cs.AI - 人工智能

    cs.CC - 计算复杂度 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.MM - 多媒体 cs.NE - 神经与进化计算 cs.RO - 机器人学 cs.SD - 声音处理 cs.SI - 社交网络与信息网络 econ.TH - 理论经济学 eess.IV - 图像与视频处理 eess.SP - 信号处理 eess.SY - 系统和控制 math.CO - 组合数学 math.NA - 数值分析 math.OC - 优化与控制 math.PR - 概率 math.ST - 统计理论 nlin.CG - 细胞自动机与晶格气 physics.soc-ph - 物理学与社会 q-bio.QM - 定量方法 stat.AP - 应用统计 stat.ME - 统计方法论 stat.ML - (统计)机器学习 stat.OT - 其他统计学

    • [cs.AI]A Conformance Checking-based Approach for Drift Detection in Business Processes
    • [cs.AI]A Scheme for Dynamic Risk-Sensitive Sequential Decision Making
    • [cs.AI]Diverse Agents for Ad-Hoc Cooperation in Hanabi
    • [cs.AI]Learning by Abstraction: The Neural State Machine
    • [cs.AI]On the Semantic Interpretability of Artificial Intelligence Models
    • [cs.CC]Interactive Verifiable Polynomial Evaluation
    • [cs.CL]An Intrinsic Nearest Neighbor Analysis of Neural Machine Translation Architectures
    • [cs.CL]Analyzing Phonetic and Graphemic Representations in End-to-End Automatic Speech Recognition
    • [cs.CL]Hahahahaha, Duuuuude, Yeeessss!: A two-parameter characterization of stretchable words and the dynamics of mistypings and misspellings
    • [cs.CL]Implicit Discourse Relation Identification for Open-domain Dialogues
    • [cs.CL]Improving short text classification through global augmentation methods
    • [cs.CL]Learning Neural Sequence-to-Sequence Models from Weak Feedback with Bipolar Ramp Loss
    • [cs.CL]Multilingual Universal Sentence Encoder for Semantic Retrieval
    • [cs.CL]NTT’s Machine Translation Systems for WMT19 Robustness Task
    • [cs.CL]Neural Aspect and Opinion Term Extraction with Mined Rules as Weak Supervision
    • [cs.CL]Sentiment and position-taking analysis of parliamentary debates: A systematic literature review
    • [cs.CL]Systematic quantitative analyses reveal the folk-zoological knowledge embedded in folktales
    • [cs.CR]ICLab: A Global, Longitudinal Internet Censorship Measurement Platform
    • [cs.CR]Private key encryption and recovery in blockchain
    • [cs.CR]Security for Distributed Deep Neural Networks Towards Data Confidentiality & Intellectual Property Protection
    • [cs.CR]Using Temporal and Topological Features for Intrusion Detection in Operational Networks
    • [cs.CV]3D pavement surface reconstruction using an RGB-D sensor
    • [cs.CV]A Baseline for 3D Multi-Object Tracking
    • [cs.CV]A Light weight and Hybrid Deep Learning Model based Online Signature Verification
    • [cs.CV]Accurate Nuclear Segmentation with \Center Vector Encoding
    • [cs.CV]Adaptive Exploration for Unsupervised Person Re-Identification
    • [cs.CV]Attentive CT Lesion Detection Using Deep Pyramid Inference with Multi-Scale Booster
    • [cs.CV]BADAM: A Public Dataset for Baseline Detection in Arabic-script Manuscripts
    • [cs.CV]Deep Learning for Spacecraft Pose Estimation from Photorealistic Rendering
    • [cs.CV]Deep Pixel-wise Binary Supervision for Face Presentation Attack Detection
    • [cs.CV]Depth from Small Motion using Rank-1 Initialization
    • [cs.CV]Distill-2MD-MTL: Data Distillation based on Multi-Dataset Multi-Domain Multi-Task Frame Work to Solve Face Related Tasksks, Multi Task Learning, Semi-Supervised Learning
    • [cs.CV]Domain Adaptation in Multi-Channel Autoencoder based Features for Robust Face Anti-Spoofing
    • [cs.CV]Efficient Pose Selection for Interactive Camera Calibration
    • [cs.CV]Fast Visual Object Tracking with Rotated Bounding Boxes
    • [cs.CV]Gated Multiple Feedback Network for Image Super-Resolution
    • [cs.CV]Improving Deep Lesion Detection Using 3D Contextual and Spatial Attention
    • [cs.CV]On the Exact Recovery Conditions of 3D Human Motion from 2D Landmark Motion with Sparse Articulated Motion
    • [cs.CV]Personalised aesthetics with residual adapters
    • [cs.CV]Positional Normalization
    • [cs.CV]Signet Ring Cell Detection With a Semi-supervised Learning Framework
    • [cs.CV]Sparse-to-Dense Hypercolumn Matching for Long-Term Visual Localization
    • [cs.CV]Template-Based Posit Multiplication for Training and Inferring in Neural Networks
    • [cs.CV]UnsuperPoint: End-to-end Unsupervised Interest Point Detector and Descriptor
    • [cs.CV]calibDB: enabling web based computer vision through on-the-fly camera calibration
    • [cs.CY]Characterizing Bitcoin donations to open source software on GitHub
    • [cs.CY]Sharing and Learning Alloy on the Web
    • [cs.DC]Bi-objective Optimisation of Data-parallel Applications on Heterogeneous Platforms for Performance and Energy via Workload Distribution
    • [cs.DC]Streaming 1.9 Billion Hypersparse Network Updates per Second with D4M
    • [cs.DS]Faster Deterministic Distributed Coloring Through Recursive List Coloring
    • [cs.DS]Near-optimal Repair of Reed-Solomon Codes with Low Sub-packetization
    • [cs.GR]Efficient Cloth Simulation using Miniature Cloth and Upscaling Deep Neural Networks
    • [cs.HC]Belief places and spaces: Mapping cognitive environments
    • [cs.HC]Pitako — Recommending Game Design Elements in Cicero
    • [cs.HC]Translating neural signals to text using a Brain-Machine Interface
    • [cs.IR]An Attention Mechanism for Musical Instrument Recognition
    • [cs.IR]UW-BHI at MEDIQA 2019: An Analysis of Representation Methods for Medical Natural Language Inference
    • [cs.IT]A Simple Derivation of AMP and its State Evolution via First-Order Cancellation
    • [cs.IT]Collaborative Machine Learning at the Wireless Edge with Blind Transmitters
    • [cs.IT]Control of Status Updates for Energy Harvesting Devices that Monitor Processes with Alarms
    • [cs.IT]Deep Learning-Aided Dynamic Read Thresholds Design For Multi-Level-Cell Flash Memories
    • [cs.IT]Distributed Approximation of Functions over Fast Fading Channels with Applications to Distributed Learning and the Max-Consensus Problem
    • [cs.IT]Fundamental limits of quantum-secure covert communication over bosonic channels
    • [cs.IT]On the 3-D Placement of Airborne Base Stations Using Tethered UAVs
    • [cs.LG]Adversarial Fault Tolerant Training for Deep Neural Networks
    • [cs.LG]Are deep ResNets provably better than linear predictors?
    • [cs.LG]Asymptotic Bayes risk for Gaussian mixture in a semi-supervised setting
    • [cs.LG]Attending to Emotional Narratives
    • [cs.LG]Characterization of Overlap in Observational Studies
    • [cs.LG]Comparing EM with GD in Mixture Models of Two Components
    • [cs.LG]Contextual One-Class Classification in Data Streams
    • [cs.LG]Deep Active Inference as Variational Policy Gradients
    • [cs.LG]Fine-Grained Continual Learning
    • [cs.LG]Latent ODEs for Irregularly-Sampled Time Series
    • [cs.LG]Learning to Optimize Domain Specific Normalization for Domain Generalization
    • [cs.LG]Mean Spectral Normalization of Deep Neural Networks for Embedded Automation
    • [cs.LG]Multi-Scale Vector Quantization with Reconstruction Trees
