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.DS - 数据结构与算法 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.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.SD - 声音处理 cs.SE - 软件工程 cs.SI - 社交网络与信息网络 eess.AS - 语音处理 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.PR - 概率 math.ST - 统计理论 physics.soc-ph - 物理学与社会 q-bio.QM - 定量方法 q-fin.CP -计算金融学 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]Data-driven Policy on Feasibility Determination for the Train Shunting Problem
    • [cs.AI]Differentiable Probabilistic Logic Networks
    • [cs.AI]Measuring Inter-group Agreement on zSlice Based General Type-2 Fuzzy Sets
    • [cs.CL]Answer Extraction for Why Arabic Questions Answering Systems: EWAQ
    • [cs.CL]BAM! Born-Again Multi-Task Networks for Natural Language Understanding
    • [cs.CL]Cross-Domain Generalization of Neural Constituency Parsers
    • [cs.CL]Don’t Take the Premise for Granted: Mitigating Artifacts in Natural Language Inference
    • [cs.CL]Exploiting user-frequency information for mining regionalisms from Social Media texts
    • [cs.CL]Learning to Speak Fluently in a Foreign Language: Multilingual Speech Synthesis and Cross-Language Voice Cloning
    • [cs.CL]Lingua Custodia at WMT’19: Attempts to Control Terminology
    • [cs.CL]Modeling Semantic Compositionality with Sememe Knowledge
    • [cs.CL]Multi-Speaker End-to-End Speech Synthesis
    • [cs.CL]Neural Networks as Explicit Word-Based Rules
    • [cs.CL]On Adversarial Removal of Hypothesis-only Bias in Natural Language Inference
    • [cs.CL]ReQA: An Evaluation for End-to-End Answer Retrieval Models
    • [cs.CL]Transfer Learning from Audio-Visual Grounding to Speech Recognition
    • [cs.CR]Application Inference using Machine Learning based Side Channel Analysis
    • [cs.CR]Entropy and Compression: A simple proof of an inequality of Khinchin
    • [cs.CR]ICLab: A Global, Longitudinal Internet Censorship Measurement Platform
    • [cs.CR]On Designing Machine Learning Models for Malicious Network Traffic Classification
    • [cs.CV]A New Stereo Benchmarking Dataset for Satellite Images
    • [cs.CV]A review on deep learning techniques for 3D sensed data classification
    • [cs.CV]Accurate Nuclear Segmentation with Center Vector Encoding
    • [cs.CV]Automatic Mass Detection in Breast Using Deep Convolutional Neural Network and SVM Classifier
    • [cs.CV]BASN — Learning Steganography with Binary Attention Mechanism
    • [cs.CV]Barnes-Hut Approximation for Point SetGeodesic Shooting
    • [cs.CV]Deep Multi Label Classification in Affine Subspaces
    • [cs.CV]Dunhuang Grotto Painting Dataset and Benchmark
    • [cs.CV]Fast Estimating Pedestrian Moving State Based on Single 2D Body Pose by Shallow Neural Network
    • [cs.CV]Fast geodesic shooting for landmark matching using CUDA
    • [cs.CV]Gated Multiple Feedback Network for Image Super-Resolution
    • [cs.CV]Generating Adversarial Fragments with Adversarial Networks for Physical-world Implementation
    • [cs.CV]Image based Eye Gaze Tracking and its Applications
    • [cs.CV]Joint Learning of Multiple Image Restoration Tasks
    • [cs.CV]Learning to Reason with Relational Video Representation for Question Answering
    • [cs.CV]M3D-GAN: Multi-Modal Multi-Domain Translation with Universal Attention
    • [cs.CV]Metamorphic Detection of Adversarial Examples in Deep Learning Models With Affine Transformations
    • [cs.CV]Multi-Person tracking by multi-scale detection in Basketball scenarios
    • [cs.CV]One Shot Learning for Deformable Medical Image Registration and Periodic Motion Tracking
    • [cs.CV]Regularizing Neural Networks for Future Trajectory Prediction via Inverse Reinforcement Learning
    • [cs.CV]SynthCity: A large scale synthetic point cloud
    • [cs.CV]Toward a Procedural Fruit Tree Rendering Framework for Image Analysis
    • [cs.CV]User Preference Prediction in Visual Data on Mobile Devices
    • [cs.CV]Utilizing Eye Gaze to Enhance the Generalization of Imitation Networks to Unseen Environments
    • [cs.CV]Video Action Recognition Via Neural Architecture Searching
    • [cs.CY]The Role of Cooperation in Responsible AI Development
    • [cs.DC]Hillview: A trillion-cell spreadsheet for big data
    • [cs.DS]Evolutionary techniques in lattice sieving algorithms
    • [cs.DS]Polytopes, lattices, and spherical codes for the nearest neighbor problem
    • [cs.GR]Progressive Wasserstein Barycenters of Persistence Diagrams
    • [cs.GR]Shadow Accrual Maps: Efficient Accumulation of City-Scale Shadows Over Time
    • [cs.HC]Coarse Graining of Data via Inhomogeneous Diffusion Condensation
    • [cs.IR]A New Benchmark and Approach for Fine-grained Cross-media Retrieval
    • [cs.IR]Click-Through Rate Prediction with the User Memory Network
    • [cs.IR]Joint Neural Collaborative Filtering for Recommender Systems
    • [cs.IR]Let’s measure run time! Extending the IR replicability infrastructure to include performance aspects
    • [cs.IR]Sentiment Analysis Challenges in Persian Language
    • [cs.IT]A Geometric Approach to Rank Metric Codes and a Classification of Constant Weight Codes
    • [cs.IT]Adding Common Randomness Can Remove the Secrecy Constraints in Communication Networks
    • [cs.IT]Minimal linear codes arising from blocking sets
    • [cs.IT]Polynomial Linear System Solving with Errors by Simultaneous Polynomial Reconstruction of Interleaved Reed-Solomon Codes
    • [cs.IT]Ultrareliable and Low-Latency Communication Techniques for Tactile Internet Services
    • [cs.IT]User Detection Performance Analysis for Grant-Free Uplink Transmission in Large-Scale Antenna Systems
    • [cs.LG]A Projectional Ansatz to Reconstruction
    • [cs.LG]An Intrinsically-Motivated Approach for Learning Highly Exploring and Fast Mixing Policies
    • [cs.LG]Deep Lagrangian Networks: Using Physics as Model Prior for Deep Learning
    • [cs.LG]DeepXDE: A deep learning library for solving differential equations
    • [cs.LG]Dual Dynamic Inference: Enabling More Efficient, Adaptive and Controllable Deep Inference
    • [cs.LG]Global Optimality Guarantees for Nonconvex Unsupervised Video Segmentation
