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