    • [cs.LG]Non-technical Loss Detection with Statistical Profile Images Based on Semi-supervised Learning
    • [cs.LG]Nonnegative Matrix Factorization with Local Similarity Learning
    • [cs.LG]On Activation Function Coresets for Network Pruning
    • [cs.LG]PathRank: A Multi-Task Learning Framework to Rank Paths in Spatial Networks
    • [cs.LG]Profiling Players with Engagement Predictions
    • [cs.LG]Quantifying Confounding Bias in Neuroimaging Datasets with Causal Inference
    • [cs.LG]Ranking-Based Reward Extrapolation without Rankings
    • [cs.LG]SVGD: A Virtual Gradients Descent Method for Stochastic Optimization
    • [cs.LG]Tensor p-shrinkage nuclear norm for low-rank tensor completion
    • [cs.LG]The Power of Comparisons for Actively Learning Linear Classifiers
    • [cs.LG]The What-If Tool: Interactive Probing of Machine Learning Models
    • [cs.LG]Thompson Sampling for Combinatorial Network Optimization in Unknown Environments
    • [cs.LG]Thompson Sampling on Symmetric $α$-Stable Bandits
    • [cs.LG]Unified Optimal Analysis of the (Stochastic) Gradient Method
    • [cs.LG]Variational Inference MPC for Bayesian Model-based Reinforcement Learning
    • [cs.LG]Which Algorithmic Choices Matter at Which Batch Sizes? Insights From a Noisy Quadratic Model
    • [cs.MM]Barriers towards no-reference metrics application to compressed video quality analysis: on the example of no-reference metric NIQE
    • [cs.NE]A new hybrid genetic algorithm for protein structure prediction on the 2D triangular lattice
    • [cs.NE]Event-based attention and tracking on neuromorphic hardware
    • [cs.NE]Learning in Competitive Network with Haeusslers Equation adapted using FIREFLY algorithm
    • [cs.NE]Melody Generation using an Interactive Evolutionary Algorithm
    • [cs.NE]Procedural Content Generation through Quality Diversity
    • [cs.RO]Assembly Planning by Subassembly Decomposition Using Blocking Reduction
    • [cs.RO]Estimating Mass Distribution of Articulated Objects through Physical Interaction
    • [cs.RO]Incremental Semantic Mapping with Unsupervised On-line Learning
    • [cs.RO]Lidar-based Object Classification with Explicit Occlusion Modeling
    • [cs.RO]Planning for target retrieval using a robotic manipulator in cluttered and occluded environments
    • [cs.RO]RoboWalk: Explicit Augmented Human-Robot Dynamics Modeling for Design Optimization
    • [cs.RO]Sequence-to-Sequence Natural Language to Humanoid Robot Sign Language
    • [cs.RO]Swarm Engineering Through Quantitative Measurement of Swarm Robotic Principles in a 10,000 Robot Swarm
    • [cs.RO]Towards Orientation Learning and Adaptation in Cartesian Space
    • [cs.RO]Towards the Internet of Robotic Things: Analysis, Architecture, Components and Challenges
    • [cs.RO]{Graph Policy Gradients for Large Scale Robot Control
    • [cs.SD]Improving Reverberant Speech Training Using Diffuse Acoustic Simulation
    • [cs.SI]Community Detection on Networks with Ricci Flow
    • [cs.SI]Multitask Learning for Blackmarket Tweet Detection
    • [econ.TH]Competing Models
    • [eess.IV]Brain Tissues Segmentation on MR Perfusion Images Using CUSUM Filter for Boundary Pixels
    • [eess.IV]DSNet: Automatic Dermoscopic Skin Lesion Segmentation
    • [eess.IV]Deep Probabilistic Modeling of Glioma Growth
    • [eess.IV]FC$^2$N: Fully Channel-Concatenated Network for Single Image Super-Resolution
    • [eess.IV]Fully Convolutional Network for Removing DCT Artefacts From Images
    • [eess.IV]Learning from Thresholds: Fully Automated Classification of Tumor Infiltrating Lymphocytes for Multiple Cancer Types
    • [eess.SP]Channel Impulse Response-based Source Localization in a Diffusion-based Molecular Communication System
    • [eess.SP]Decentralized Gaussian Mixture Fusion through Unified Quotient Approximations
    • [eess.SP]Deep Learning Techniques for Improving Digital Gait Segmentation
    • [eess.SP]Functional Brain Networks Discovery Using Dictionary Learning with Correlated Sparsity
    • [eess.SY]Control of Painlevé Paradox in a Robotic System
    • [math.CO]Placement Delivery Arrays from Combinations of Strong Edge Colorings
    • [math.NA]A divide-and-conquer algorithm for binary matrix completion
    • [math.NA]Bayesian approach for inverse obstacle scattering with Poisson data
    • [math.OC]A Hybrid Stochastic Optimization Framework for Stochastic Composite Nonconvex Optimization
    • [math.OC]Empirical Bayesian Learning in AR Graphical Models
    • [math.PR]Posterior Convergence Analysis of $α$-Stable Sheets
    • [math.PR]Scaling Limit of Neural Networks with the Xavier Initialization and Convergence to a Global Minimum
    • [math.ST]A Bayesian Approach for Analyzing Data on the Stiefel Manifold
    • [math.ST]Nonconvex Regularized Robust Regression with Oracle Properties in Polynomial Time
    • [math.ST]Optimal experimental designs for treatment contrasts in heteroscedastic models with covariates
    • [nlin.CG]Universal One-Dimensional Cellular Automata Derived for Turing Machines and its Dynamical Behaviour
    • [physics.soc-ph]Loss of transmission on directed networks due to link deletion
    • [physics.soc-ph]Uncertainty and causal emergence in complex networks
    • [q-bio.QM]Applications of a Novel Knowledge Discovery and Data Mining Process Model for Metabolomics
    • [stat.AP]Predictively Consistent Prior Effective Sample Sizes
    • [stat.AP]Risk models for breast cancer and their validation
    • [stat.AP]Road Maintenance Operation Start Time Optimization Based on Real-time Traffic Map Data
    • [stat.AP]Surrogate modeling of indoor down-link human exposure based on sparse polynomial chaos expansion
    • [stat.ME]A Robust Two-Sample Test for Time Series data
    • [stat.ME]A framework for the pre-specification of statistical analysis strategies in clinical trials (Pre-SPEC)
    • [stat.ME]Adaptive inference for a semiparametric GARCH model
    • [stat.ME]Aggregated False Discovery Rate Control
    • [stat.ME]False Discovery Rates in Biological Networks
    • [stat.ME]Identifying the Influential Inputs for Network Output Variance Using Sparse Polynomial Chaos Expansion
    • [stat.ME]Incremental Intervention Effects in Studies with Many Timepoints, Repeated Outcomes, and Dropout
    • [stat.ME]Residual Entropy
    • [stat.ML]All Sparse PCA Models Are Wrong, But Some Are Useful. Part I: Computation of Scores, Residuals and Explained Variance
    • [stat.ML]Characterizing Inter-Layer Functional Mappings of Deep Learning Models
    • [stat.ML]Conditional Independence Testing using Generative Adversarial Networks
    • [stat.ML]Multivariate Time Series Imputation with Variational Autoencoders
    • [stat.ML]Statistical Analysis of Nearest Neighbor Methods for Anomaly Detection
    • [stat.ML]Understanding Player Engagement and In-Game Purchasing Behavior with Ensemble Learning
    • [stat.ML]k-GANs: Ensemble of Generative Models with Semi-Discrete Optimal Transport
    • [stat.OT]Topological Information Data Analysis

