astro-ph.IM - 仪器仪表和天体物理学方法

    cs.AI - 人工智能 cs.CL - 计算与语言 cs.CR - 加密与安全 cs.CV - 机器视觉与模式识别 cs.CY - 计算与社会 cs.DB - 数据库 cs.DC - 分布式、并行与集群计算 cs.DM - 离散数学 cs.HC - 人机接口 cs.IR - 信息检索 cs.IT - 信息论 cs.LG - 自动学习 cs.MA - 多代理系统 cs.NE - 神经与进化计算 cs.RO - 机器人学 cs.SD - 声音处理 cs.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 eess.SY - 系统和控制 math.ST - 统计理论 q-bio.QM - 定量方法 quant-ph - 量子物理 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.IM]Astroalign: A Python module for astronomical image registration
    • [cs.AI]A Comparative Study of Some Central Notions of ASPIC+ and DeLP
    • [cs.AI]From Few to More: Large-scale Dynamic Multiagent Curriculum Learning
    • [cs.AI]One Explanation Does Not Fit All: A Toolkit and Taxonomy of AI Explainability Techniques
    • [cs.AI]Structured Query Construction via Knowledge Graph Embedding
    • [cs.CL]#MeTooMaastricht: Building a chatbot to assist survivors of sexual harassment
    • [cs.CL]A systematic comparison of methods for low-resource dependency parsing on genuinely low-resource languages
    • [cs.CL]Adversarial Examples with Difficult Common Words for Paraphrase Identification
    • [cs.CL]Annotating Student Talk in Text-based Classroom Discussions
    • [cs.CL]Argument Component Classification for Classroom Discussions
    • [cs.CL]Broad-Coverage Semantic Parsing as Transduction
    • [cs.CL]Deep learning with sentence embeddings pre-trained on biomedical corpora improves the performance of finding similar sentences in electronic medical records
    • [cs.CL]Don’t Forget the Long Tail! A Comprehensive Analysis of Morphological Generalization in Bilingual Lexicon Induction
    • [cs.CL]Effective Search of Logical Forms for Weakly Supervised Knowledge-Based Question Answering
    • [cs.CL]Effective Use of Transformer Networks for Entity Tracking
    • [cs.CL]Extracting and Learning a Dependency-Enhanced Type Lexicon for Dutch
    • [cs.CL]Features in Extractive Supervised Single-document Summarization: Case of Persian News
    • [cs.CL]Giveme5W1H: A Universal System for Extracting Main Events from News Articles
    • [cs.CL]In Plain Sight: Media Bias Through the Lens of Factual Reporting
    • [cs.CL]Incorporating External Knowledge into Machine Reading for Generative Question Answering
    • [cs.CL]Investigating BERT’s Knowledge of Language: Five Analysis Methods with NPIs
    • [cs.CL]MoverScore: Text Generation Evaluating with Contextualized Embeddings and Earth Mover Distance
    • [cs.CL]RNN Architecture Learning with Sparse Regularization
    • [cs.CL]Supervised Multimodal Bitransformers for Classifying Images and Text
    • [cs.CL]Syntax-Aware Aspect Level Sentiment Classification with Graph Attention Networks
    • [cs.CL]TEASPN: Framework and Protocol for Integrated Writing Assistance Environments
    • [cs.CL]Towards Multimodal Emotion Recognition in German Speech Events in Cars using Transfer Learning
    • [cs.CL]Uncertain Natural Language Inference
    • [cs.CL]User Evaluation of a Multi-dimensional Statistical Dialogue System
    • [cs.CR]Full-text Search for Verifiable Credential Metadata on Distributed Ledgers
    • [cs.CR]Invisible Backdoor Attacks Against Deep Neural Networks
    • [cs.CV]Automatic Weight Estimation of Harvested Fish from Images
    • [cs.CV]Coarse2Fine: A Two-stage Training Method for Fine-grained Visual Classification
    • [cs.CV]Deep Iterative Frame Interpolation for Full-frame Video Stabilization
    • [cs.CV]Deep Visual Template-Free Form Parsing
    • [cs.CV]Discriminative and Robust Online Learning for Siamese Visual Tracking
    • [cs.CV]Explicit Facial Expression Transfer via Fine-Grained Semantic Representations
    • [cs.CV]Image anomaly detection with capsule networks and imbalanced datasets
    • [cs.CV]Multi-layer Domain Adaptation for Deep Convolutional Networks
    • [cs.CV]Neural Style-Preserving Visual Dubbing
    • [cs.CV]Running Event Visualization using Videos from Multiple Cameras
    • [cs.CV]Semantic Correlation Promoted Shape-Variant Context for Segmentation