    • [cs.LG]GluonCV and GluonNLP: Deep Learning in Computer Vision and Natural Language Processing
    • [cs.LG]Haar Transforms for Graph Neural Networks
    • [cs.LG]Improving the Performance of the LSTM and HMM Models via Hybridization
    • [cs.LG]Label Aware Graph Convolutional Network — Not All Edges Deserve Your Attention
    • [cs.LG]Learning a Behavior Model of Hybrid Systems Through Combining Model-Based Testing and Machine Learning (Full Version)
    • [cs.LG]Markov Decision Process for MOOC users behavioral inference
    • [cs.LG]Model Development Process
    • [cs.LG]Multi-layer Attention Mechanism for Speech Keyword Recognition
    • [cs.LG]Neural Input Search for Large Scale Recommendation Models
    • [cs.LG]Out-of-Distribution Detection Using Neural Rendering Generative Models
    • [cs.LG]Quantifying Error in the Presence of Confounders for Causal Inference
    • [cs.LG]Routine Modeling with Time Series Metric Learning
    • [cs.LG]Sparse Networks from Scratch: Faster Training without Losing Performance
    • [cs.LG]Striving for Simplicity in Off-policy Deep Reinforcement Learning
    • [cs.LG]Towards Explaining the Regularization Effect of Initial Large Learning Rate in Training Neural Networks
    • [cs.LG]Trust-Region Variational Inference with Gaussian Mixture Models
    • [cs.LO]Making Study Populations Visible through Knowledge Graphs
    • [cs.MA]Decentralized Dynamic Task Allocation in Swarm Robotic Systems for Disaster Response
    • [cs.MA]Informative Path Planning with Local Penalization for Decentralized and Asynchronous Swarm Robotic Search
    • [cs.NE]Evolutionary Multi-Objective Optimization Driven by Generative Adversarial Networks
    • [cs.NE]Lexicase selection in Learning Classifier Systems
    • [cs.RO]Assessing Transferability from Simulation to Reality for Reinforcement Learning
    • [cs.RO]Bayesian Optimization in Variational Latent Spaces with Dynamic Compression
    • [cs.RO]DOB-Net: Actively Rejecting Unknown Excessive Time-Varying Disturbances
    • [cs.RO]Deep Lagrangian Networks for end-to-end learning of energy-based control for under-actuated systems
    • [cs.RO]Generating All the Roads to Rome: Road Layout Randomization for Improved Road Marking Segmentation
    • [cs.RO]Hybrid system identification using switching density networks
    • [cs.RO]Lidar-based Object Classification with Explicit Occlusion Modeling
    • [cs.RO]Non-Smooth Newton Methods for Deformable Multi-Body Dynamics
    • [cs.RO]Partially Observable Planning and Learning for Systems with Non-Uniform Dynamics
    • [cs.RO]RL-RRT: Kinodynamic Motion Planning via Learning Reachability Estimators from RL Policies
    • [cs.RO]Robust Humanoid Locomotion Using Trajectory Optimization and Sample-Efficient Learning
    • [cs.RO]Simple Kinematic Feedback Enhances Autonomous Learning in Bio-Inspired Tendon-Driven Systems
    • [cs.RO]Towards Affordance Prediction with Vision via Task Oriented Grasp Quality Metrics
    • [cs.SD]Exploring Conditioning for Generative Music Systems with Human-Interpretable Controls
    • [cs.SE]Do Design Metrics Capture Developers Perception of Quality? An Empirical Study on Self-Affirmed Refactoring Activities
    • [cs.SI]Democratic summary of public opinions in free-response surveys
    • [cs.SI]Dynamics of Team Library Adoptions: An Exploration of GitHub Commit Logs
    • [cs.SI]Pairwise Link Prediction
    • [eess.AS]Interpretable Deep Learning Model for the Detection and Reconstruction of Dysarthric Speech
    • [eess.IV]Domain Adaptation-based Augmentation for Weakly Supervised Nuclei Detection
    • [eess.IV]Enhanced generative adversarial network for 3D brain MRI super-resolution
    • [eess.IV]Fetal Pose Estimation in Volumetric MRI using a 3D Convolution Neural Network
    • [eess.IV]Generalized Rank Minimization based Group Sparse Coding for Low-level Image Restoration via Dictionary Learning
    • [eess.SP]Cooperative Localization with Angular Measurements and Posterior Linearization
    • [eess.SP]Learning the Wireless V2I Channels Using Deep Neural Networks
    • [math.NA]The stochastic multi-gradient algorithm for multi-objective optimization and its application to supervised machine learning
    • [math.OC]Randomized Constraints Consensus for Distributed Robust Mixed-Integer Programming
    • [math.OC]SNAP: Finding Approximate Second-Order Stationary Solutions Efficiently for Non-convex Linearly Constrained Problems
    • [math.PR]Optimal Chernoff and Hoeffding Bounds for Finite Markov Chains
    • [math.ST]Convergence Rates for Gaussian Mixtures of Experts
    • [math.ST]Tails of Triangular Flows
    • [physics.soc-ph]Mobile phone data’s potential for informing infrastructure planning in developing countries
    • [q-bio.QM]Computer-Aided Data Mining: Automating a Novel Knowledge Discovery and Data Mining Process Model for Metabolomics
    • [q-bio.QM]Improving Prognostic Performance in Resectable Pancreatic Ductal Adenocarcinoma using Radiomics and Deep Learning Features Fusion in CT Images
    • [q-fin.CP]Deep Reinforcement Learning in Financial Markets
    • [stat.AP]Identifying mediating variables with graphical models: an application to the study of causal pathways in people living with HIV
    • [stat.CO]Probabilistic programming for birth-death models of evolution using an alive particle filter with delayed sampling
    • [stat.CO]The Integrated nested Laplace approximation for fitting models with multivariate response
    • [stat.ME]Approximate Bayesian inference for spatial flood frequency analysis
    • [stat.ME]Bayesian Inference for Regression Copulas
    • [stat.ME]Bayesian Variable Selection for Non-Gaussian Responses: A Marginally Calibrated Copula Approach
    • [stat.ME]Bayesian inferences on uncertain ranks and orderings
    • [stat.ME]Sparse Unit-Sum Regression
    • [stat.ML]A Stochastic First-Order Method for Ordered Empirical Risk Minimization
    • [stat.ML]Two-block vs. Multi-block ADMM: An empirical evaluation of convergence
    • [stat.ML]Variational Autoencoders and Nonlinear ICA: A Unifying Framework