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    • [cs.AI]A Conformance Checking-based Approach for Drift Detection in Business Processes
    Víctor Gallego-Fontenla, Juan C. Vidal, Manuel Lama
    http://arxiv.org/abs/1907.04276v1

    • [cs.AI]A Scheme for Dynamic Risk-Sensitive Sequential Decision Making
    Shuai Ma, Jia Yuan Yu, Ahmet Satir
    http://arxiv.org/abs/1907.04269v1

    • [cs.AI]Diverse Agents for Ad-Hoc Cooperation in Hanabi
    Rodrigo Canaan, Julian Togelius, Andy Nealen, Stefan Menzel
    http://arxiv.org/abs/1907.03840v1

    • [cs.AI]Learning by Abstraction: The Neural State Machine
    Drew A. Hudson, Christopher D. Manning
    http://arxiv.org/abs/1907.03950v1

    • [cs.AI]On the Semantic Interpretability of Artificial Intelligence Models
    Vivian S. Silva, André Freitas, Siegfried Handschuh
    http://arxiv.org/abs/1907.04105v1

    • [cs.CC]Interactive Verifiable Polynomial Evaluation
    Saeid Sahraei, Mohammad Ali Maddah-Ali, Salman Avestimehr
    http://arxiv.org/abs/1907.04302v1

    • [cs.CL]An Intrinsic Nearest Neighbor Analysis of Neural Machine Translation Architectures
    Hamidreza Ghader, Christof Monz
    http://arxiv.org/abs/1907.03885v1

    • [cs.CL]Analyzing Phonetic and Graphemic Representations in End-to-End Automatic Speech Recognition
    Yonatan Belinkov, Ahmed Ali, James Glass
    http://arxiv.org/abs/1907.04224v1

    • [cs.CL]Hahahahaha, Duuuuude, Yeeessss!: A two-parameter characterization of stretchable words and the dynamics of mistypings and misspellings
    Tyler J. Gray, Christopher M. Danforth, Peter Sheridan Dodds
    http://arxiv.org/abs/1907.03920v1

    • [cs.CL]Implicit Discourse Relation Identification for Open-domain Dialogues
    Mingyu Derek Ma, Kevin K. Bowden, Jiaqi Wu, Wen Cui, Marilyn Walker
    http://arxiv.org/abs/1907.03975v1

    • [cs.CL]Improving short text classification through global augmentation methods
    Vukosi Marivate, Tshephisho Sefara
    http://arxiv.org/abs/1907.03752v1

    • [cs.CL]Learning Neural Sequence-to-Sequence Models from Weak Feedback with Bipolar Ramp Loss
    Laura Jehl, Carolin Lawrence, Stefan Riezler
    http://arxiv.org/abs/1907.03748v1

    • [cs.CL]Multilingual Universal Sentence Encoder for Semantic Retrieval
    Yinfei Yang, Daniel Cer, Amin Ahmad, Mandy Guo, Jax Law, Noah Constant, Gustavo Hernandez Abrego, Steve Yuan, Chris Tar, Yun-Hsuan Sung, Brian Strope, Ray Kurzweil
    http://arxiv.org/abs/1907.04307v1

    • [cs.CL]NTT’s Machine Translation Systems for WMT19 Robustness Task
    Soichiro Murakami, Makoto Morishita, Tsutomu Hirao, Masaaki Nagata
    http://arxiv.org/abs/1907.03927v1

    • [cs.CL]Neural Aspect and Opinion Term Extraction with Mined Rules as Weak Supervision
    Hongliang Dai, Yangqiu Song
    http://arxiv.org/abs/1907.03750v1

    • [cs.CL]Sentiment and position-taking analysis of parliamentary debates: A systematic literature review
    Gavin Abercrombie, Riza Batista-Navarro
    http://arxiv.org/abs/1907.04126v1

    • [cs.CL]Systematic quantitative analyses reveal the folk-zoological knowledge embedded in folktales
    Yo Nakawake, Kosuke Sato
    http://arxiv.org/abs/1907.03969v1