    • [cs.CV]Video Interpolation and Prediction with Unsupervised Landmarks
    • [cs.CV]Visual Semantic Reasoning for Image-Text Matching
    • [cs.CY]Blockchain Technologies for Smart Energy Systems: Fundamentals, Challenges and Solutions
    • [cs.CY]Reproducibility via Crowdsourced Reverse Engineering: A Neural Network Case Study With DeepMind’s Alpha Zero
    • [cs.CY]The Difficulties of Addressing Interdisciplinary Challenges at the Foundations of Data Science
    • [cs.DB]Agora: Towards An Open Ecosystem for Democratizing Data Science & Artificial Intelligence
    • [cs.DC]A Proposed Framework for Interactive Virtual Reality In Situ Visualization of Parallel Numerical Simulations
    • [cs.DC]An Automatic Debugging Tool of Instruction-Driven Multicore Systems with Synchronization Points
    • [cs.DC]Asynchronous Byzantine Consensus on Undirected Graphs under Local Broadcast Model
    • [cs.DC]HNMTP Conv: Optimize Convolution Algorithm for Single-Image Convolution Neural Network Inference on Mobile GPUs
    • [cs.DC]iFDK: A Scalable Framework for Instant High-resolution Image Reconstruction
    • [cs.DM]An Effective Upperbound on Treewidth Using Partial Fill-in of Separators
    • cs.HCinformed Consent: Studying GDPR Consent Notices in the Field
    • [cs.IR]Pairwise Learning to Rank by Neural Networks Revisited: Reconstruction, Theoretical Analysis and Practical Performance
    • [cs.IT]Asymptotic Optimality in Byzantine Distributed Quickest Change Detection
    • [cs.IT]Deep Learning for Spectrum Sensing
    • [cs.IT]Encoders and Decoders for Quantum Expander Codes Using Machine Learning
    • [cs.LG]A Baseline for Few-Shot Image Classification
    • [cs.LG]A Reinforcement Learning Based Approach for Joint Multi-Agent Decision Making
    • [cs.LG]A review on ranking problems in statistical learning
    • [cs.LG]Adaptive Trust Region Policy Optimization: Global Convergence and Faster Rates for Regularized MDPs
    • [cs.LG]Approaching Machine Learning Fairness through Adversarial Network
    • [cs.LG]AutoGMM: Automatic Gaussian Mixture Modeling in Python
    • [cs.LG]Bayes-Factor-VAE: Hierarchical Bayesian Deep Auto-Encoder Models for Factor Disentanglement
    • [cs.LG]Better PAC-Bayes Bounds for Deep Neural Networks using the Loss Curvature
    • [cs.LG]Blackbox Attacks on Reinforcement Learning Agents Using Approximated Temporal Information
    • [cs.LG]Classification with Costly Features as a Sequential Decision-Making Problem
    • [cs.LG]DRLViz: Understanding Decisions and Memory in Deep Reinforcement Learning
    • [cs.LG]Decentralized Stochastic Gradient Tracking for Empirical Risk Minimization
    • [cs.LG]Diversely Stale Parameters for Efficient Training of CNNs
    • [cs.LG]Efficient Communication in Multi-Agent Reinforcement Learning via Variance Based Control
    • [cs.LG]Efficient Multivariate Bandit Algorithm with Path Planning
    • [cs.LG]Gradient Q$(σ, λ)$: A Unified Algorithm with Function Approximation for Reinforcement Learning
    • [cs.LG]Improved Patient Classification with Language Model Pretraining Over Clinical Notes
    • [cs.LG]Mass Personalization of Deep Learning
    • [cs.LG]Master your Metrics with Calibration
    • [cs.LG]NEAR: Neighborhood Edge AggregatoR for Graph Classification
    • [cs.LG]Optimizing Generalized Rate Metrics through Game Equilibrium
    • [cs.LG]Parallel Computation of Graph Embeddings
    • [cs.LG]Regression Under Human Assistance
    • [cs.LG]Robust Logistic Regression against Attribute and Label Outliers via Information Theoretic Learning
    • [cs.LG]Set Flow: A Permutation Invariant Normalizing Flow
    • [cs.LG]Show Your Work: Improved Reporting of Experimental Results
    • [cs.LG]Spatiotemporally Constrained Action Space Attacks on Deep Reinforcement Learning Agents
    • [cs.LG]TFCheck : A TensorFlow Library for Detecting Training Issues in Neural Network Programs
    • [cs.MA]Multi-Objective Multi-Agent Decision Making: A Utility-based Analysis and Survey
