astro-ph.EP - 地球与行星天体

    cond-mat.stat-mech - 统计数学 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.DB - 数据库 cs.DC - 分布式、并行与集群计算 cs.DS - 数据结构与算法 cs.GL - 一般文献 cs.IR - 信息检索 cs.IT - 信息论 cs.LG - 自动学习 cs.MA - 多代理系统 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 eess.AS - 语音处理 eess.IV - 图像与视频处理 eess.SP - 信号处理 math.FA - 泛函演算 math.HO - 历史与概述 math.OC - 优化与控制 math.PR - 概率 math.ST - 统计理论 physics.chem-ph -化学物理 q-bio.NC - 神经元与认知 q-bio.QM - 定量方法 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

    • [astro-ph.EP]Bias and robustness of eccentricity estimates from radial velocity data
    • [cond-mat.stat-mech]Minimum Power to Maintain a Nonequilibrium Distribution of a Markov Chain
    • [cs.AI]Evolving the Hearthstone Meta
    • [cs.AI]On Conflicting and Conflicting Values
    • [cs.AI]Perspective Taking in Deep Reinforcement Learning Agents
    • [cs.AI]Recommendations on Designing Practical Interval Type-2 Fuzzy Systems
    • [cs.AI]Using Bi-Directional Information Exchange to Improve Decentralized Schedule-Driven Traffic Control
    • [cs.CC]Efficient Circuit Simulation in MapReduce
    • [cs.CL]CS563-QA: A Collection for Evaluating Question Answering Systems
    • [cs.CL]Constructing large scale biomedical knowledge bases from scratch with rapid annotation of interpretable patterns
    • [cs.CL]Data mining Mandarin tone contour shapes
    • [cs.CL]Deep neural network-based classification model for Sentiment Analysis
    • [cs.CL]Depth Growing for Neural Machine Translation
    • [cs.CL]Multi-Task Networks With Universe, Group, and Task Feature Learning
    • [cs.CL]MultiWOZ 2.1: Multi-Domain Dialogue State Corrections and State Tracking Baselines
    • [cs.CL]On the Weaknesses of Reinforcement Learning for Neural Machine Translation
    • [cs.CL]Polyphone Disambiguation for Mandarin Chinese Using Conditional Neural Network with Multi-level Embedding Features
    • [cs.CL]Real-time Claim Detection from News Articles and Retrieval of Semantically-Similar Factchecks
    • [cs.CL]Scalable Multi Corpora Neural Language Models for ASR
    • [cs.CR]Gathering Cyber Threat Intelligence from Twitter Using Novelty Classification
    • [cs.CV]A Deep Image Compression Framework for Face Recognition
    • [cs.CV]A Semi-Supervised Framework for Automatic Pixel-Wise Breast Cancer Grading of Histological Images
    • [cs.CV]Attention routing between capsules
    • [cs.CV]Cascade Attention Guided Residue Learning GAN for Cross-Modal Translation
    • [cs.CV]Chasing Ghosts: Instruction Following as Bayesian State Tracking
    • [cs.CV]Deformable Tube Network for Action Detection in Videos
    • [cs.CV]Hyper-Molecules: on the Representation and Recovery of Dynamical Structures, with Application to Flexible Macro-Molecular Structures in Cryo-EM
    • [cs.CV]Learning Landmarks from Unaligned Data using Image Translation
    • [cs.CV]Learning to Predict Robot Keypoints Using Artificially Generated Images
    • [cs.CV]Learning to aggregate feature representations
    • [cs.CV]Mask Embedding in conditional GAN for Guided Synthesis of High Resolution Images
    • [cs.CV]Novel evaluation of surgical activity recognition models using task-based efficiency metrics
    • [cs.CV]Probabilistic Multilayer Regularization Network for Unsupervised 3D Brain Image Registration
    • [cs.CV]Robust Synthesis of Adversarial Visual Examples Using a Deep Image Prior
    • [cs.CV]Semi-supervised Image Attribute Editing using Generative Adversarial Networks
    • [cs.CV]Simple vs complex temporal recurrences for video saliency prediction
    • [cs.CV]SkeletonNet: Shape Pixel to Skeleton Pixel
    • [cs.CV]Super-Resolution of PROBA-V Images Using Convolutional Neural Networks
    • [cs.CV]Tracking system of Mine Patrol Robot for Low Illumination Environment
    • [cs.CV]Unsupervised Anomalous Trajectory Detection for Crowded Scenes
    • [cs.CV]Using Deep Learning to Count Albatrosses from Space
    • [cs.CY]Beyond content analysis: Detecting targeted ads via distributed counting
    • [cs.CY]Quantifying Algorithmic Biases over Time
    • [cs.DB]Rule Applicability on RDF Triplestore Schemas
    • [cs.DC]Koalja: from Data Plumbing to Smart Workspaces in the Extended Cloud
    • [cs.DC]On-Device Neural Net Inference with Mobile GPUs
    • [cs.DC]The Information Processing Factory: Organization, Terminology, and Definitions
    • [cs.DS]Cache-Friendly Search Trees; or, In Which Everything Beats std::set
    • [cs.GL]Challenges in IT Operations Management at a German University Chair — Ten Years in Retrospect
    • [cs.IR]A Neural Attention Model for Adaptive Learning of Social Friends’ Preferences
    • [cs.IR]Adaptive Deep Learning of Cross-Domain Loss in Collaborative Filtering
    • [cs.IR]Bandit Learning for Diversified Interactive Recommendation
    • [cs.IR]Combating the Filter Bubble: Designing for Serendipity in a University Course Recommendation System
    • [cs.IR]Combining Q&A Pair Quality and Question Relevance Features on Community-based Question Retrieval
    • [cs.IR]Learning to Rank Broad and Narrow Queries in E-Commerce
    • [cs.IR]Pentagon at MEDIQA 2019: Multi-task Learning for Filtering and Re-ranking Answers using Language Inference and Question Entailment
    • [cs.IT]A Simple Evaluation for the Secrecy Outage Probability Over Generalized-K Fading Channels
    • [cs.IT]An Encoding-Decoding algorithm based on Padovan numbers
    • [cs.IT]Serial Quantization for Representing Sparse Signals
    • [cs.LG]A Bayesian Hierarchical Model for Criminal Investigations
    • [cs.LG]AMI-Net+: A Novel Multi-Instance Neural Network for Medical Diagnosis from Incomplete and Imbalanced Data
    • [cs.LG]Accelerating Deconvolution on Unmodified CNN Accelerators for Generative Adversarial Networks — A Software Approach
    • [cs.LG]Adjustment Criteria for Recovering Causal Effects from Missing Data
    • [cs.LG]An Enhanced Ad Event-Prediction Method Based on Feature Engineering
    • [cs.LG]An Experimental Evaluation of Large Scale GBDT Systems
    • [cs.LG]Benchmarking Model-Based Reinforcement Learning
    • [cs.LG]Canonical Correlation Analysis (CCA) Based Multi-View Learning: An Overview
    • [cs.LG]Circuit-Based Intrinsic Methods to Detect Overfitting
    • [cs.LG]Compositional Structure Learning for Sequential Video Data
    • [cs.LG]Don’t take it lightly: Phasing optical random projections with unknown operators
    • [cs.LG]Dynamics-Aware Unsupervised Discovery of Skills
    • [cs.LG]E-Sports Talent Scouting Based on Multimodal Twitch Stream Data
    • [cs.LG]Encoding high-cardinality string categorical variables
    • [cs.LG]FairNAS: Rethinking Evaluation Fairness of Weight Sharing Neural Architecture Search
    • [cs.LG]Generative Models for Automatic Chemical Design
    • [cs.LG]Graph Embeddings at Scale
    • [cs.LG]Graph Neural Network for Interpreting Task-fMRI Biomarkers
    • [cs.LG]HyperNOMAD: Hyperparameter optimization of deep neural networks using mesh adaptive direct search
    • [cs.LG]Learning graph-structured data using Poincaré embeddings and Riemannian K-means algorithms
    • [cs.LG]Learning with Known Operators reduces Maximum Training Error Bounds
    • [cs.LG]Machine Learning based Prediction of Hierarchical Classification of Transposable Elements
    • [cs.LG]MimosaNet: An Unrobust Neural Network Preventing Model Stealing
