Yuka Fujiki, Kousuke Yakubo
    http://arxiv.org/abs/1904.10148v1

    • [q-bio.GN]MinCall - MinION end2end convolutional deep learning basecaller
    Neven Miculinić, Marko Ratković, Mile Šikić
    http://arxiv.org/abs/1904.10337v1

    • [q-bio.NC]Spike-Based Winner-Take-All Computation: Fundamental Limits and Order-Optimal Circuits
    Lili Su, Chia-Jung Chang, Nancy Lynch
    http://arxiv.org/abs/1904.10399v1

    • [q-bio.TO]Can fallopian tube anatomy predict pregnancy and pregnancy outcomes after tubal reversal surgery?
    Rafael S. de Souza, Gary S. Berger
    http://arxiv.org/abs/1904.10398v1

    • [q-fin.ST]Copula estimation for nonsynchronous financial data
    Arnab Chakrabarti, Rituparna Sen
    http://arxiv.org/abs/1904.10182v1

    • [quant-ph]$α$-Logarithmic negativity
    Xin Wang, Mark M. Wilde
    http://arxiv.org/abs/1904.10437v1

    • [quant-ph]Quantum boomerang capacity
    Siddhartha Das, Mark M. Wilde
    http://arxiv.org/abs/1904.10344v1

    • [stat.AP]Amazon Forest Fires Between 2001 and 2006 and Birth Weight in Porto Velho
    Taiane Schaedler Prass, Sílvia Regina Costa Lopes, José G. Dórea, Rejane C. Marques, Katiane G. Brandão
    http://arxiv.org/abs/1904.10118v1

    • [stat.AP]How Many Customers Does a Retail Chain Have?
    Ondřej Sokol, Vladimír Holý
    http://arxiv.org/abs/1904.10199v1

    • [stat.AP]Identifying Precipitation Regimes in China Using Model-Based Clustering of Spatial Functional Data
    Haozhe Zhang, Zhengyuan Zhu, Shuiqing Yin
    http://arxiv.org/abs/1904.10152v1

    • [stat.CO]ssMousetrack: Analysing computerized tracking data via Bayesian state-space models in {R}
    Antonio Calcagnì, Massimiliano Pastore, Gianmarco Altoè
    http://arxiv.org/abs/1904.10172v1

    • [stat.ME]A distribution-free smoothed combination method of biomarkers to improve diagnostic accuracy in multi-category classification
    Raju Maiti, Jialiang Li, Priyam Das, Lei Feng, Derek Hausenloy, Bibhas Chakraborty
    http://arxiv.org/abs/1904.10046v1

    • [stat.ME]Deep Learning for Survival Outcomes
    Jon Arni Steingrimsson
    http://arxiv.org/abs/1904.10345v1

    • [stat.ME]Exponential Random Graph models for Little Networks
    George G. Vega Yon, Kayla de la Haye
    http://arxiv.org/abs/1904.10406v1

    • [stat.ME]From one environment to many: The problem of replicability of statistical inferences
    James J. Higgins, Michael J. Higgins, Jinguang Lin
    http://arxiv.org/abs/1904.10036v1

    • [stat.ME]Heterofusion: Fusing genomics data of different measurement scales
    Age K. Smilde, Yipeng Song, Johan A. Westerhuis, Henk A. L. Kiers, Nanne Aben, Lodewyk F. A. Wessels
    http://arxiv.org/abs/1904.10279v1

    • [stat.ME]Model based functional clustering of varved lake sediments
    Per Arnqvist, Sara Sjöstedt de Luna
    http://arxiv.org/abs/1904.10265v1

    • [stat.ML]Regression-Enhanced Random Forests
    Haozhe Zhang, Dan Nettleton, Zhengyuan Zhu
    http://arxiv.org/abs/1904.10416v1