    • [cs.NE]Additive function approximation in the brain
    • [cs.NE]Port-Hamiltonian Approach to Neural Network Training
    • [cs.RO]AR-based interaction for safe human-robot collaborative manufacturing
    • [cs.RO]Automatic Failure Recovery for End-User Programs on Service Mobile Robots
    • [cs.RO]Real-time Joint Motion Analysis and Instrument Tracking for Robot-Assisted Orthopaedic Surgery
    • [cs.RO]Robust Barrier Functions for a Fully Autonomous, Remotely Accessible Swarm-Robotics Testbed
    • [cs.RO]SocNav1: A Dataset to Benchmark and Learn Social Navigation Conventions
    • [cs.SD]Neural Network-Based Modeling of Phonetic Durations
    • [cs.SI]Analyzing Network Effects on a Fanfiction Community
    • [cs.SI]Causal Effects of Brevity on Style and Success in Social Media
    • [cs.SI]Graph Representation Ensemble Learning
    • [eess.AS]Bandwidth Embeddings for Mixed-bandwidth Speech Recognition
    • [eess.IV]A new operation mode for depth-focused high-sensitivity ToF range finding
    • [eess.IV]Deep CNN frameworks comparison for malaria diagnosis
    • [eess.IV]Deep Learning for Brain Tumor Segmentation in Radiosurgery: Prospective Clinical Evaluation
    • [eess.IV]Eelgrass beds and oyster farming at a lagoon before and after the Great East Japan Earthquake 2011: potential to apply deep learning at a coastal area
    • [eess.IV]Geolocation of an aircraft using image registration coupling modes for autonomous navigation
    • [eess.IV]Intensity augmentation for domain transfer of whole breast segmentation in MRI
    • [eess.IV]Unsupervised Clustering of Quantitative Imaging Phenotypes using Autoencoder and Gaussian Mixture Model
    • [eess.SP]Automated Polysomnography Analysis for Detection of Non-Apneic and Non-Hypopneic Arousals using Feature Engineering and a Bidirectional LSTM Network
    • [eess.SP]Low-Latency Communication with Computational Complexity Constraints
    • [eess.SP]Supervised Learning Based Super Resolution DOA Estimation Utilizing Antenna Array Subsets
    • [eess.SY]Fast Trajectory Planning for Multiple Quadrotors using Relative Safe Flight Corridor
    • [eess.SY]On Epidemic Spreading under Mobility on Networks
    • [math.ST]BNB autoregressions for modeling integer-valued time series with extreme observations
    • [math.ST]Block bootstrap optimality for density estimation with dependent data
    • [math.ST]Generalization of the simplicial depth: no vanishment outside the convex hull of the distribution support
    • [math.ST]Optimal unbiased estimators via convex hulls
    • [q-bio.QM]Contextual Minimum-Norm Estimates (CMNE): A Deep Learning Method for Source Estimation in Neuronal Networks
    • [q-bio.QM]Network-Based Approach for Modeling and Analyzing Coronary Angiography
    • [quant-ph]Extreme dimensional compression with quantum modelling
    • [stat.AP]A simulation study of methods for handling disease progression in dose-finding clinical trials
    • [stat.AP]Changepoint analysis of historical battle deaths
    • [stat.AP]Demand Forecasting in the Presence of Systematic Events: Cases in Capturing Sales Promotions
    • [stat.AP]The Role of Shopping Mission in Retail Customer Segmentation
    • [stat.CO]A review of Approximate Bayesian Computation methods via density estimation: inference for simulator-models
    • [stat.ME]A Bayesian Approach to Multiple-Output Quantile Regression
    • [stat.ME]A Pólya-Gamma Sampler for a Generalized Logistic Regression
    • [stat.ME]Bayesian Semiparametric Estimation with Nonignorable Nonresponse
    • [stat.ME]Covariate Selection for Generalizing Experimental Results
    • [stat.ME]Estimation and inference in metabolomics with non-random missing data and latent factors
    • [stat.ME]Optimal curtailed designs for single arm phase II clinical trials
    • [stat.ML]Differentially Private Precision Matrix Estimation
    • [stat.ML]On the Estimation of Network Complexity: Dimension of Graphons
    • [stat.ML]Quantized Fisher Discriminant Analysis