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

    • [cs.AI]Data-driven Policy on Feasibility Determination for the Train Shunting Problem
    Paulo R. de O. da Costa, J. Rhuggenaath, Y. Zhang, A. Akcay, W. Lee, U. Kaymak
    http://arxiv.org/abs/1907.04711v1

    • [cs.AI]Differentiable Probabilistic Logic Networks
    Alexey Potapov, Anatoly Belikov, Vitaly Bogdanov, Alexander Scherbatiy
    http://arxiv.org/abs/1907.04592v1

    • [cs.AI]Measuring Inter-group Agreement on zSlice Based General Type-2 Fuzzy Sets
    Javier Navarro, Christian Wagner
    http://arxiv.org/abs/1907.04679v1

    • [cs.CL]Answer Extraction for Why Arabic Questions Answering Systems: EWAQ
    Fatima T. AL-Khawaldeh
    http://arxiv.org/abs/1907.04149v1

    • [cs.CL]BAM! Born-Again Multi-Task Networks for Natural Language Understanding
    Kevin Clark, Minh-Thang Luong, Urvashi Khandelwal, Christopher D. Manning, Quoc V. Le
    http://arxiv.org/abs/1907.04829v1

    • [cs.CL]Cross-Domain Generalization of Neural Constituency Parsers
    Daniel Fried, Nikita Kitaev, Dan Klein
    http://arxiv.org/abs/1907.04347v1

    • [cs.CL]Don’t Take the Premise for Granted: Mitigating Artifacts in Natural Language Inference
    Yonatan Belinkov, Adam Poliak, Stuart M. Shieber, Benjamin Van Durme, Alexander M. Rush
    http://arxiv.org/abs/1907.04380v1

    • [cs.CL]Exploiting user-frequency information for mining regionalisms from Social Media texts
    Juan Manuel Pérez, Damián E. Aleman, Santiago N. Kalinowski, Agustín Gravano
    http://arxiv.org/abs/1907.04492v1

    • [cs.CL]Learning to Speak Fluently in a Foreign Language: Multilingual Speech Synthesis and Cross-Language Voice Cloning
    Yu Zhang, Ron J. Weiss, Heiga Zen, Yonghui Wu, Zhifeng Chen, RJ Skerry-Ryan, Ye Jia, Andrew Rosenberg, Bhuvana Ramabhadran
    http://arxiv.org/abs/1907.04448v1

    • [cs.CL]Lingua Custodia at WMT’19: Attempts to Control Terminology
    Franck Burlot
    http://arxiv.org/abs/1907.04618v1

    • [cs.CL]Modeling Semantic Compositionality with Sememe Knowledge
    Fanchao Qi, Junjie Huang, Chenghao Yang, Zhiyuan Liu, Xiao Chen, Qun Liu, Maosong Sun
    http://arxiv.org/abs/1907.04744v1

    • [cs.CL]Multi-Speaker End-to-End Speech Synthesis
    Jihyun Park, Kexin Zhao, Kainan Peng, Wei Ping
    http://arxiv.org/abs/1907.04462v1

    • [cs.CL]Neural Networks as Explicit Word-Based Rules
    Jindřich Libovický
    http://arxiv.org/abs/1907.04613v1

    • [cs.CL]On Adversarial Removal of Hypothesis-only Bias in Natural Language Inference
    Yonatan Belinkov, Adam Poliak, Stuart M. Shieber, Benjamin Van Durme, Alexander M. Rush
    http://arxiv.org/abs/1907.04389v1

    • [cs.CL]ReQA: An Evaluation for End-to-End Answer Retrieval Models
    Amin Ahmad, Noah Constant, Yinfei Yang, Daniel Cer
    http://arxiv.org/abs/1907.04780v1

    • [cs.CL]Transfer Learning from Audio-Visual Grounding to Speech Recognition
    Wei-Ning Hsu, David Harwath, James Glass
    http://arxiv.org/abs/1907.04355v1

    • [cs.CR]Application Inference using Machine Learning based Side Channel Analysis
    Nikhil Chawla, Arvind Singh, Monodeep Kar, Saibal Mukhopadhyay
    http://arxiv.org/abs/1907.04428v1