    • [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/1907.04245v1

    • [cs.CR]Private key encryption and recovery in blockchain
    Mehmet Aydar, Salih Cemil Cetin, Serkan Ayvaz, Betul Aygun
    http://arxiv.org/abs/1907.04156v1

    • [cs.CR]Security for Distributed Deep Neural Networks Towards Data Confidentiality & Intellectual Property Protection
    Laurent Gomez, Marcus Wilhelm, José Márquez, Patrick Duverger
    http://arxiv.org/abs/1907.04246v1

    • [cs.CR]Using Temporal and Topological Features for Intrusion Detection in Operational Networks
    Simon D. Duque Anton, Daniel Fraunholz, Hans Dieter Schotten
    http://arxiv.org/abs/1907.04098v1

    • [cs.CV]3D pavement surface reconstruction using an RGB-D sensor
    Ahmadreza Mahmoudzadeh, Sayna Firoozi Yeganeh, Amir Golroo
    http://arxiv.org/abs/1907.04124v1

    • [cs.CV]A Baseline for 3D Multi-Object Tracking
    Xinshuo Weng, Kris Kitani
    http://arxiv.org/abs/1907.03961v1

    • [cs.CV]A Light weight and Hybrid Deep Learning Model based Online Signature Verification
    Chandra Sekhar V., Anoushka Doctor, Prerana Mukherjee, Viswanath Pulabaigiri
    http://arxiv.org/abs/1907.04061v1

    • [cs.CV]Accurate Nuclear Segmentation with \Center Vector Encoding
    Jiahui Li, Zhiqiang Hu, Shuang Yang
    http://arxiv.org/abs/1907.03951v1

    • [cs.CV]Adaptive Exploration for Unsupervised Person Re-Identification
    Yuhang Ding, Hehe Fan, Mingliang Xu, Yi Yang
    http://arxiv.org/abs/1907.04194v1

    • [cs.CV]Attentive CT Lesion Detection Using Deep Pyramid Inference with Multi-Scale Booster
    Qingbin Shao, Lijun Gong, Kai Ma, Hualuo Liu, Yefeng Zheng
    http://arxiv.org/abs/1907.03958v1

    • [cs.CV]BADAM: A Public Dataset for Baseline Detection in Arabic-script Manuscripts
    Benjamin Kiessling, Daniel Stökl Ben Ezra, Matthew Thomas Miller
    http://arxiv.org/abs/1907.04041v1

    • [cs.CV]Deep Learning for Spacecraft Pose Estimation from Photorealistic Rendering
    Pedro F. Proenca, Yang Gao
    http://arxiv.org/abs/1907.04298v1

    • [cs.CV]Deep Pixel-wise Binary Supervision for Face Presentation Attack Detection
    Anjith George, Sebastien Marcel
    http://arxiv.org/abs/1907.04047v1

    • [cs.CV]Depth from Small Motion using Rank-1 Initialization
    Peter O. Fasogbon
    http://arxiv.org/abs/1907.04058v1

    • [cs.CV]Distill-2MD-MTL: Data Distillation based on Multi-Dataset Multi-Domain Multi-Task Frame Work to Solve Face Related Tasksks, Multi Task Learning, Semi-Supervised Learning
    Sepidehsadat Hosseini, Mohammad Amin Shabani, Nam Ik Cho
    http://arxiv.org/abs/1907.03402v2

    • [cs.CV]Domain Adaptation in Multi-Channel Autoencoder based Features for Robust Face Anti-Spoofing
    Olegs Nikisins, Anjith George, Sebastien Marcel
    http://arxiv.org/abs/1907.04048v1

    • [cs.CV]Efficient Pose Selection for Interactive Camera Calibration
    Pavel Rojtberg, Arjan Kuijper
    http://arxiv.org/abs/1907.04096v1

    • [cs.CV]Fast Visual Object Tracking with Rotated Bounding Boxes
    Bao Xin Chen, John K. Tsotsos
    http://arxiv.org/abs/1907.03892v1

    • [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/1907.04253v1

    • [cs.CV]Improving Deep Lesion Detection Using 3D Contextual and Spatial Attention
    Qingyi Tao, Zongyuan Ge, Jianfei Cai, Jianxiong Yin, Simon See
    http://arxiv.org/abs/1907.04052v1

    • [cs.CV]On the Exact Recovery Conditions of 3D Human Motion from 2D Landmark Motion with Sparse Articulated Motion
    Abed Malti
    http://arxiv.org/abs/1907.03967v1

    • [cs.CV]Personalised aesthetics with residual adapters
    Carlos Rodríguez - Pardo, Hakan Bilen
    http://arxiv.org/abs/1907.03802v1

    • [cs.CV]Positional Normalization
    Boyi Li, Felix Wu, Kilian Q. Weinberger, Serge Belongie
    http://arxiv.org/abs/1907.04312v1

    • [cs.CV]Signet Ring Cell Detection With a Semi-supervised Learning Framework
    Jiahui Li, Shuang Yang, Xiaodi Huang, Qian Da, Xiaoqun Yang, Zhiqiang Hu, Qi Duan, Chaofu Wang, Hongsheng Li
    http://arxiv.org/abs/1907.03954v1

    • [cs.CV]Sparse-to-Dense Hypercolumn Matching for Long-Term Visual Localization
    Hugo Germain, Guillaume Bourmaud, Vincent Lepetit
    http://arxiv.org/abs/1907.03965v1

    • [cs.CV]Template-Based Posit Multiplication for Training and Inferring in Neural Networks
    Raúl Murillo Montero, Alberto A. Del Barrio, Guillermo Botella
    http://arxiv.org/abs/1907.04091v1

    • [cs.CV]UnsuperPoint: End-to-end Unsupervised Interest Point Detector and Descriptor
    Peter Hviid Christiansen, Mikkel Fly Kragh, Yury Brodskiy, Henrik Karstoft
    http://arxiv.org/abs/1907.04011v1

    • [cs.CV]calibDB: enabling web based computer vision through on-the-fly camera calibration
    Pavel Rojtberg, Felix Gorschlüter
    http://arxiv.org/abs/1907.04100v1

    • [cs.CY]Characterizing Bitcoin donations to open source software on GitHub
    Yury Zhauniarovich, Yazan Boshmaf, Husam Al Jawaheri, Mashael Al Sabah
    http://arxiv.org/abs/1907.04002v1