    • [cs.LG]Minimally distorted Adversarial Examples with a Fast Adaptive Boundary Attack
    • [cs.LG]Quickly Finding the Best Linear Model in High Dimensions
    • [cs.LG]Rethinking Continual Learning for Autonomous Agents and Robots
    • [cs.LG]Solving Partial Assignment Problems using Random Clique Complexes
    • [cs.LG]Spatially-Coupled Neural Network Architectures
    • [cs.LG]Supervised Uncertainty Quantification for Segmentation with Multiple Annotations
    • [cs.LG]The Receptive Field as a Regularizer in Deep Convolutional Neural Networks for Acoustic Scene Classification
    • [cs.LG]The Role of Memory in Stochastic Optimization
    • [cs.LG]Time Series Anomaly Detection with Variational Autoencoders
    • [cs.LG]Treant: Training Evasion-Aware Decision Trees
    • [cs.LG]libconform v0.1.0: a Python library for conformal prediction
    • [cs.MA]Are You Doing What I Think You Are Doing? Criticising Uncertain Agent Models
    • [cs.MA]Distributed Learning in Non-Convex Environments — Part II: Polynomial Escape from Saddle-Points
    • [cs.MM]Intrinsic Image Popularity Assessment
    • [cs.NE]A Note On The Popularity of Stochastic Optimization Algorithms in Different Fields: A Quantitative Analysis from 2007 to 2017
    • [cs.NE]A general representation of dynamical systems for reservoir computing
    • [cs.NE]Introducing Astrocytes on a Neuromorphic Processor: Synchronization, Local Plasticity and Edge of Chaos
    • [cs.NE]Neural Network Architecture Search with Differentiable Cartesian Genetic Programming for Regression
    • [cs.NE]Reasoning and Generalization in RL: A Tool Use Perspective
    • [cs.RO]Action Prediction in Humans and Robots
    • [cs.RO]An Approximation Algorithm for a Task Allocation, Sequencing and Scheduling Problem involving a Human-Robot Team
    • [cs.RO]Cooperative Schedule-Driven Intersection Control with Connected and Autonomous Vehicles
    • [cs.RO]End-to-end Decentralized Multi-robot Navigation in Unknown Complex Environments via Deep Reinforcement Learning
    • [cs.RO]Intrinsic Calibration of Depth Cameras for Mobile Robots using a Radial Laser Scanner
    • [cs.RO]Robust and Efficient Quadrotor Trajectory Generation for Fast Autonomous Flight
    • [cs.RO]SVM Enhanced Frenet Frame Planner For Safe Navigation Amidst Moving Agents
    • [cs.RO]Sensitivity of Legged Balance Control to Uncertainties and Sampling Period
    • [cs.RO]Statistical Characteristics of Driver Accelerating Behavior and Its Probability Model
    • [cs.SD]A Case Study of Deep-Learned Activations via Hand-Crafted Audio Features
    • [cs.SD]Cover Detection using Dominant Melody Embeddings
    • [cs.SD]Supervised Classifiers for Audio Impairments with Noisy Labels
    • [cs.SI]On Adaptivity Gaps of Influence Maximization under the Independent Cascade Model with Full Adoption Feedback
    • [cs.SI]On the Privacy of dK-Random Graphs
    • [eess.AS]Attention model for articulatory features detection
    • [eess.AS]End-to-End Speech Recognition with High-Frame-Rate Features Extraction
    • [eess.IV]3D Cardiac Shape Prediction with Deep Neural Networks: Simultaneous Use of Images and Patient Metadata
    • [eess.IV]Anatomically Consistent Segmentation of Organs at Risk in MRI with Convolutional Neural Networks
    • [eess.IV]Automated Detection and Type Classification of Central Venous Catheters in Chest X-Rays
    • [eess.IV]Calibration of fisheye camera using entrance pupil
    • [eess.IV]Cryo-EM reconstruction of continuous heterogeneity by Laplacian spectral volumes
    • [eess.IV]Deep Attentive Features for Prostate Segmentation in 3D Transrectal Ultrasound
    • [eess.IV]Deep Transfer Learning For Whole-Brain fMRI Analyses
    • [eess.IV]Dual Network Architecture for Few-view CT — Trained on ImageNet Data and Transferred for Medical Imaging
    • [eess.IV]Estimation of Absolute States of Human Skeletal Muscle via Standard B-Mode Ultrasound Imaging and Deep Convolutional Neural Networks
    • [eess.IV]Improving the generalizability of convolutional neural network-based segmentation on CMR images
    • [eess.IV]Region-Manipulated Fusion Networks for Pancreatitis Recognition
    • [eess.IV]Robust Cochlear Modiolar Axis Detection in CT
    • [eess.SP]Enumerative Sphere Shaping for Rate Adaptation and Reach Increase in WDM Transmission Systems
    • [eess.SP]Evaluation of Low Complexity Massive MIMO Techniques Under Realistic Channel Conditions
    • [math.FA]The multidimensional truncated Moment Problem: Shape and Gaussian Mixture Reconstruction from Derivatives of Moments
    • [math.HO]Male Under-performance in Undergraduate Engineering Mathematical Courses: Causes and Solution Strategy
    • [math.OC]Globally Convergent Newton Methods for Ill-conditioned Generalized Self-concordant Losses
    • [math.OC]Variance Reduction for Matrix Games
    • [math.PR]Bounding quantiles of Wasserstein distance between true and empirical measure
    • [math.PR]Deviation inequalities for separately Lipschitz functionals of composition of random functions
    • [math.PR]Weighted distances in scale-free preferential attachment models
    • [math.ST]Causal models on probability spaces
    • [math.ST]Estimating a probability of failure with the convex order in computer experiments
    • [math.ST]Large Deviations of the Estimated Cumulative Hazard Rate
    • [math.ST]Sparse High-Dimensional Isotonic Regression
    • [math.ST]Test for parameter change in the presence of outliers: the density power divergence based approach
    • [math.ST]Unbiased Estimation of the Reciprocal Mean for Non-negative Random Variables
    • [physics.chem-ph]Predicting Retrosynthetic Reaction using Self-Corrected Transformer Neural Networks
    • [q-bio.NC]Modeling Response Time Distributions with Generalized Beta Prime
    • [q-bio.QM]High-Throughput Machine Learning from Electronic Health Records
    • [stat.AP]An Intrinsic Geometrical Approach for Statistical Process Control of Surface and Manifold Data
    • [stat.AP]Data integration for high-resolution, continental-scale estimation of air pollution concentrations
    • [stat.AP]bayes4psy — an Open Source R Package for Bayesian Statistics in Psychology
    • [stat.CO]Model-based clustering and classification using mixtures of multivariate skewed power exponential distributions
    • [stat.ME]A Bayesian Semiparametric Gaussian Copula Approach to a Multivariate Normality Test
    • [stat.ME]Double Cross Validation for the Number of Factors in Approximate Factor Models
    • [stat.ME]Evaluating A Key Instrumental Variable Assumption Using Randomization Tests
    • [stat.ME]Learning Markov models via low-rank optimization
    • [stat.ME]Mid-quantile regression for discrete responses
    • [stat.ML]A flexible EM-like clustering algorithm for noisy data
    • [stat.ML]Forecasting high-dimensional dynamics exploiting suboptimal embeddings
    • [stat.ML]Implementation of batched Sinkhorn iterations for entropy-regularized Wasserstein loss
    • [stat.ML]Selecting the independent coordinates of manifolds with large aspect ratios
    • [stat.ML]Spectral Overlap and a Comparison of Parameter-Free, Dimensionality Reduction Quality Metrics
    • [stat.ML]Towards Interpretable Deep Extreme Multi-label Learning
    • [stat.ML]Unscented Gaussian Process Latent Variable Model: learning from uncertain inputs with intractable kernels