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    • [astro-ph.IM]Astroalign: A Python module for astronomical image registration
    Martin Beroiz, Juan B. Cabral, Bruno Sanchez
    http://arxiv.org/abs/1909.02946v1

    • [cs.AI]A Comparative Study of Some Central Notions of ASPIC+ and DeLP
    Alejandro J. Garcia, Henry Prakken, Guillermo R. Simari
    http://arxiv.org/abs/1909.02810v1

    • [cs.AI]From Few to More: Large-scale Dynamic Multiagent Curriculum Learning
    Weixun Wang, Tianpei Yang, Yong Liu, Jianye Hao, Xiaotian Hao, Yujing Hu, Yingfeng Chen, Changjie Fan, Yang Gao
    http://arxiv.org/abs/1909.02790v1

    • [cs.AI]One Explanation Does Not Fit All: A Toolkit and Taxonomy of AI Explainability Techniques
    Vijay Arya, Rachel K. E. Bellamy, Pin-Yu Chen, Amit Dhurandhar, Michael Hind, Samuel C. Hoffman, Stephanie Houde, Q. Vera Liao, Ronny Luss, Aleksandra Mojsilović, Sami Mourad, Pablo Pedemonte, Ramya Raghavendra, John Richards, Prasanna Sattigeri, Karthikeyan Shanmugam, Moninder Singh, Kush R. Varshney, Dennis Wei, Yunfeng Zhang
    http://arxiv.org/abs/1909.03012v1

    • [cs.AI]Structured Query Construction via Knowledge Graph Embedding
    Ruijie Wang, Meng Wang, Jun Liu, Michael Cochez, Stefan Decker
    http://arxiv.org/abs/1909.02930v1

    • [cs.CL]#MeTooMaastricht: Building a chatbot to assist survivors of sexual harassment
    Tobias Bauer, Emre Devrim, Misha Glazunov, William Lopez Jaramillo, Balaganesh Mohan, Gerasimos Spanakis
    http://arxiv.org/abs/1909.02809v1

    • [cs.CL]A systematic comparison of methods for low-resource dependency parsing on genuinely low-resource languages
    Clara Vania, Yova Kementchedjhieva, Anders Søgaard, Adam Lopez
    http://arxiv.org/abs/1909.02857v1

    • [cs.CL]Adversarial Examples with Difficult Common Words for Paraphrase Identification
    Zhouxing Shi, Minlie Huang, Ting Yao, Jingfang Xu
    http://arxiv.org/abs/1909.02560v2

    • [cs.CL]Annotating Student Talk in Text-based Classroom Discussions
    Luca Lugini, Diane Litman, Amanda Godley, Christopher Olshefski
    http://arxiv.org/abs/1909.03023v1

    • [cs.CL]Argument Component Classification for Classroom Discussions
    Luca Lugini, Diane Litman
    http://arxiv.org/abs/1909.03022v1

    • [cs.CL]Broad-Coverage Semantic Parsing as Transduction
    Sheng Zhang, Xutai Ma, Kevin Duh, Benjamin Van Durme
    http://arxiv.org/abs/1909.02607v1

    • [cs.CL]Deep learning with sentence embeddings pre-trained on biomedical corpora improves the performance of finding similar sentences in electronic medical records
    Qingyu Chen, Jingcheng Du, Sun Kim, W. John Wilbur, Zhiyong Lu
    http://arxiv.org/abs/1909.03044v1

