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