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

    • [astro-ph.EP]Bias and robustness of eccentricity estimates from radial velocity data
    N. C. Hara, G. Boué, J. Laskar, J. B. Delisle, N. Unger
    http://arxiv.org/abs/1907.02048v1

    • [cond-mat.stat-mech]Minimum Power to Maintain a Nonequilibrium Distribution of a Markov Chain
    Dmitri S. Pavlichin, Yihui Quek, Tsachy Weissman
    http://arxiv.org/abs/1907.01582v1

    • [cs.AI]Evolving the Hearthstone Meta
    Fernando de Mesentier Silva, Rodrigo Canaan, Scott Lee, Matthew C. Fontaine, Julian Togelius, Amy K. Hoover
    http://arxiv.org/abs/1907.01623v1

    • [cs.AI]On Conflicting and Conflicting Values
    Kinzang Chhogyal, Abhaya Nayak, Aditya Ghose, Mehmet Orgun, Hoa Dam
    http://arxiv.org/abs/1907.01682v1

    • [cs.AI]Perspective Taking in Deep Reinforcement Learning Agents
    Aqeel Labash, Jaan Aru, Tambet Matiisen, Ardi Tampuu, Raul Vicente
    http://arxiv.org/abs/1907.01851v1

    • [cs.AI]Recommendations on Designing Practical Interval Type-2 Fuzzy Systems
    Dongrui Wu, Jerry Mendel
    http://arxiv.org/abs/1907.01697v1

    • [cs.AI]Using Bi-Directional Information Exchange to Improve Decentralized Schedule-Driven Traffic Control
    Hsu-Chieh Hu, Stephen F. Smith
    http://arxiv.org/abs/1907.01978v1

    • [cs.CC]Efficient Circuit Simulation in MapReduce
    Fabian Frei, Koichi Wada
    http://arxiv.org/abs/1907.01624v1

    • [cs.CL]CS563-QA: A Collection for Evaluating Question Answering Systems
    Katerina Papantoniou, Yannis Tzitzikas
    http://arxiv.org/abs/1907.01611v1

    • [cs.CL]Constructing large scale biomedical knowledge bases from scratch with rapid annotation of interpretable patterns
    Julien Fauqueur, Ashok Thillaisundaram, Theodosia Togia
    http://arxiv.org/abs/1907.01417v2

    • [cs.CL]Data mining Mandarin tone contour shapes
    Shuo Zhang
    http://arxiv.org/abs/1907.01668v1

    • [cs.CL]Deep neural network-based classification model for Sentiment Analysis
    Donghang Pan, Jingling Yuan, Lin Li, Deming Sheng
    http://arxiv.org/abs/1907.02046v1

    • [cs.CL]Depth Growing for Neural Machine Translation
    Lijun Wu, Yiren Wang, Yingce Xia, Fei Tian, Fei Gao, Tao Qin, Jianhuang Lai, Tie-Yan Liu
    http://arxiv.org/abs/1907.01968v1

    • [cs.CL]Multi-Task Networks With Universe, Group, and Task Feature Learning
    Shiva Pentyala, Mengwen Liu, Markus Dreyer
    http://arxiv.org/abs/1907.01791v1

    • [cs.CL]MultiWOZ 2.1: Multi-Domain Dialogue State Corrections and State Tracking Baselines
    Mihail Eric, Rahul Goel, Shachi Paul, Abhishek Sethi, Sanchit Agarwal, Shuyag Gao, Dilek Hakkani-Tur
    http://arxiv.org/abs/1907.01669v1

    • [cs.CL]On the Weaknesses of Reinforcement Learning for Neural Machine Translation
    Leshem Choshen, Lior Fox, Zohar Aizenbud, Omri Abend
    http://arxiv.org/abs/1907.01752v1

    • [cs.CL]Polyphone Disambiguation for Mandarin Chinese Using Conditional Neural Network with Multi-level Embedding Features
    Zexin Cai, Yaogen Yang, Chuxiong Zhang, Xiaoyi Qin, Ming Li
    http://arxiv.org/abs/1907.01749v1

    • [cs.CL]Real-time Claim Detection from News Articles and Retrieval of Semantically-Similar Factchecks
    Ben Adler, Giacomo Boscaini-Gilroy
    http://arxiv.org/abs/1907.02030v1

    • [cs.CL]Scalable Multi Corpora Neural Language Models for ASR
    Anirudh Raju, Denis Filimonov, Gautam Tiwari, Guitang Lan, Ariya Rastrow
    http://arxiv.org/abs/1907.01677v1