    • [cs.CR]Entropy and Compression: A simple proof of an inequality of Khinchin
    Riccardo Aragona, Francesca Marzi, Filippo Mignosi
    http://arxiv.org/abs/1907.04713v1

    • [cs.CR]ICLab: A Global, Longitudinal Internet Censorship Measurement Platform
    Arian Akhavan Niaki, Shinyoung Cho, Zachary Weinberg, Nguyen Phong Hoang, Abbas Razaghpanah, Nicolas Christin, Phillipa Gill
    http://arxiv.org/abs/1907.04245v2

    • [cs.CR]On Designing Machine Learning Models for Malicious Network Traffic Classification
    Talha Ongun, Timothy Sakharaov, Simona Boboila, Alina Oprea, Tina Eliassi-Rad
    http://arxiv.org/abs/1907.04846v1

    • [cs.CV]A New Stereo Benchmarking Dataset for Satellite Images
    Sonali Patil, Bharath Comandur, Tanmay Prakash, Avinash C. Kak
    http://arxiv.org/abs/1907.04404v1

    • [cs.CV]A review on deep learning techniques for 3D sensed data classification
    David Griffiths, Jan Boehm
    http://arxiv.org/abs/1907.04444v1

    • [cs.CV]Accurate Nuclear Segmentation with Center Vector Encoding
    Jiahui Li, Zhiqiang Hu, Shuang Yang
    http://arxiv.org/abs/1907.03951v2

    • [cs.CV]Automatic Mass Detection in Breast Using Deep Convolutional Neural Network and SVM Classifier
    Md. Kamrul Hasan, Tajwar Abrar Aleef
    http://arxiv.org/abs/1907.04424v1

    • [cs.CV]BASN — Learning Steganography with Binary Attention Mechanism
    Yang Yang
    http://arxiv.org/abs/1907.04362v1

    • [cs.CV]Barnes-Hut Approximation for Point SetGeodesic Shooting
    Jiancong Wang, Long Xie, Paul Yushkevich, James Gee
    http://arxiv.org/abs/1907.04834v1

    • [cs.CV]Deep Multi Label Classification in Affine Subspaces
    Thomas Kurmann, Pablo Marquez Neila, Sebastian Wolf, Raphael Sznitman
    http://arxiv.org/abs/1907.04563v1

    • [cs.CV]Dunhuang Grotto Painting Dataset and Benchmark
    Tianxiu Yu, Shijie Zhang, Cong Lin, Shaodi You
    http://arxiv.org/abs/1907.04589v1

    • [cs.CV]Fast Estimating Pedestrian Moving State Based on Single 2D Body Pose by Shallow Neural Network
    Zixing Wang, Nikolaos Papanikolopoulos
    http://arxiv.org/abs/1907.04361v1

    • [cs.CV]Fast geodesic shooting for landmark matching using CUDA
    Jiancong Wang
    http://arxiv.org/abs/1907.04839v1

    • [cs.CV]Gated Multiple Feedback Network for Image Super-Resolution
    Qilei Li, Zhen Li, Lu Lu, Gwanggil Jeon, Kai Liu, Xiaomin Yang
    http://arxiv.org/abs/1907.04253v2

    • [cs.CV]Generating Adversarial Fragments with Adversarial Networks for Physical-world Implementation
    Zelun Kong, Cong Liu
    http://arxiv.org/abs/1907.04449v1

    • [cs.CV]Image based Eye Gaze Tracking and its Applications
    Anjith George
    http://arxiv.org/abs/1907.04325v1

    • [cs.CV]Joint Learning of Multiple Image Restoration Tasks
    Xing Liu, Masanori Suganuma, Takayuki Okatani
    http://arxiv.org/abs/1907.04508v1

    • [cs.CV]Learning to Reason with Relational Video Representation for Question Answering
    Thao Minh Le, Vuong Le, Svetha Venkatesh, Truyen Tran
    http://arxiv.org/abs/1907.04553v1

    • [cs.CV]M3D-GAN: Multi-Modal Multi-Domain Translation with Universal Attention
    Shuang Ma, Daniel McDuff, Yale Song
    http://arxiv.org/abs/1907.04378v1

    • [cs.CV]Metamorphic Detection of Adversarial Examples in Deep Learning Models With Affine Transformations
    Rohan Reddy Mekala, Gudjon Einar Magnusson, Adam Porter, Mikael Lindvall, Madeline Diep
    http://arxiv.org/abs/1907.04774v1

    • [cs.CV]Multi-Person tracking by multi-scale detection in Basketball scenarios
    Adrià Arbués-Sangüesa, Gloria Haro, Coloma Ballester
    http://arxiv.org/abs/1907.04637v1

    • [cs.CV]One Shot Learning for Deformable Medical Image Registration and Periodic Motion Tracking
    Tobias Fechter, Dimos Baltas
    http://arxiv.org/abs/1907.04641v1

    • [cs.CV]Regularizing Neural Networks for Future Trajectory Prediction via Inverse Reinforcement Learning
    Dooseop Choi, Kyoungwook Min, Jeongdan Choi
    http://arxiv.org/abs/1907.04525v1

    • [cs.CV]SynthCity: A large scale synthetic point cloud
    David Griffiths, Jan Boehm
    http://arxiv.org/abs/1907.04758v1

    • [cs.CV]Toward a Procedural Fruit Tree Rendering Framework for Image Analysis
    Thomas Duboudin, Maxime Petit, Liming Chen
    http://arxiv.org/abs/1907.04759v1

    • [cs.CV]User Preference Prediction in Visual Data on Mobile Devices
    A. V. Savchenko, K. V. Demochkin, I. S. Grechikhin
    http://arxiv.org/abs/1907.04519v1