    • [cs.CY]Sharing and Learning Alloy on the Web
    Nuno Macedo, Alcino Cunha, José Pereira, Renato Carvalho, Ricardo Silva, Ana C. R. Paiva, Miguel S. Ramalho, Daniel Silva
    http://arxiv.org/abs/1907.02275v1

    • [cs.DC]Bi-objective Optimisation of Data-parallel Applications on Heterogeneous Platforms for Performance and Energy via Workload Distribution
    Hamidreza Khaleghzadeh, Muhammad Fahad, Arsalan Shahid, Ravi Reddy Manumachu, Alexey Lastovetsky
    http://arxiv.org/abs/1907.04080v1

    • [cs.DC]Streaming 1.9 Billion Hypersparse Network Updates per Second with D4M
    Jeremy Kepner, Vijay Gadepally, Lauren Milechin, Siddharth Samsi, William Arcand, David Bestor, William Bergeron, Chansup Byun, Matthew Hubbell, Michael Houle, Michael Jones, Anne Klein, Peter Michaleas, Julie Mullen, Andrew Prout, Antonio Rosa, Charles Yee, Albert Reuther
    http://arxiv.org/abs/1907.04217v1

    • [cs.DS]Faster Deterministic Distributed Coloring Through Recursive List Coloring
    Fabian Kuhn
    http://arxiv.org/abs/1907.03797v1

    • [cs.DS]Near-optimal Repair of Reed-Solomon Codes with Low Sub-packetization
    Venkatesan Guruswami, Haotian Jiang
    http://arxiv.org/abs/1907.03931v1

    • [cs.GR]Efficient Cloth Simulation using Miniature Cloth and Upscaling Deep Neural Networks
    Tae Min Lee, Young Jin Oh, In-Kwon Lee
    http://arxiv.org/abs/1907.03953v1

    • [cs.HC]Belief places and spaces: Mapping cognitive environments
    Philip Feldman, Aaron Dant, Wayne Lutters
    http://arxiv.org/abs/1907.04191v1

    • [cs.HC]Pitako — Recommending Game Design Elements in Cicero
    Tiago Machado, Dan Gopstein, Andy Nealen, Julian Togelius
    http://arxiv.org/abs/1907.03877v1

    • [cs.HC]Translating neural signals to text using a Brain-Machine Interface
    Janaki Sheth, Ariel Tankus, Michelle Tran, Nader Pouratian, Itzhak Fried, William Speier
    http://arxiv.org/abs/1907.04265v1

    • [cs.IR]An Attention Mechanism for Musical Instrument Recognition
    Siddharth Gururani, Mohit Sharma, Alexander Lerch
    http://arxiv.org/abs/1907.04294v1

    • [cs.IR]UW-BHI at MEDIQA 2019: An Analysis of Representation Methods for Medical Natural Language Inference
    William R. Kearns, Wilson Lau, Jason A. Thomas
    http://arxiv.org/abs/1907.04286v1

    • [cs.IT]A Simple Derivation of AMP and its State Evolution via First-Order Cancellation
    Philip Schniter
    http://arxiv.org/abs/1907.04235v1

    • [cs.IT]Collaborative Machine Learning at the Wireless Edge with Blind Transmitters
    Mohammad Mohammadi Amiri, Tolga M. Duman, Deniz Gunduz
    http://arxiv.org/abs/1907.03909v1

    • [cs.IT]Control of Status Updates for Energy Harvesting Devices that Monitor Processes with Alarms
    George Stamatakis, Nikolaos Pappas, Apostolos Traganitis
    http://arxiv.org/abs/1907.03826v1

    • [cs.IT]Deep Learning-Aided Dynamic Read Thresholds Design For Multi-Level-Cell Flash Memories
    Zhen Mei, Kui Cai, Xuan He
    http://arxiv.org/abs/1907.03938v1

    • [cs.IT]Distributed Approximation of Functions over Fast Fading Channels with Applications to Distributed Learning and the Max-Consensus Problem
    Igor Bjelakovic, Matthias Frey, Slawomir Stanczak
    http://arxiv.org/abs/1907.03777v1

    • [cs.IT]Fundamental limits of quantum-secure covert communication over bosonic channels
    Michael S. Bullock, Christos N. Gagatsos, Saikat Guha, Boulat A. Bash
    http://arxiv.org/abs/1907.04228v1

    • [cs.IT]On the 3-D Placement of Airborne Base Stations Using Tethered UAVs
    Mustafa A. Kishk, Ahmed Bader, Mohamed-Slim Alouini
    http://arxiv.org/abs/1907.04299v1

    • [cs.LG]Adversarial Fault Tolerant Training for Deep Neural Networks
    Vasisht Duddu, D. Vijay Rao, Valentina E. Balas
    http://arxiv.org/abs/1907.03103v2

    • [cs.LG]Are deep ResNets provably better than linear predictors?
    Chulhee Yun, Suvrit Sra, Ali Jadbabaie
    http://arxiv.org/abs/1907.03922v1

    • [cs.LG]Asymptotic Bayes risk for Gaussian mixture in a semi-supervised setting
    Marc Lelarge, Leo Miolane
    http://arxiv.org/abs/1907.03792v1

    • [cs.LG]Attending to Emotional Narratives
    Zhengxuan Wu, Xiyu Zhang, Tan Zhi-Xuan, Jamil Zaki, Desmond C. Ong
    http://arxiv.org/abs/1907.04197v1

    • [cs.LG]Characterization of Overlap in Observational Studies
    Fredrik D. Johansson, Dennis Wei, Michael Oberst, Tian Gao, Gabriel Brat, David Sontag, Kush R. Varshney
    http://arxiv.org/abs/1907.04138v1

    • [cs.LG]Comparing EM with GD in Mixture Models of Two Components
    Guojun Zhang, Pascal Poupart, George Trimponias
    http://arxiv.org/abs/1907.03783v1

    • [cs.LG]Contextual One-Class Classification in Data Streams
    Richard Hugh Moulton, Herna L. Viktor, Nathalie Japkowicz, João Gama
    http://arxiv.org/abs/1907.04233v1

    • [cs.LG]Deep Active Inference as Variational Policy Gradients
    Beren Millidge
    http://arxiv.org/abs/1907.03876v1

    • [cs.LG]Fine-Grained Continual Learning
    Vincenzo Lomonaco, Davide Maltoni, Lorenzo Pellegrini
    http://arxiv.org/abs/1907.03799v1

    • [cs.LG]Latent ODEs for Irregularly-Sampled Time Series
    Yulia Rubanova, Ricky T. Q. Chen, David Duvenaud
    http://arxiv.org/abs/1907.03907v1