    • [cs.CR]Gathering Cyber Threat Intelligence from Twitter Using Novelty Classification
    Ba Dung Le, Guanhua Wang, Mehwish Nasim, Ali Babar
    http://arxiv.org/abs/1907.01755v1

    • [cs.CV]A Deep Image Compression Framework for Face Recognition
    Nai Bian, Feng Liang, Haisheng Fu, Bo Lei
    http://arxiv.org/abs/1907.01714v1

    • [cs.CV]A Semi-Supervised Framework for Automatic Pixel-Wise Breast Cancer Grading of Histological Images
    Yanyuet Man, Xiangyun Ding, Xingcheng Yao, Han Bao
    http://arxiv.org/abs/1907.01696v1

    • [cs.CV]Attention routing between capsules
    Jaewoong Choi, Hyun Seo, Suee Im, Myungju Kang
    http://arxiv.org/abs/1907.01750v1

    • [cs.CV]Cascade Attention Guided Residue Learning GAN for Cross-Modal Translation
    Bin Duan, Wei Wang, Hao Tang, Hugo Latapie, Yan Yan
    http://arxiv.org/abs/1907.01826v1

    • [cs.CV]Chasing Ghosts: Instruction Following as Bayesian State Tracking
    Peter Anderson, Ayush Shrivastava, Devi Parikh, Dhruv Batra, Stefan Lee
    http://arxiv.org/abs/1907.02022v1

    • [cs.CV]Deformable Tube Network for Action Detection in Videos
    Wei Li, Zehuan Yuan, Dashan Guo, Lei Huang, Xiangzhong Fang, Changhu Wang
    http://arxiv.org/abs/1907.01847v1

    • [cs.CV]Hyper-Molecules: on the Representation and Recovery of Dynamical Structures, with Application to Flexible Macro-Molecular Structures in Cryo-EM
    Roy R. Lederman, Joakim Andén, Amit Singer
    http://arxiv.org/abs/1907.01589v1

    • [cs.CV]Learning Landmarks from Unaligned Data using Image Translation
    Tomas Jakab, Ankush Gupta, Hakan Bilen, Andrea Vedaldi
    http://arxiv.org/abs/1907.02055v1

    • [cs.CV]Learning to Predict Robot Keypoints Using Artificially Generated Images
    Christoph Heindl, Sebastian Zambal, Josef Scharinger
    http://arxiv.org/abs/1907.01879v1

    • [cs.CV]Learning to aggregate feature representations
    Guy Gaziv
    http://arxiv.org/abs/1907.01034v2

    • [cs.CV]Mask Embedding in conditional GAN for Guided Synthesis of High Resolution Images
    Yinhao Ren, Zhe Zhu, Yingzhou Li, Joseph Lo
    http://arxiv.org/abs/1907.01710v1

    • [cs.CV]Novel evaluation of surgical activity recognition models using task-based efficiency metrics
    Aneeq Zia, Liheng Guo, Linlin Zhou, Irfan Essa, Anthony Jarc
    http://arxiv.org/abs/1907.02060v1

    • [cs.CV]Probabilistic Multilayer Regularization Network for Unsupervised 3D Brain Image Registration
    Lihao Liu, Xiaowei Hu, Lei Zhu, Pheng-Ann Heng
    http://arxiv.org/abs/1907.01922v1

    • [cs.CV]Robust Synthesis of Adversarial Visual Examples Using a Deep Image Prior
    Thomas Gittings, Steve Schneider, John Collomosse
    http://arxiv.org/abs/1907.01996v1

    • [cs.CV]Semi-supervised Image Attribute Editing using Generative Adversarial Networks
    Yahya Dogan, Hacer Yalim Keles
    http://arxiv.org/abs/1907.01841v1

    • [cs.CV]Simple vs complex temporal recurrences for video saliency prediction
    Panagiotis Linardos, Eva Mohedano, Juan Jose Nieto, Noel E. O’Connor, Xavier Giro-i-Nieto, Kevin McGuinness
    http://arxiv.org/abs/1907.01869v1

    • [cs.CV]SkeletonNet: Shape Pixel to Skeleton Pixel
    Sabari Nathan, Priya Kansal
    http://arxiv.org/abs/1907.01683v1

    • [cs.CV]Super-Resolution of PROBA-V Images Using Convolutional Neural Networks
    Marcus Märtens, Dario Izzo, Andrej Krzic, Daniël Cox
    http://arxiv.org/abs/1907.01821v1

    • [cs.CV]Tracking system of Mine Patrol Robot for Low Illumination Environment
    Shaoze You, Hua Zhu, Menggang Li, Lei Wang, Chaoquan Tang
    http://arxiv.org/abs/1907.01806v1

    • [cs.CV]Unsupervised Anomalous Trajectory Detection for Crowded Scenes
    Deepan Das, Deepak Mishra
    http://arxiv.org/abs/1907.01717v1

    • [cs.CV]Using Deep Learning to Count Albatrosses from Space
    Ellen Bowler, Peter T. Fretwell, Geoffrey French, Michal Mackiewicz
    http://arxiv.org/abs/1907.02040v1

    • [cs.CY]Beyond content analysis: Detecting targeted ads via distributed counting
    Costas Iordanou, Nicolas Kourtellis, Juan Miguel Carrascosa, Claudio Soriente, Ruben Cuevas, Nikolaos Laoutaris
    http://arxiv.org/abs/1907.01862v1

    • [cs.CY]Quantifying Algorithmic Biases over Time
    Vivek K. Singh, Ishaan Singh
    http://arxiv.org/abs/1907.01671v1

    • [cs.DB]Rule Applicability on RDF Triplestore Schemas
    Paolo Pareti, George Konstantinidis, Timothy J. Norman, Murat Şensoy
    http://arxiv.org/abs/1907.01627v1

    • [cs.DC]Koalja: from Data Plumbing to Smart Workspaces in the Extended Cloud
    Mark Burgess, Ewout Prangsma
    http://arxiv.org/abs/1907.01796v1

    • [cs.DC]On-Device Neural Net Inference with Mobile GPUs
    Juhyun Lee, Nikolay Chirkov, Ekaterina Ignasheva, Yury Pisarchyk, Mogan Shieh, Fabio Riccardi, Raman Sarokin, Andrei Kulik, Matthias Grundmann
    http://arxiv.org/abs/1907.01989v1

    • [cs.DC]The Information Processing Factory: Organization, Terminology, and Definitions
    Eberle A. Rambo, Bryan Donyanavard, Minjun Seo, Florian Maurer, Thawra Kadeed, Caio B. de Melo, Biswadip Maity, Anmol Surhonne, Andreas Herkersdorf, Fadi Kurdahi, Nikil Dutt, Rolf Ernst
    http://arxiv.org/abs/1907.01578v1

    • [cs.DS]Cache-Friendly Search Trees; or, In Which Everything Beats std::set
    Jeffrey Barratt, Brian Zhang
    http://arxiv.org/abs/1907.01631v1