    • [cs.CV]Utilizing Eye Gaze to Enhance the Generalization of Imitation Networks to Unseen Environments
    Congcong Liu, Yuying Chen, Lei Tai, Ming Liu, Bertram Shi
    http://arxiv.org/abs/1907.04728v1

    • [cs.CV]Video Action Recognition Via Neural Architecture Searching
    Wei Peng, Xiaopeng Hong, Guoying Zhao
    http://arxiv.org/abs/1907.04632v1

    • [cs.CY]The Role of Cooperation in Responsible AI Development
    Amanda Askell, Miles Brundage, Gillian Hadfield
    http://arxiv.org/abs/1907.04534v1

    • [cs.DC]Hillview: A trillion-cell spreadsheet for big data
    Mihai Budiu, Parikshit Gopalan, Lalith Suresh, Udi Wieder, Han Kruiger, Marcos K. Aguilera
    http://arxiv.org/abs/1907.04827v1

    • [cs.DS]Evolutionary techniques in lattice sieving algorithms
    Thijs Laarhoven
    http://arxiv.org/abs/1907.04629v1

    • [cs.DS]Polytopes, lattices, and spherical codes for the nearest neighbor problem
    Thijs Laarhoven
    http://arxiv.org/abs/1907.04628v1

    • [cs.GR]Progressive Wasserstein Barycenters of Persistence Diagrams
    Jules Vidal, Joseph Budin, Julien Tierny
    http://arxiv.org/abs/1907.04565v1

    • [cs.GR]Shadow Accrual Maps: Efficient Accumulation of City-Scale Shadows Over Time
    Fabio Miranda, Harish Doraiswamy, Marcos Lage, Luc Wilson, Mondrian Hsieh, Claudio T. Silva
    http://arxiv.org/abs/1907.04435v1

    • [cs.HC]Coarse Graining of Data via Inhomogeneous Diffusion Condensation
    Nathan Brugnone, Alex Gonopolskiy, Mark W. Moyle, Manik Kuchroo, David van Dijk, Kevin R. Moon, Daniel Colon-Ramos, Guy Wolf, Matthew J. Hirn, Smita Krishnaswamy
    http://arxiv.org/abs/1907.04463v1

    • [cs.IR]A New Benchmark and Approach for Fine-grained Cross-media Retrieval
    Xiangteng He, Yuxin Peng, Liu Xie
    http://arxiv.org/abs/1907.04476v1

    • [cs.IR]Click-Through Rate Prediction with the User Memory Network
    Wentao Ouyang, Xiuwu Zhang, Shukui Ren, Li Li, Zhaojie Liu, Yanlong Du
    http://arxiv.org/abs/1907.04667v1

    • [cs.IR]Joint Neural Collaborative Filtering for Recommender Systems
    Wanyu Chen, Fei Cai, Honghui Chen, Maarten de Rijke
    http://arxiv.org/abs/1907.03459v2

    • [cs.IR]Let’s measure run time! Extending the IR replicability infrastructure to include performance aspects
    Sebastian Hofstätter, Allan Hanbury
    http://arxiv.org/abs/1907.04614v1

    • [cs.IR]Sentiment Analysis Challenges in Persian Language
    Mohammad Heydari
    http://arxiv.org/abs/1907.04407v1

    • [cs.IT]A Geometric Approach to Rank Metric Codes and a Classification of Constant Weight Codes
    Tovohery Hajatiana Randrianarisoa
    http://arxiv.org/abs/1907.04372v1

    • [cs.IT]Adding Common Randomness Can Remove the Secrecy Constraints in Communication Networks
    Fan Li, Jinyuan Chen
    http://arxiv.org/abs/1907.04599v1

    • [cs.IT]Minimal linear codes arising from blocking sets
    Matteo Bonini, Martino Borello
    http://arxiv.org/abs/1907.04626v1

    • [cs.IT]Polynomial Linear System Solving with Errors by Simultaneous Polynomial Reconstruction of Interleaved Reed-Solomon Codes
    E. Guerrini, R. Lebreton, I. Zappatore
    http://arxiv.org/abs/1907.04401v1

    • [cs.IT]Ultrareliable and Low-Latency Communication Techniques for Tactile Internet Services
    Kwang Soon Kim, Dong Ku Kim, Chan-Byoung Chae, Sunghyun Choi, Young-Chai Ko, Jonghyun Kim, Yeon-Geun Lim, Minho Yang, Sundo Kim, Byungju Lim, Kwanghoon Lee, Kyung Lin Ryu
    http://arxiv.org/abs/1907.04474v1

    • [cs.IT]User Detection Performance Analysis for Grant-Free Uplink Transmission in Large-Scale Antenna Systems
    Jonghyun Kim, Kyung Lin Ryu, Kwang Soon Kim
    http://arxiv.org/abs/1907.04478v1

    • [cs.LG]A Projectional Ansatz to Reconstruction
    Sören Dittmer, Peter Maass
    http://arxiv.org/abs/1907.04675v1

    • [cs.LG]An Intrinsically-Motivated Approach for Learning Highly Exploring and Fast Mixing Policies
    Mirco Mutti, Marcello Restelli
    http://arxiv.org/abs/1907.04662v1

    • [cs.LG]Deep Lagrangian Networks: Using Physics as Model Prior for Deep Learning
    Michael Lutter, Christian Ritter, Jan Peters
    http://arxiv.org/abs/1907.04490v1

    • [cs.LG]DeepXDE: A deep learning library for solving differential equations
    Lu Lu, Xuhui Meng, Zhiping Mao, George E. Karniadakis
    http://arxiv.org/abs/1907.04502v1