    • [cs.LG]Learning to Optimize Domain Specific Normalization for Domain Generalization
    Seonguk Seo, Yumin Suh, Dongwan Kim, Jongwoo Han, Bohyung Han
    http://arxiv.org/abs/1907.04275v1

    • [cs.LG]Mean Spectral Normalization of Deep Neural Networks for Embedded Automation
    Anand Krishnamoorthy Subramanian, Nak Young Chong
    http://arxiv.org/abs/1907.04003v1

    • [cs.LG]Multi-Scale Vector Quantization with Reconstruction Trees
    Enrico Cecini, Ernesto De Vito, Lorenzo Rosasco
    http://arxiv.org/abs/1907.03875v1

    • [cs.LG]Non-technical Loss Detection with Statistical Profile Images Based on Semi-supervised Learning
    Jiangteng Li, Fei Wang
    http://arxiv.org/abs/1907.03925v1

    • [cs.LG]Nonnegative Matrix Factorization with Local Similarity Learning
    Chong Peng, Zhao Kang, Chenglizhao Chen, Qiang Cheng
    http://arxiv.org/abs/1907.04150v1

    • [cs.LG]On Activation Function Coresets for Network Pruning
    Ben Mussay, Samson Zhou, Vladimir Braverman, Dan Feldman
    http://arxiv.org/abs/1907.04018v1

    • [cs.LG]PathRank: A Multi-Task Learning Framework to Rank Paths in Spatial Networks
    Sean Bin Yang, Bin Yang
    http://arxiv.org/abs/1907.04028v1

    • [cs.LG]Profiling Players with Engagement Predictions
    Ana Fernández del Río, Pei Pei Chen, África Periáñez
    http://arxiv.org/abs/1907.03870v1

    • [cs.LG]Quantifying Confounding Bias in Neuroimaging Datasets with Causal Inference
    Christian Wachinger, Benjamin Gutierrez Becker, Anna Rieckmann, Sebastian Pölsterl
    http://arxiv.org/abs/1907.04102v1

    • [cs.LG]Ranking-Based Reward Extrapolation without Rankings
    Daniel S. Brown, Wonjoon Goo, Scott Niekum
    http://arxiv.org/abs/1907.03976v1

    • [cs.LG]SVGD: A Virtual Gradients Descent Method for Stochastic Optimization
    Zheng Li, Shi Shu
    http://arxiv.org/abs/1907.04021v1

    • [cs.LG]Tensor p-shrinkage nuclear norm for low-rank tensor completion
    Chunsheng Liu, Hong Shan, Chunlei Chen
    http://arxiv.org/abs/1907.04092v1

    • [cs.LG]The Power of Comparisons for Actively Learning Linear Classifiers
    Max Hopkins, Daniel M. Kane, Shachar Lovett
    http://arxiv.org/abs/1907.03816v1

    • [cs.LG]The What-If Tool: Interactive Probing of Machine Learning Models
    James Wexler, Mahima Pushkarna, Tolga Bolukbasi, Martin Wattenberg, Fernanda Viegas, Jimbo Wilson
    http://arxiv.org/abs/1907.04135v1

    • [cs.LG]Thompson Sampling for Combinatorial Network Optimization in Unknown Environments
    Alihan Hüyük, Cem Tekin
    http://arxiv.org/abs/1907.04201v1

    • [cs.LG]Thompson Sampling on Symmetric $α$-Stable Bandits
    Abhimanyu Dubey, Alex Pentland
    http://arxiv.org/abs/1907.03821v1

    • [cs.LG]Unified Optimal Analysis of the (Stochastic) Gradient Method
    Sebastian U. Stich
    http://arxiv.org/abs/1907.04232v1

    • [cs.LG]Variational Inference MPC for Bayesian Model-based Reinforcement Learning
    Masashi Okada, Tadahiro Taniguchi
    http://arxiv.org/abs/1907.04202v1

    • [cs.LG]Which Algorithmic Choices Matter at Which Batch Sizes? Insights From a Noisy Quadratic Model
    Guodong Zhang, Lala Li, Zachary Nado, James Martens, Sushant Sachdeva, George E. Dahl, Christopher J. Shallue, Roger Grosse
    http://arxiv.org/abs/1907.04164v1

    • [cs.MM]Barriers towards no-reference metrics application to compressed video quality analysis: on the example of no-reference metric NIQE
    Anastasia Antsiferova, Dmitriy Kulikov, Denis Kondranin, Dmitriy Vatolin
    http://arxiv.org/abs/1907.03842v1

    • [cs.NE]A new hybrid genetic algorithm for protein structure prediction on the 2D triangular lattice
    Nabil Boumedine, Sadek Bouroubi
    http://arxiv.org/abs/1907.04190v1

    • [cs.NE]Event-based attention and tracking on neuromorphic hardware
    Alpha Renner, Matthew Evanusa, Yulia Sandamirskaya
    http://arxiv.org/abs/1907.04060v1

    • [cs.NE]Learning in Competitive Network with Haeusslers Equation adapted using FIREFLY algorithm
    N. Joshi
    http://arxiv.org/abs/1907.04160v1

    • [cs.NE]Melody Generation using an Interactive Evolutionary Algorithm
    Majid Farzaneh, Rahil Mahdian Toroghi
    http://arxiv.org/abs/1907.04258v1

    • [cs.NE]Procedural Content Generation through Quality Diversity
    Daniele Gravina, Ahmed Khalifa, Antonios Liapis, Julian Togelius, Georgios N. Yannakakis
    http://arxiv.org/abs/1907.04053v1

    • [cs.RO]Assembly Planning by Subassembly Decomposition Using Blocking Reduction
    James Watson, Tucker Hermans
    http://arxiv.org/abs/1907.03835v1

    • [cs.RO]Estimating Mass Distribution of Articulated Objects through Physical Interaction
    Niranjan Kumar Kannabiran, Irfan Essa, C. Karen Liu
    http://arxiv.org/abs/1907.03964v1

    • [cs.RO]Incremental Semantic Mapping with Unsupervised On-line Learning
    Ygor C. N. Sousa, Hansenclever F. Bassani
    http://arxiv.org/abs/1907.04001v1

    • [cs.RO]Lidar-based Object Classification with Explicit Occlusion Modeling
    Xiaoxiang Zhang, Hao Fu, Bin Dai
    http://arxiv.org/abs/1907.04057v1