    • [cs.GL]Challenges in IT Operations Management at a German University Chair — Ten Years in Retrospect
    Martin Geier, Samarjit Chakraborty
    http://arxiv.org/abs/1907.01874v1

    • [cs.IR]A Neural Attention Model for Adaptive Learning of Social Friends’ Preferences
    Dimitrios Rafailidis, Gerhard Weiss
    http://arxiv.org/abs/1907.01644v1

    • [cs.IR]Adaptive Deep Learning of Cross-Domain Loss in Collaborative Filtering
    Dimitrios Rafailidis, Gerhard Weiss
    http://arxiv.org/abs/1907.01645v1

    • [cs.IR]Bandit Learning for Diversified Interactive Recommendation
    Yong Liu, Yingtai Xiao, Qiong Wu, Chunyan Miao, Juyong Zhang
    http://arxiv.org/abs/1907.01647v1

    • [cs.IR]Combating the Filter Bubble: Designing for Serendipity in a University Course Recommendation System
    Zachary A. Pardos, Weijie Jiang
    http://arxiv.org/abs/1907.01591v1

    • [cs.IR]Combining Q&A Pair Quality and Question Relevance Features on Community-based Question Retrieval
    Dong Li, Lin Li
    http://arxiv.org/abs/1907.02031v1

    • [cs.IR]Learning to Rank Broad and Narrow Queries in E-Commerce
    Siddhartha Devapujula, Sagar Arora, Sumit Borar
    http://arxiv.org/abs/1907.01549v1

    • [cs.IR]Pentagon at MEDIQA 2019: Multi-task Learning for Filtering and Re-ranking Answers using Language Inference and Question Entailment
    Hemant Pugaliya, Karan Saxena, Shefali Garg, Sheetal Shalini, Prashant Gupta, Eric Nyberg, Teruko Mitamura
    http://arxiv.org/abs/1907.01643v1

    • [cs.IT]A Simple Evaluation for the Secrecy Outage Probability Over Generalized-K Fading Channels
    Hui Zhao, Yuanwei Liu, Ahemd Sultan-Salem, Mohamed-Slim Alouini
    http://arxiv.org/abs/1907.01818v1

    • [cs.IT]An Encoding-Decoding algorithm based on Padovan numbers
    Jenan Shtayat, Alaa Al-Kateeb
    http://arxiv.org/abs/1907.02007v1

    • [cs.IT]Serial Quantization for Representing Sparse Signals
    Alejandro Cohen, Nir Shlezinger, Yonina C. Eldar, Muriel Médard
    http://arxiv.org/abs/1907.01691v1

    • [cs.LG]A Bayesian Hierarchical Model for Criminal Investigations
    F. O. Bunnin, J. Q. Smith
    http://arxiv.org/abs/1907.01894v1

    • [cs.LG]AMI-Net+: A Novel Multi-Instance Neural Network for Medical Diagnosis from Incomplete and Imbalanced Data
    Zeyuan Wang, Josiah Poon, Simon Poon
    http://arxiv.org/abs/1907.01734v1

    • [cs.LG]Accelerating Deconvolution on Unmodified CNN Accelerators for Generative Adversarial Networks — A Software Approach
    Kaijie Tu
    http://arxiv.org/abs/1907.01773v1

    • [cs.LG]Adjustment Criteria for Recovering Causal Effects from Missing Data
    Mojdeh Saadati, Jin Tian
    http://arxiv.org/abs/1907.01654v1

    • [cs.LG]An Enhanced Ad Event-Prediction Method Based on Feature Engineering
    Saeid Soheily Khah, Yiming Wu
    http://arxiv.org/abs/1907.01959v1

    • [cs.LG]An Experimental Evaluation of Large Scale GBDT Systems
    Fangcheng Fu, Jiawei Jiang, Shaoxia Ying, Bin Cui
    http://arxiv.org/abs/1907.01882v1

    • [cs.LG]Benchmarking Model-Based Reinforcement Learning
    Tingwu Wang, Xuchan Bao, Ignasi Clavera, Jerrick Hoang, Yeming Wen, Eric Langlois, Shunshi Zhang, Guodong Zhang, Pieter Abbeel, Jimmy Ba
    http://arxiv.org/abs/1907.02057v1

    • [cs.LG]Canonical Correlation Analysis (CCA) Based Multi-View Learning: An Overview
    Chenfeng Guo, Dongrui Wu
    http://arxiv.org/abs/1907.01693v1

    • [cs.LG]Circuit-Based Intrinsic Methods to Detect Overfitting
    Sat Chatterjee, Alan Mishchenko
    http://arxiv.org/abs/1907.01991v1

    • [cs.LG]Compositional Structure Learning for Sequential Video Data
    Kyoung-Woon On, Eun-Sol Kim, Yu-Jung Heo, Byoung-Tak Zhang
    http://arxiv.org/abs/1907.01709v1

    • [cs.LG]Don’t take it lightly: Phasing optical random projections with unknown operators
    Sidharth Gupta, Rémi Gribonval, Laurent Daudet, Ivan Dokmanić
    http://arxiv.org/abs/1907.01703v1

    • [cs.LG]Dynamics-Aware Unsupervised Discovery of Skills
    Archit Sharma, Shixiang Gu, Sergey Levine, Vikash Kumar, Karol Hausman
    http://arxiv.org/abs/1907.01657v1

    • [cs.LG]E-Sports Talent Scouting Based on Multimodal Twitch Stream Data
    Anna Belova, Wen He, Ziyi Zhong
    http://arxiv.org/abs/1907.01615v1

    • [cs.LG]Encoding high-cardinality string categorical variables
    Patricio Cerda, Gaël Varoquaux
    http://arxiv.org/abs/1907.01860v1

    • [cs.LG]FairNAS: Rethinking Evaluation Fairness of Weight Sharing Neural Architecture Search
    Xiangxiang Chu, Bo Zhang, Ruijun Xu, Jixiang Li
    http://arxiv.org/abs/1907.01845v1

    • [cs.LG]Generative Models for Automatic Chemical Design
    Daniel Schwalbe-Koda, Rafael Gómez-Bombarelli
    http://arxiv.org/abs/1907.01632v1

    • [cs.LG]Graph Embeddings at Scale
    C. Bayan Bruss, Anish Khazane, Jonathan Rider, Richard Serpe, Saurabh Nagrecha, Keegan E. Hines
    http://arxiv.org/abs/1907.01705v1

    • [cs.LG]Graph Neural Network for Interpreting Task-fMRI Biomarkers
    Xiaoxiao Li, Nicha C. Dvornek, Yuan Zhou, Juntang Zhuang, Pamela Ventola, James S. Duncan
    http://arxiv.org/abs/1907.01661v1

    • [cs.LG]HyperNOMAD: Hyperparameter optimization of deep neural networks using mesh adaptive direct search
    Dounia Lakhmiri, Sébastien Le Digabel, Christophe Tribes
    http://arxiv.org/abs/1907.01698v1