    • [cs.LG]Dual Dynamic Inference: Enabling More Efficient, Adaptive and Controllable Deep Inference
    Yue Wang, Jianghao Shen, Ting-Kuei Hu, Pengfei Xu, Tan Nguyen, Richard Baraniuk, Zhangyang Wang, Yingyan Lin
    http://arxiv.org/abs/1907.04523v1

    • [cs.LG]Global Optimality Guarantees for Nonconvex Unsupervised Video Segmentation
    Brendon G. Anderson, Somayeh Sojoudi
    http://arxiv.org/abs/1907.04409v1

    • [cs.LG]GluonCV and GluonNLP: Deep Learning in Computer Vision and Natural Language Processing
    Jian Guo, He He, Tong He, Leonard Lausen, Mu Li, Haibin Lin, Xingjian Shi, Chenguang Wang, Junyuan Xie, Sheng Zha, Aston Zhang, Hang Zhang, Zhi Zhang, Zhongyue Zhang, Shuai Zheng
    http://arxiv.org/abs/1907.04433v1

    • [cs.LG]Haar Transforms for Graph Neural Networks
    Ming Li, Zheng Ma, Yu Guang Wang, Xiaosheng Zhuang
    http://arxiv.org/abs/1907.04786v1

    • [cs.LG]Improving the Performance of the LSTM and HMM Models via Hybridization
    Larkin Liu, Yu-Chung Lin, Joshua Reid
    http://arxiv.org/abs/1907.04670v1

    • [cs.LG]Label Aware Graph Convolutional Network — Not All Edges Deserve Your Attention
    Hao Chen, Lu Wang, Senzhang Wang, Dijun Luo, Wenbing Huang, Zhoujun Li
    http://arxiv.org/abs/1907.04707v1

    • [cs.LG]Learning a Behavior Model of Hybrid Systems Through Combining Model-Based Testing and Machine Learning (Full Version)
    Bernhard K. Aichernig, Roderick Bloem, Masoud Ebrahimi, Martin Horn, Franz Pernkopf, Wolfgang Roth, Astrid Rupp, Martin Tappler, Markus Tranninger
    http://arxiv.org/abs/1907.04708v1

    • [cs.LG]Markov Decision Process for MOOC users behavioral inference
    Firas Jarboui, Célya Gruson-daniel, Alain Durmus, Vincent Rocchisani, Sophie-helene Goulet Ebongue, Anneliese Depoux, Wilfried Kirschenmann, Vianney Perchet
    http://arxiv.org/abs/1907.04723v1

    • [cs.LG]Model Development Process
    Przemyslaw Biecek
    http://arxiv.org/abs/1907.04461v1

    • [cs.LG]Multi-layer Attention Mechanism for Speech Keyword Recognition
    Ruisen Luo, Tianran Sun, Chen Wang, Miao Du, Zuodong Tang, Kai Zhou, Xiaofeng Gong, Xiaomei Yang
    http://arxiv.org/abs/1907.04536v1

    • [cs.LG]Neural Input Search for Large Scale Recommendation Models
    Manas R. Joglekar, Cong Li, Jay K. Adams, Pranav Khaitan, Quoc V. Le
    http://arxiv.org/abs/1907.04471v1

    • [cs.LG]Out-of-Distribution Detection Using Neural Rendering Generative Models
    Yujia Huang, Sihui Dai, Tan Nguyen, Richard G. Baraniuk, Anima Anandkumar
    http://arxiv.org/abs/1907.04572v1

    • [cs.LG]Quantifying Error in the Presence of Confounders for Causal Inference
    Rathin Desai, Amit Sharma
    http://arxiv.org/abs/1907.04805v1

    • [cs.LG]Routine Modeling with Time Series Metric Learning
    Paul Compagnon, Grégoire Lefebvre, Stefan Duffner, Christophe Garcia
    http://arxiv.org/abs/1907.04666v1

    • [cs.LG]Sparse Networks from Scratch: Faster Training without Losing Performance
    Tim Dettmers, Luke Zettlemoyer
    http://arxiv.org/abs/1907.04840v1

    • [cs.LG]Striving for Simplicity in Off-policy Deep Reinforcement Learning
    Rishabh Agarwal, Dale Schuurmans, Mohammad Norouzi
    http://arxiv.org/abs/1907.04543v1

    • [cs.LG]Towards Explaining the Regularization Effect of Initial Large Learning Rate in Training Neural Networks
    Yuanzhi Li, Colin Wei, Tengyu Ma
    http://arxiv.org/abs/1907.04595v1

    • [cs.LG]Trust-Region Variational Inference with Gaussian Mixture Models
    Oleg Arenz, Mingjun Zhong, Gerhard Neumann
    http://arxiv.org/abs/1907.04710v1

    • [cs.LO]Making Study Populations Visible through Knowledge Graphs
    Shruthi Chari, Miao Qi, Nkcheniyere N. Agu, Oshani Seneviratne, James P. McCusker, Kristin P. Bennett, Amar K. Das, Deborah L. McGuinness
    http://arxiv.org/abs/1907.04358v1

    • [cs.MA]Decentralized Dynamic Task Allocation in Swarm Robotic Systems for Disaster Response
    Payam Ghassemi, David DePauw, Souma Chowdhury
    http://arxiv.org/abs/1907.04394v1

    • [cs.MA]Informative Path Planning with Local Penalization for Decentralized and Asynchronous Swarm Robotic Search
    Payam Ghassemi, Souma Chowdhury
    http://arxiv.org/abs/1907.04396v1

    • [cs.NE]Evolutionary Multi-Objective Optimization Driven by Generative Adversarial Networks
    Cheng He, Shihua Huang, Ran Cheng, Kay Chen Tan, Yaochu Jin
    http://arxiv.org/abs/1907.04482v1