    • [cs.RO]Planning for target retrieval using a robotic manipulator in cluttered and occluded environments
    Changjoo Nam, Jinhwi Lee, Younggil Cho, Jeongho Lee, Dong Hwan Kim, ChangHwan Kim
    http://arxiv.org/abs/1907.03956v1

    • [cs.RO]RoboWalk: Explicit Augmented Human-Robot Dynamics Modeling for Design Optimization
    S. Ali A. Moosavian, Mahdi Nabipour, Farshid Absalan, Vahdi Akbari
    http://arxiv.org/abs/1907.04114v1

    • [cs.RO]Sequence-to-Sequence Natural Language to Humanoid Robot Sign Language
    Jennifer J. Gago, Valentina Vasco, Bartek Łukawski, Ugo Pattacini, Vadim Tikhanoff, Juan G. Victores, Carlos Balaguer
    http://arxiv.org/abs/1907.04198v1

    • [cs.RO]Swarm Engineering Through Quantitative Measurement of Swarm Robotic Principles in a 10,000 Robot Swarm
    John Harwell, Maria Gini
    http://arxiv.org/abs/1907.03880v1

    • [cs.RO]Towards Orientation Learning and Adaptation in Cartesian Space
    Yanlong Huang, Fares J. Abu-Dakka, João Silvério, Darwin G. Caldwell
    http://arxiv.org/abs/1907.03918v1

    • [cs.RO]Towards the Internet of Robotic Things: Analysis, Architecture, Components and Challenges
    Ilya Afanasyev, Manuel Mazzara, Subham Chakraborty, Nikita Zhuchkov, Aizhan Maksatbek, Mohamad Kassab, Salvatore Distefano
    http://arxiv.org/abs/1907.03817v1

    • [cs.RO]{Graph Policy Gradients for Large Scale Robot Control
    Arbaaz Khan, Ekaterina Tolstaya, Alejandro Ribeiro, Vijay Kumar
    http://arxiv.org/abs/1907.03822v1

    • [cs.SD]Improving Reverberant Speech Training Using Diffuse Acoustic Simulation
    Zhenyu Tang, Lianwu Chen, Bo Wu, Dong Yu, Dinesh Manocha
    http://arxiv.org/abs/1907.03988v1

    • [cs.SI]Community Detection on Networks with Ricci Flow
    Chien-Chun Ni, Yu-Yao Lin, Feng Luo, Jie Gao
    http://arxiv.org/abs/1907.03993v1

    • [cs.SI]Multitask Learning for Blackmarket Tweet Detection
    Udit Arora, William Scott Paka, Tanmoy Chakraborty
    http://arxiv.org/abs/1907.04072v1

    • [econ.TH]Competing Models
    Jose Luis Montiel Olea, Pietro Ortoleva, Mallesh M Pai, Andrea Prat
    http://arxiv.org/abs/1907.03809v1

    • [eess.IV]Brain Tissues Segmentation on MR Perfusion Images Using CUSUM Filter for Boundary Pixels
    S. M. Alkhimova, A. P. Krenevych
    http://arxiv.org/abs/1907.03865v1

    • [eess.IV]DSNet: Automatic Dermoscopic Skin Lesion Segmentation
    Md. Kamrul Hasan, Lavsen Dahal, Prasad N. Samarakoon, Fakrul Islam Tushar, Robert Marti Marly
    http://arxiv.org/abs/1907.04305v1

    • [eess.IV]Deep Probabilistic Modeling of Glioma Growth
    Jens Petersen, Paul F. Jäger, Fabian Isensee, Simon A. A. Kohl, Ulf Neuberger, Wolfgang Wick, Jürgen Debus, Sabine Heiland, Martin Bendszus, Philipp Kickingereder, Klaus H. Maier-Hein
    http://arxiv.org/abs/1907.04064v1

    • [eess.IV]FC$^2$N: Fully Channel-Concatenated Network for Single Image Super-Resolution
    Xiaole Zhao, Ying Liao, Ye Li, Tao Zhang, Xueming Zou
    http://arxiv.org/abs/1907.03221v2

    • [eess.IV]Fully Convolutional Network for Removing DCT Artefacts From Images
    Patryk Najgebauer, Rafal Scherer
    http://arxiv.org/abs/1907.03798v1

    • [eess.IV]Learning from Thresholds: Fully Automated Classification of Tumor Infiltrating Lymphocytes for Multiple Cancer Types
    Shahira Abousamra, Le Hou, Rajarsi Gupta, Chao Chen, Dimitris Samaras, Tahsin Kurc, Rebecca Batiste, Tianhao Zhao, Shroyer Kenneth, Joel Saltz
    http://arxiv.org/abs/1907.03960v1

    • [eess.SP]Channel Impulse Response-based Source Localization in a Diffusion-based Molecular Communication System
    Henry Ernest Baidoo-Williams, Muhammad Mahboob Ur Rahman, Qammer Hussain Abbasi
    http://arxiv.org/abs/1907.04239v1

    • [eess.SP]Decentralized Gaussian Mixture Fusion through Unified Quotient Approximations
    Nisar R. Ahmed
    http://arxiv.org/abs/1907.04008v1

    • [eess.SP]Deep Learning Techniques for Improving Digital Gait Segmentation
    Matteo Gadaleta, Giulia Cisotto, Michele Rossi, Rana Zia Ur Rehman, Lynn Rochester, Silvia Del Din
    http://arxiv.org/abs/1907.04281v1

    • [eess.SP]Functional Brain Networks Discovery Using Dictionary Learning with Correlated Sparsity
    Mohsen Joneidi
    http://arxiv.org/abs/1907.03929v1

    • [eess.SY]Control of Painlevé Paradox in a Robotic System
    Davide Marchese, Marco Coraggio, S. John Hogan, Mario di Bernardo
    http://arxiv.org/abs/1907.04070v1

    • [math.CO]Placement Delivery Arrays from Combinations of Strong Edge Colorings
    Jerod Michel, Qi Wang
    http://arxiv.org/abs/1907.03177v2

    • [math.NA]A divide-and-conquer algorithm for binary matrix completion
    Melanie Beckerleg, Andrew Thompson
    http://arxiv.org/abs/1907.04251v1

    • [math.NA]Bayesian approach for inverse obstacle scattering with Poisson data
    Xiaomei Yang, Zhiliang Deng
    http://arxiv.org/abs/1907.03955v1

    • [math.OC]A Hybrid Stochastic Optimization Framework for Stochastic Composite Nonconvex Optimization
    Quoc Tran-Dinh, Nhan H. Pham, Dzung T. Phan, Lam M. Nguyen
    http://arxiv.org/abs/1907.03793v1