    • [cs.LG]Learning graph-structured data using Poincaré embeddings and Riemannian K-means algorithms
    Hatem Hajri, Hadi Zaatiti, Georges Hebrail
    http://arxiv.org/abs/1907.01662v1

    • [cs.LG]Learning with Known Operators reduces Maximum Training Error Bounds
    Andreas K. Maier, Christopher Syben, Bernhard Stimpel, Tobias Würfl, Mathis Hoffmann, Frank Schebesch, Weilin Fu, Leonid Mill, Lasse Kling, Silke Christiansen
    http://arxiv.org/abs/1907.01992v1

    • [cs.LG]Machine Learning based Prediction of Hierarchical Classification of Transposable Elements
    Manisha Panta, Avdesh Mishra, Md Tamjidul Hoque, Joel Atallah
    http://arxiv.org/abs/1907.01674v1

    • [cs.LG]MimosaNet: An Unrobust Neural Network Preventing Model Stealing
    Kálmán Szentannai, Jalal Al-Afandi, András Horváth
    http://arxiv.org/abs/1907.01650v1

    • [cs.LG]Minimally distorted Adversarial Examples with a Fast Adaptive Boundary Attack
    Francesco Croce, Matthias Hein
    http://arxiv.org/abs/1907.02044v1

    • [cs.LG]Quickly Finding the Best Linear Model in High Dimensions
    Yahya Sattar, Samet Oymak
    http://arxiv.org/abs/1907.01728v1

    • [cs.LG]Rethinking Continual Learning for Autonomous Agents and Robots
    German I. Parisi, Christopher Kanan
    http://arxiv.org/abs/1907.01929v1

    • [cs.LG]Solving Partial Assignment Problems using Random Clique Complexes
    Charu Sharma, Deepak Nathani, Manohar Kaul
    http://arxiv.org/abs/1907.01739v1

    • [cs.LG]Spatially-Coupled Neural Network Architectures
    Arman Hasanzadeh, Nagaraj T. Janakiraman, Vamsi K. Amalladinne, Krishna R. Narayanan
    http://arxiv.org/abs/1907.02051v1

    • [cs.LG]Supervised Uncertainty Quantification for Segmentation with Multiple Annotations
    Shi Hu, Daniel Worrall, Stefan Knegt, Bas Veeling, Henkjan Huisman, Max Welling
    http://arxiv.org/abs/1907.01949v1

    • [cs.LG]The Receptive Field as a Regularizer in Deep Convolutional Neural Networks for Acoustic Scene Classification
    Khaled Koutini, Hamid Eghbal-zadeh, Matthias Dorfer, Gerhard Widmer
    http://arxiv.org/abs/1907.01803v1

    • [cs.LG]The Role of Memory in Stochastic Optimization
    Antonio Orvieto, Jonas Kohler, Aurelien Lucchi
    http://arxiv.org/abs/1907.01678v1

    • [cs.LG]Time Series Anomaly Detection with Variational Autoencoders
    Chunkai Zhang, Yingyang Chen
    http://arxiv.org/abs/1907.01702v1

    • [cs.LG]Treant: Training Evasion-Aware Decision Trees
    Stefano Calzavara, Claudio Lucchese, Gabriele Tolomei, Seyum Assefa Abebe, Salvatore Orlando
    http://arxiv.org/abs/1907.01197v2

    • [cs.LG]libconform v0.1.0: a Python library for conformal prediction
    Jonas Fassbender
    http://arxiv.org/abs/1907.02015v1

    • [cs.MA]Are You Doing What I Think You Are Doing? Criticising Uncertain Agent Models
    Stefano V. Albrecht, S. Ramamoorthy
    http://arxiv.org/abs/1907.01912v1

    • [cs.MA]Distributed Learning in Non-Convex Environments — Part II: Polynomial Escape from Saddle-Points
    Stefan Vlaski, Ali H. Sayed
    http://arxiv.org/abs/1907.01849v1

    • [cs.MM]Intrinsic Image Popularity Assessment
    Keyan Ding, Kede Ma, Shiqi Wang
    http://arxiv.org/abs/1907.01985v1

    • [cs.NE]A Note On The Popularity of Stochastic Optimization Algorithms in Different Fields: A Quantitative Analysis from 2007 to 2017
    Son Duy Dao
    http://arxiv.org/abs/1907.01453v2

    • [cs.NE]A general representation of dynamical systems for reservoir computing
    Sidney Pontes-Filho, Anis Yazidi, Jianhua Zhang, Hugo Hammer, Gustavo B. M. Mello, Ioanna Sandvig, Gunnar Tufte, Stefano Nichele
    http://arxiv.org/abs/1907.01856v1

    • [cs.NE]Introducing Astrocytes on a Neuromorphic Processor: Synchronization, Local Plasticity and Edge of Chaos
    Guangzhi Tang, Ioannis E. Polykretis, Vladimir A. Ivanov, Arpit Shah, Konstantinos P. Michmizos
    http://arxiv.org/abs/1907.01620v1

    • [cs.NE]Neural Network Architecture Search with Differentiable Cartesian Genetic Programming for Regression
    Marcus Märtens, Dario Izzo
    http://arxiv.org/abs/1907.01939v1

    • [cs.NE]Reasoning and Generalization in RL: A Tool Use Perspective
    Sam Wenke, Dan Saunders, Mike Qiu, Jim Fleming
    http://arxiv.org/abs/1907.02050v1

    • [cs.RO]Action Prediction in Humans and Robots
    Florentin Wörgötter, Fatemeh Ziaeetabar, Stefan Pfeiffer, Osman Kaya, Tomas Kulvicius, Minija Tamosiunaite
    http://arxiv.org/abs/1907.01932v1

    • [cs.RO]An Approximation Algorithm for a Task Allocation, Sequencing and Scheduling Problem involving a Human-Robot Team
    Sai Krishna Hari, Abhishek Nayak, Sivakumar Rathinam
    http://arxiv.org/abs/1907.01692v1

    • [cs.RO]Cooperative Schedule-Driven Intersection Control with Connected and Autonomous Vehicles
    Hsu-Chieh Hu, Stephen F. Smith, Rick Goldstein
    http://arxiv.org/abs/1907.01984v1

    • [cs.RO]End-to-end Decentralized Multi-robot Navigation in Unknown Complex Environments via Deep Reinforcement Learning
    Juntong Lin, Xuyun Yang, Peiwei Zheng, Hui Cheng
    http://arxiv.org/abs/1907.01713v1

    • [cs.RO]Intrinsic Calibration of Depth Cameras for Mobile Robots using a Radial Laser Scanner
    David Zuñiga-Noël, Jose-Raul Ruiz-Sarmiento, Javier Gonzalez-Jimenez
    http://arxiv.org/abs/1907.01839v1