    • [cs.NE]Lexicase selection in Learning Classifier Systems
    Sneha Aenugu, Lee Spector
    http://arxiv.org/abs/1907.04736v1

    • [cs.RO]Assessing Transferability from Simulation to Reality for Reinforcement Learning
    Fabio Muratore, Michael Gienger, Jan Peters
    http://arxiv.org/abs/1907.04685v1

    • [cs.RO]Bayesian Optimization in Variational Latent Spaces with Dynamic Compression
    Rika Antonova, Akshara Rai, Tianyu Li, Danica Kragic
    http://arxiv.org/abs/1907.04796v1

    • [cs.RO]DOB-Net: Actively Rejecting Unknown Excessive Time-Varying Disturbances
    Tianming Wang, Wenjie Lu, Zheng Yan, Dikai Liu
    http://arxiv.org/abs/1907.04514v1

    • [cs.RO]Deep Lagrangian Networks for end-to-end learning of energy-based control for under-actuated systems
    Michael Lutter, Kim Listmann, Jan Peters
    http://arxiv.org/abs/1907.04489v1

    • [cs.RO]Generating All the Roads to Rome: Road Layout Randomization for Improved Road Marking Segmentation
    Tom Bruls, Horia Porav, Lars Kunze, Paul Newman
    http://arxiv.org/abs/1907.04569v1

    • [cs.RO]Hybrid system identification using switching density networks
    Michael Burke, Yordan Hristov, Subramanian Ramamoorthy
    http://arxiv.org/abs/1907.04360v1

    • [cs.RO]Lidar-based Object Classification with Explicit Occlusion Modeling
    Xiaoxiang Zhang, Hao Fu, Bin Dai
    http://arxiv.org/abs/1907.04057v2

    • [cs.RO]Non-Smooth Newton Methods for Deformable Multi-Body Dynamics
    Miles Macklin, Kenny Erleben, Matthias Müller, Nuttapong Chentanez, Stefan Jeschke, Viktor Makoviychuk
    http://arxiv.org/abs/1907.04587v1

    • [cs.RO]Partially Observable Planning and Learning for Systems with Non-Uniform Dynamics
    Nicholas Collins, Hanna Kurniawati
    http://arxiv.org/abs/1907.04457v1

    • [cs.RO]RL-RRT: Kinodynamic Motion Planning via Learning Reachability Estimators from RL Policies
    Hao-Tien Lewis Chiang, Jasmine Hsu, Marek Fiser, Lydia Tapia, Aleksandra Faust
    http://arxiv.org/abs/1907.04799v1

    • [cs.RO]Robust Humanoid Locomotion Using Trajectory Optimization and Sample-Efficient Learning
    Mohammad Hasan Yeganegi, Majid Khadiv, S. Ali A. Moosavian, Jia-Jie Zhu, Andrea Del Prete, Ludovic Righetti
    http://arxiv.org/abs/1907.04616v1

    • [cs.RO]Simple Kinematic Feedback Enhances Autonomous Learning in Bio-Inspired Tendon-Driven Systems
    Ali Marjaninejad, Darío Urbina-Meléndez, Francisco J. Valero-Cuevas
    http://arxiv.org/abs/1907.04539v1

    • [cs.RO]Towards Affordance Prediction with Vision via Task Oriented Grasp Quality Metrics
    Luca Cavalli, Gianpaolo Di Pietro, Matteo Matteucci
    http://arxiv.org/abs/1907.04761v1

    • [cs.SD]Exploring Conditioning for Generative Music Systems with Human-Interpretable Controls
    Nicholas Meade, Nicholas Barreyre, Scott C. Lowe, Sageev Oore
    http://arxiv.org/abs/1907.04352v1

    • [cs.SE]Do Design Metrics Capture Developers Perception of Quality? An Empirical Study on Self-Affirmed Refactoring Activities
    Eman Abdullah AlOmar, Mohamed Wiem Mkaouer, Ali Ouni, Marouane Kessentini
    http://arxiv.org/abs/1907.04797v1

    • [cs.SI]Democratic summary of public opinions in free-response surveys
    Tatsuro Kawamoto, Takaaki Aoki
    http://arxiv.org/abs/1907.04359v1

    • [cs.SI]Dynamics of Team Library Adoptions: An Exploration of GitHub Commit Logs
    Pamela Bilo Thomas, Rachel Krohn, Tim Weninger
    http://arxiv.org/abs/1907.04527v1

    • [cs.SI]Pairwise Link Prediction
    Huda Nassar, Austin R. Benson, David F. Gleich
    http://arxiv.org/abs/1907.04503v1

    • [eess.AS]Interpretable Deep Learning Model for the Detection and Reconstruction of Dysarthric Speech
    Daniel Korzekwa, Roberto Barra-Chicote, Bozena Kostek, Thomas Drugman, Mateusz Lajszczak
    http://arxiv.org/abs/1907.04743v1

    • [eess.IV]Domain Adaptation-based Augmentation for Weakly Supervised Nuclei Detection
    Nicolas Brieu, Armin Meier, Ansh Kapil, Ralf Schoenmeyer, Christos G. Gavriel, Peter D. Caie, Günter Schmidt
    http://arxiv.org/abs/1907.04681v1

    • [eess.IV]Enhanced generative adversarial network for 3D brain MRI super-resolution
    Jiancong Wang, Yuhua Chen, Yifan Wu, Jianbo Shi, James Gee
    http://arxiv.org/abs/1907.04835v1

    • [eess.IV]Fetal Pose Estimation in Volumetric MRI using a 3D Convolution Neural Network
    Junshen Xu, Molin Zhang, Esra Abaci Turk, Larry Zhang, Ellen Grant, Kui Ying, Polina Golland, Elfar Adalsteinsson
    http://arxiv.org/abs/1907.04500v1