    • [math.OC]Empirical Bayesian Learning in AR Graphical Models
    Mattia Zorzi
    http://arxiv.org/abs/1907.03829v1

    • [math.PR]Posterior Convergence Analysis of $α$-Stable Sheets
    Neil K. Chada, Sari Lasanen, Lassi Roininen
    http://arxiv.org/abs/1907.03086v2

    • [math.PR]Scaling Limit of Neural Networks with the Xavier Initialization and Convergence to a Global Minimum
    Justin Sirignano, Konstantinos Spiliopoulos
    http://arxiv.org/abs/1907.04108v1

    • [math.ST]A Bayesian Approach for Analyzing Data on the Stiefel Manifold
    Subhadip Pal, Subhajit Sengupta, Riten Mitra, Arunava Banerjee
    http://arxiv.org/abs/1907.04303v1

    • [math.ST]Nonconvex Regularized Robust Regression with Oracle Properties in Polynomial Time
    Xiaoou Pan, Qiang Sun, Wen-Xin Zhou
    http://arxiv.org/abs/1907.04027v1

    • [math.ST]Optimal experimental designs for treatment contrasts in heteroscedastic models with covariates
    Samuel Rosa
    http://arxiv.org/abs/1907.04044v1

    • [nlin.CG]Universal One-Dimensional Cellular Automata Derived for Turing Machines and its Dynamical Behaviour
    Sergio J. Martinez, Ivan M. Mendoza, Genaro J. Martinez, Shigeru Ninagawa
    http://arxiv.org/abs/1907.04211v1

    • [physics.soc-ph]Loss of transmission on directed networks due to link deletion
    G. Kashyap, G. Ambika
    http://arxiv.org/abs/1907.04007v1

    • [physics.soc-ph]Uncertainty and causal emergence in complex networks
    Brennan Klein, Erik Hoel
    http://arxiv.org/abs/1907.03902v1

    • [q-bio.QM]Applications of a Novel Knowledge Discovery and Data Mining Process Model for Metabolomics
    Ahmed BaniMustafa, Nigel Hardy
    http://arxiv.org/abs/1907.03755v1

    • [stat.AP]Predictively Consistent Prior Effective Sample Sizes
    Beat Neuenschwander, Sebastian Weber, Heinz Schmidli, Anthony O’Hagan
    http://arxiv.org/abs/1907.04185v1

    • [stat.AP]Risk models for breast cancer and their validation
    Adam R Brentnall, Jack Cuzick
    http://arxiv.org/abs/1907.02829v2

    • [stat.AP]Road Maintenance Operation Start Time Optimization Based on Real-time Traffic Map Data
    Zhepu Xu, Qun Yang
    http://arxiv.org/abs/1907.03814v1

    • [stat.AP]Surrogate modeling of indoor down-link human exposure based on sparse polynomial chaos expansion
    Zicheng Liu, Dominique Lesselier, Bruno Sudret, Joe Wiart
    http://arxiv.org/abs/1907.03933v1

    • [stat.ME]A Robust Two-Sample Test for Time Series data
    Alexis Bellot, Mihaela van der Schaar
    http://arxiv.org/abs/1907.04081v1

    • [stat.ME]A framework for the pre-specification of statistical analysis strategies in clinical trials (Pre-SPEC)
    Brennan C Kahan, Gordon Forbes, Suzie Cro
    http://arxiv.org/abs/1907.04078v1

    • [stat.ME]Adaptive inference for a semiparametric GARCH model
    Feiyu Jiang, Dong Li, Ke Zhu
    http://arxiv.org/abs/1907.04147v1

    • [stat.ME]Aggregated False Discovery Rate Control
    Fang Xie, Johannes Lederer
    http://arxiv.org/abs/1907.03807v1

    • [stat.ME]False Discovery Rates in Biological Networks
    Lu Yu, Tobias Kaufmann, Johannes Lederer
    http://arxiv.org/abs/1907.03808v1

    • [stat.ME]Identifying the Influential Inputs for Network Output Variance Using Sparse Polynomial Chaos Expansion
    Zhanlin Liu, Ashis G. Banerjee, Youngjun Choe
    http://arxiv.org/abs/1907.04266v1

    • [stat.ME]Incremental Intervention Effects in Studies with Many Timepoints, Repeated Outcomes, and Dropout
    Kwangho Kim, Edward H. Kennedy, Ashley I. Naimi
    http://arxiv.org/abs/1907.04004v1

    • [stat.ME]Residual Entropy
    Barnaby Rowe
    http://arxiv.org/abs/1907.03888v1

    • [stat.ML]All Sparse PCA Models Are Wrong, But Some Are Useful. Part I: Computation of Scores, Residuals and Explained Variance
    J. Camacho, A. K. Smilde, E. Saccenti, J. A. Westerhuis
    http://arxiv.org/abs/1907.03989v1

    • [stat.ML]Characterizing Inter-Layer Functional Mappings of Deep Learning Models
    Donald Waagen, Katie Rainey, Jamie Gantert, David Gray, Megan King, M. Shane Thompson, Jonathan Barton, Will Waldron, Samantha Livingston, Don Hulsey
    http://arxiv.org/abs/1907.04223v1

    • [stat.ML]Conditional Independence Testing using Generative Adversarial Networks
    Alexis Bellot, Mihaela van der Schaar
    http://arxiv.org/abs/1907.04068v1

    • [stat.ML]Multivariate Time Series Imputation with Variational Autoencoders
    Vincent Fortuin, Gunnar Rätsch, Stephan Mandt
    http://arxiv.org/abs/1907.04155v1

    • [stat.ML]Statistical Analysis of Nearest Neighbor Methods for Anomaly Detection
    Xiaoyi Gu, Leman Akoglu, Alessandro Rinaldo
    http://arxiv.org/abs/1907.03813v1

    • [stat.ML]Understanding Player Engagement and In-Game Purchasing Behavior with Ensemble Learning
    Anna Guitart, Ana Fernández del Río, África Periáñez
    http://arxiv.org/abs/1907.03947v1

    • [stat.ML]k-GANs: Ensemble of Generative Models with Semi-Discrete Optimal Transport
    Luca Ambrogioni, Umut Güçlü, Marcel van Gerven
    http://arxiv.org/abs/1907.04050v1

    • [stat.OT]Topological Information Data Analysis
    Pierre Baudot, Monica Tapia, Daniel Bennequin, Jean-Marc Goaillard
    http://arxiv.org/abs/1907.04242v1