    • [cs.RO]Robust and Efficient Quadrotor Trajectory Generation for Fast Autonomous Flight
    Boyu Zhou, Fei Gao, Luqi Wang, Chuhao Liu, Shaojie Shen
    http://arxiv.org/abs/1907.01531v2

    • [cs.RO]SVM Enhanced Frenet Frame Planner For Safe Navigation Amidst Moving Agents
    Unni Krishnan R Nair, Nivedita Rufus, Vashist Madiraju, K Madhava Krishna
    http://arxiv.org/abs/1907.01577v1

    • [cs.RO]Sensitivity of Legged Balance Control to Uncertainties and Sampling Period
    Nahuel Villa, Johannes Englsberger, Pierre-Brice Wieber
    http://arxiv.org/abs/1907.01805v1

    • [cs.RO]Statistical Characteristics of Driver Accelerating Behavior and Its Probability Model
    Rui Liu, Xichan Zhu
    http://arxiv.org/abs/1907.01747v1

    • [cs.SD]A Case Study of Deep-Learned Activations via Hand-Crafted Audio Features
    Olga Slizovskaia, Emilia Gómez, Gloria Haro
    http://arxiv.org/abs/1907.01813v1

    • [cs.SD]Cover Detection using Dominant Melody Embeddings
    Guillaume Doras, Geoffroy Peeters
    http://arxiv.org/abs/1907.01824v1

    • [cs.SD]Supervised Classifiers for Audio Impairments with Noisy Labels
    Chandan K A Reddy, Ross Cutler, Johannes Gehrke
    http://arxiv.org/abs/1907.01742v1

    • [cs.SI]On Adaptivity Gaps of Influence Maximization under the Independent Cascade Model with Full Adoption Feedback
    Wei Chen, Binghui Peng
    http://arxiv.org/abs/1907.01707v1

    • [cs.SI]On the Privacy of dK-Random Graphs
    Sameera Horawalavithana, Adriana Iamnitchi
    http://arxiv.org/abs/1907.01695v1

    • [eess.AS]Attention model for articulatory features detection
    Ievgen Karaulov, Dmytro Tkanov
    http://arxiv.org/abs/1907.01914v1

    • [eess.AS]End-to-End Speech Recognition with High-Frame-Rate Features Extraction
    Cong-Thanh Do
    http://arxiv.org/abs/1907.01957v1

    • [eess.IV]3D Cardiac Shape Prediction with Deep Neural Networks: Simultaneous Use of Images and Patient Metadata
    Rahman Attar, Marco Pereanez, Christopher Bowles, Stefan K. Piechnik, Stefan Neubauer, Steffen E. Petersen, Alejandro F. Frangi
    http://arxiv.org/abs/1907.01913v1

    • [eess.IV]Anatomically Consistent Segmentation of Organs at Risk in MRI with Convolutional Neural Networks
    Pawel Mlynarski, Hervé Delingette, Hamza Alghamdi, Pierre-Yves Bondiau, Nicholas Ayache
    http://arxiv.org/abs/1907.02003v1

    • [eess.IV]Automated Detection and Type Classification of Central Venous Catheters in Chest X-Rays
    Vaishnavi Subramanian, Hongzhi Wang, Joy T. Wu, Ken C. L. Wong, Arjun Sharma, Tanveer Syeda-Mahmood
    http://arxiv.org/abs/1907.01656v1

    • [eess.IV]Calibration of fisheye camera using entrance pupil
    Peter Fasogbon, Emre Aksu
    http://arxiv.org/abs/1907.01759v1

    • [eess.IV]Cryo-EM reconstruction of continuous heterogeneity by Laplacian spectral volumes
    Amit Moscovich, Amit Halevi, Joakim Andén, Amit Singer
    http://arxiv.org/abs/1907.01898v1

    • [eess.IV]Deep Attentive Features for Prostate Segmentation in 3D Transrectal Ultrasound
    Yi Wang, Haoran Dou, Xiaowei Hu, Lei Zhu, Xin Yang, Ming Xu, Jing Qin, Pheng-Ann Heng, Tianfu Wang, Dong Ni
    http://arxiv.org/abs/1907.01743v1

    • [eess.IV]Deep Transfer Learning For Whole-Brain fMRI Analyses
    Armin W. Thomas, Klaus-Robert Müller, Wojciech Samek
    http://arxiv.org/abs/1907.01953v1

    • [eess.IV]Dual Network Architecture for Few-view CT — Trained on ImageNet Data and Transferred for Medical Imaging
    Huidong Xie, Hongming Shan, Wenxiang Cong, Xiaohua Zhang, Shaohua Liu, Ruola Ning, Ge Wang
    http://arxiv.org/abs/1907.01262v2

    • [eess.IV]Estimation of Absolute States of Human Skeletal Muscle via Standard B-Mode Ultrasound Imaging and Deep Convolutional Neural Networks
    Ryan J. Cunningham, Ian D. Loram
    http://arxiv.org/abs/1907.01649v1

    • [eess.IV]Improving the generalizability of convolutional neural network-based segmentation on CMR images
    Chen Chen, Wenjia Bai, Rhodri H. Davies, Anish N. Bhuva, Charlotte Manisty, James C. Moon, Nay Aung, Aaron M. Lee, Mihir M. Sanghvi, Kenneth Fung, Jose Miguel Paiva, Steffen E. Petersen, Elena Lukaschuk, Stefan K. Piechnik, Stefan Neubauer, Daniel Rueckert
    http://arxiv.org/abs/1907.01268v2

    • [eess.IV]Region-Manipulated Fusion Networks for Pancreatitis Recognition
    Jian Wang, Xiaoyao Li, Xiangbo Shu, Weiqin Li
    http://arxiv.org/abs/1907.01744v1

    • [eess.IV]Robust Cochlear Modiolar Axis Detection in CT
    Wilhelm Wimmer, Clair Vandersteen, Nicolas Guevara, Marco Caversaccio, Hervé Delingette
    http://arxiv.org/abs/1907.01870v1

    • [eess.SP]Enumerative Sphere Shaping for Rate Adaptation and Reach Increase in WDM Transmission Systems
    Abdelkerim Amari, Sebastiaan Goossens, Yunus Can Gultekin, Olga Vassilieva, Inwoong Kim, Tadashi Ikeuchi, Chigo Okonkwo, Frans M. J. Willems, Alex Alvarado
    http://arxiv.org/abs/1907.01881v1

    • [eess.SP]Evaluation of Low Complexity Massive MIMO Techniques Under Realistic Channel Conditions
    Manijeh Bashar, Alister G. Burr, Katsuyuki Haneda, Kanapathippillai Cumanan, Mehdi M. Molu, Mohsen Khalily, Pei Xiao
    http://arxiv.org/abs/1907.01865v1

    • [math.FA]The multidimensional truncated Moment Problem: Shape and Gaussian Mixture Reconstruction from Derivatives of Moments
    Philipp J. di Dio
    http://arxiv.org/abs/1907.00790v1