    • [eess.IV]Generalized Rank Minimization based Group Sparse Coding for Low-level Image Restoration via Dictionary Learning
    Yunyi Li, Guan Gui, Xiefeng Cheng
    http://arxiv.org/abs/1907.04699v1

    • [eess.SP]Cooperative Localization with Angular Measurements and Posterior Linearization
    Yibo Wu, Bile Peng, Henk Wymeersch, Gonzalo Seco-Granados, Anastasios Kakkavas, Mario H. Castañeda Garcia, Richard A. Stirling-Gallacher
    http://arxiv.org/abs/1907.04700v1

    • [eess.SP]Learning the Wireless V2I Channels Using Deep Neural Networks
    Tian-Hao Li, Muhammad R. A. Khandaker, Faisal Tariq, Kai-Kit Wong, Risala T. Khan
    http://arxiv.org/abs/1907.04831v1

    • [math.NA]The stochastic multi-gradient algorithm for multi-objective optimization and its application to supervised machine learning
    Suyun Liu, Luis Nunes Vicente
    http://arxiv.org/abs/1907.04472v1

    • [math.OC]Randomized Constraints Consensus for Distributed Robust Mixed-Integer Programming
    Mohammadreza Chamanbaz, Giuseppe Notarstefano, Francesco Sasso, Roland Bouffanais
    http://arxiv.org/abs/1907.04691v1

    • [math.OC]SNAP: Finding Approximate Second-Order Stationary Solutions Efficiently for Non-convex Linearly Constrained Problems
    Songtao Lu, Meisam Razaviyayn, Bo Yang, Kejun Huang, Mingyi Hong
    http://arxiv.org/abs/1907.04450v1

    • [math.PR]Optimal Chernoff and Hoeffding Bounds for Finite Markov Chains
    Vrettos Moulos, Venkat Anantharam
    http://arxiv.org/abs/1907.04467v1

    • [math.ST]Convergence Rates for Gaussian Mixtures of Experts
    Nhat Ho, Chiao-Yu Yang, Michael I. Jordan
    http://arxiv.org/abs/1907.04377v1

    • [math.ST]Tails of Triangular Flows
    Priyank Jaini, Ivan Kobyzev, Marcus Brubaker, Yaoliang Yu
    http://arxiv.org/abs/1907.04481v1

    • [physics.soc-ph]Mobile phone data’s potential for informing infrastructure planning in developing countries
    Hadrien Salat, Zbigniew Smoreda, Markus Schläpfer
    http://arxiv.org/abs/1907.04812v1

    • [q-bio.QM]Computer-Aided Data Mining: Automating a Novel Knowledge Discovery and Data Mining Process Model for Metabolomics
    Ahmed BaniMustafa, Nigel Hardy
    http://arxiv.org/abs/1907.04318v1

    • [q-bio.QM]Improving Prognostic Performance in Resectable Pancreatic Ductal Adenocarcinoma using Radiomics and Deep Learning Features Fusion in CT Images
    Yucheng Zhang, Edrise M. Lobo-Mueller, Paul Karanicolas, Steven Gallinger, Masoom A. Haider, Farzad Khalvati
    http://arxiv.org/abs/1907.04822v1

    • [q-fin.CP]Deep Reinforcement Learning in Financial Markets
    Souradeep Chakraborty
    http://arxiv.org/abs/1907.04373v1

    • [stat.AP]Identifying mediating variables with graphical models: an application to the study of causal pathways in people living with HIV
    Adrian Dobra, Katherine Buhikire, Joachim G. Voss
    http://arxiv.org/abs/1907.04838v1

    • [stat.CO]Probabilistic programming for birth-death models of evolution using an alive particle filter with delayed sampling
    Jan Kudlicka, Lawrence M. Murray, Fredrik Ronquist, Thomas B. Schön
    http://arxiv.org/abs/1907.04615v1

    • [stat.CO]The Integrated nested Laplace approximation for fitting models with multivariate response
    Joaquín Martínez-Minaya, Finn Lindgren, Antonio López-Quílez, Daniel Simpson, David Conesa
    http://arxiv.org/abs/1907.04059v1

    • [stat.ME]Approximate Bayesian inference for spatial flood frequency analysis
    Árni V. Johannesson, Birgir Hrafnkelsson, Raphaël Huser, Haakon Bakka, Stefan Siegert
    http://arxiv.org/abs/1907.04763v1

    • [stat.ME]Bayesian Inference for Regression Copulas
    Michael Stanley Smith, Nadja Klein
    http://arxiv.org/abs/1907.04529v1

    • [stat.ME]Bayesian Variable Selection for Non-Gaussian Responses: A Marginally Calibrated Copula Approach
    Nadja Klein, Michael Stanley Smith
    http://arxiv.org/abs/1907.04530v1

    • [stat.ME]Bayesian inferences on uncertain ranks and orderings
    Andres F. Barrientos, Deborshee Sen, Garritt L Page, David B Dunson
    http://arxiv.org/abs/1907.04842v1

    • [stat.ME]Sparse Unit-Sum Regression
    Nick Koning, Paul Bekker
    http://arxiv.org/abs/1907.04620v1

    • [stat.ML]A Stochastic First-Order Method for Ordered Empirical Risk Minimization
    Kenji Kawaguchi, Haihao Lu
    http://arxiv.org/abs/1907.04371v1

    • [stat.ML]Two-block vs. Multi-block ADMM: An empirical evaluation of convergence
    Andre Goncalves, Xiaoli Liu, Arindam Banerjee
    http://arxiv.org/abs/1907.04524v1

    • [stat.ML]Variational Autoencoders and Nonlinear ICA: A Unifying Framework
    Ilyes Khemakhem, Diederik P. Kingma, Aapo Hyvärinen
    http://arxiv.org/abs/1907.04809v1