    • [math.HO]Male Under-performance in Undergraduate Engineering Mathematical Courses: Causes and Solution Strategy
    Luai Al Labadi, Hishyar Khalil, Nida Siddiqui
    http://arxiv.org/abs/1907.00552v2

    • [math.OC]Globally Convergent Newton Methods for Ill-conditioned Generalized Self-concordant Losses
    Ulysse Marteau-Ferey, Francis Bach, Alessandro Rudi
    http://arxiv.org/abs/1907.01771v1

    • [math.OC]Variance Reduction for Matrix Games
    Yair Carmon, Yujia Jin, Aaron Sidford, Kevin Tian
    http://arxiv.org/abs/1907.02056v1

    • [math.PR]Bounding quantiles of Wasserstein distance between true and empirical measure
    Samuel N. Cohen, Martin N. A. Tegnér, Johannes Wiesel
    http://arxiv.org/abs/1907.02006v1

    • [math.PR]Deviation inequalities for separately Lipschitz functionals of composition of random functions
    Jérôme Dedecker, Paul Doukhan, Xiequan Fan
    http://arxiv.org/abs/1907.01758v1

    • [math.PR]Weighted distances in scale-free preferential attachment models
    Joost Jorritsma, Júlia Komjáthy
    http://arxiv.org/abs/1907.01907v1

    • [math.ST]Causal models on probability spaces
    Irineo Cabreros, John D. Storey
    http://arxiv.org/abs/1907.01672v1

    • [math.ST]Estimating a probability of failure with the convex order in computer experiments
    Lucie Bernard, Philippe Leduc
    http://arxiv.org/abs/1907.01781v1

    • [math.ST]Large Deviations of the Estimated Cumulative Hazard Rate
    Niklas Hohmann
    http://arxiv.org/abs/1907.02033v1

    • [math.ST]Sparse High-Dimensional Isotonic Regression
    David Gamarnik, Julia Gaudio
    http://arxiv.org/abs/1907.01715v1

    • [math.ST]Test for parameter change in the presence of outliers: the density power divergence based approach
    Junmo Song, Jiwon Kang
    http://arxiv.org/abs/1907.00004v1

    • [math.ST]Unbiased Estimation of the Reciprocal Mean for Non-negative Random Variables
    Sarat Moka, Dirk P. Kroese, Sandeep Juneja
    http://arxiv.org/abs/1907.01843v1

    • [physics.chem-ph]Predicting Retrosynthetic Reaction using Self-Corrected Transformer Neural Networks
    Shuangjia Zheng, Jiahua Rao, Zhongyue Zhang, Jun Xu, Yuedong Yang
    http://arxiv.org/abs/1907.01356v2

    • [q-bio.NC]Modeling Response Time Distributions with Generalized Beta Prime
    M. Dashti Moghaddam, Jiong Liu, John G. Holden, R. A. Serota
    http://arxiv.org/abs/1907.00070v1

    • [q-bio.QM]High-Throughput Machine Learning from Electronic Health Records
    Ross S. Kleiman, Paul S. Bennett, Peggy L. Peissig, Richard L. Berg, Zhaobin Kuang, Scott J. Hebbring, Michael D. Caldwell, David Page
    http://arxiv.org/abs/1907.01901v1

    • [stat.AP]An Intrinsic Geometrical Approach for Statistical Process Control of Surface and Manifold Data
    Xueqi Zhao, Enrique del Castillo
    http://arxiv.org/abs/1907.00111v2

    • [stat.AP]Data integration for high-resolution, continental-scale estimation of air pollution concentrations
    Matthew L. Thomas, Gavin Shaddick, Daniel Simpson, Kees de Hoogh, James V. Zidek
    http://arxiv.org/abs/1907.00093v1

    • [stat.AP]bayes4psy — an Open Source R Package for Bayesian Statistics in Psychology
    Jure Demšar, Grega Repovš, Erik Štrumbelj
    http://arxiv.org/abs/1907.01952v1

    • [stat.CO]Model-based clustering and classification using mixtures of multivariate skewed power exponential distributions
    Utkarsh J. Dang, Michael P. B. Gallaugher, Ryan P. Browne, Paul D. McNicholas
    http://arxiv.org/abs/1907.01938v1

    • [stat.ME]A Bayesian Semiparametric Gaussian Copula Approach to a Multivariate Normality Test
    Luai Al-Labadi, Forough Fazeli Asl, Zahra Saberi
    http://arxiv.org/abs/1907.01736v1

    • [stat.ME]Double Cross Validation for the Number of Factors in Approximate Factor Models
    Xianli Zeng, Yingcun Xia, Linjun Zhang
    http://arxiv.org/abs/1907.01670v1

    • [stat.ME]Evaluating A Key Instrumental Variable Assumption Using Randomization Tests
    Zach Branson, Luke Keele
    http://arxiv.org/abs/1907.01943v1

    • [stat.ME]Learning Markov models via low-rank optimization
    Ziwei Zhu, Xudong Li, Mengdi Wang, Anru Zhang
    http://arxiv.org/abs/1907.00113v1

    • [stat.ME]Mid-quantile regression for discrete responses
    Marco Geraci, Alessio Farcomeni
    http://arxiv.org/abs/1907.01945v1

    • [stat.ML]A flexible EM-like clustering algorithm for noisy data
    Violeta Roizman, Matthieu Jonckheere, Frédéric Pascal
    http://arxiv.org/abs/1907.01660v1

    • [stat.ML]Forecasting high-dimensional dynamics exploiting suboptimal embeddings
    Shunya Okuno, Kazuyuki Aihara, Yoshito Hirata
    http://arxiv.org/abs/1907.01552v1

    • [stat.ML]Implementation of batched Sinkhorn iterations for entropy-regularized Wasserstein loss
    Thomas Viehmann
    http://arxiv.org/abs/1907.01729v1

    • [stat.ML]Selecting the independent coordinates of manifolds with large aspect ratios
    Yu-Chia Chen, Marina Meilă
    http://arxiv.org/abs/1907.01651v1

    • [stat.ML]Spectral Overlap and a Comparison of Parameter-Free, Dimensionality Reduction Quality Metrics
    Jonathan Johannemann, Robert Tibshirani
    http://arxiv.org/abs/1907.01974v1

    • [stat.ML]Towards Interpretable Deep Extreme Multi-label Learning
    Yihuang Kang, I-Ling Cheng, Wenjui Mao, Bowen Kuo, Pei-Ju Lee
    http://arxiv.org/abs/1907.01723v1

    • [stat.ML]Unscented Gaussian Process Latent Variable Model: learning from uncertain inputs with intractable kernels
    Daniel Augusto R. M. A. de Souza, César Lincoln C. Mattos, João Paulo P. Gomes
    http://arxiv.org/abs/1907.01867v1