    • [cs.CL]Don’t Forget the Long Tail! A Comprehensive Analysis of Morphological Generalization in Bilingual Lexicon Induction
    Paula Czarnowska, Sebastian Ruder, Edouard Grave, Ryan Cotterell, Ann Copestake
    http://arxiv.org/abs/1909.02855v1

    • [cs.CL]Effective Search of Logical Forms for Weakly Supervised Knowledge-Based Question Answering
    Tao Shen, Xiubo Geng, Tao Qin, Guodong Long, Jing Jiang, Daxin Jiang
    http://arxiv.org/abs/1909.02762v1

    • [cs.CL]Effective Use of Transformer Networks for Entity Tracking
    Aditya Gupta, Greg Durrett
    http://arxiv.org/abs/1909.02635v1

    • [cs.CL]Extracting and Learning a Dependency-Enhanced Type Lexicon for Dutch
    Konstantinos Kogkalidis
    http://arxiv.org/abs/1909.02955v1

    • [cs.CL]Features in Extractive Supervised Single-document Summarization: Case of Persian News
    Hosein Rezaei, Seyed Amid Moeinzadeh, Azar Shahgholian, Mohamad Saraee
    http://arxiv.org/abs/1909.02776v1

    • [cs.CL]Giveme5W1H: A Universal System for Extracting Main Events from News Articles
    Felix Hamborg, Corinna Breitinger, Bela Gipp
    http://arxiv.org/abs/1909.02766v1

    • [cs.CL]In Plain Sight: Media Bias Through the Lens of Factual Reporting
    Lisa Fan, Marshall White, Eva Sharma, Ruisi Su, Prafulla Kumar Choubey, Ruihong Huang, Lu Wang
    http://arxiv.org/abs/1909.02670v1

    • [cs.CL]Incorporating External Knowledge into Machine Reading for Generative Question Answering
    Bin Bi, Chen Wu, Ming Yan, Wei Wang, Jiangnan Xia, Chenliang Li
    http://arxiv.org/abs/1909.02745v1

    • [cs.CL]Investigating BERT’s Knowledge of Language: Five Analysis Methods with NPIs
    Alex Warstadt, Yu Cao, Ioana Grosu, Wei Peng, Hagen Blix, Yining Nie, Anna Alsop, Shikha Bordia, Haokun Liu, Alicia Parrish, Sheng-Fu Wang, Jason Phang, Anhad Mohananey, Phu Mon Htut, Paloma Jeretič, Samuel R. Bowman
    http://arxiv.org/abs/1909.02597v1

    • [cs.CL]MoverScore: Text Generation Evaluating with Contextualized Embeddings and Earth Mover Distance
    Wei Zhao, Maxime Peyrard, Fei Liu, Yang Gao, Christian M. Meyer, Steffen Eger
    http://arxiv.org/abs/1909.02622v1

    • [cs.CL]RNN Architecture Learning with Sparse Regularization
    Jesse Dodge, Roy Schwartz, Hao Peng, Noah A. Smith
    http://arxiv.org/abs/1909.03011v1

    • [cs.CL]Supervised Multimodal Bitransformers for Classifying Images and Text
    Douwe Kiela, Suvrat Bhooshan, Hamed Firooz, Davide Testuggine
    http://arxiv.org/abs/1909.02950v1

    • [cs.CL]Syntax-Aware Aspect Level Sentiment Classification with Graph Attention Networks
    Binxuan Huang, Kathleen M. Carley
    http://arxiv.org/abs/1909.02606v1

    • [cs.CL]TEASPN: Framework and Protocol for Integrated Writing Assistance Environments
    Masato Hagiwara, Takumi Ito, Tatsuki Kuribayashi, Jun Suzuki, Kentaro Inui
    http://arxiv.org/abs/1909.02621v1

    • [cs.CL]Towards Multimodal Emotion Recognition in German Speech Events in Cars using Transfer Learning
    Deniz Cevher, Sebastian Zepf, Roman Klinger
    http://arxiv.org/abs/1909.02764v1

    • [cs.CL]Uncertain Natural Language Inference
    Tongfei Chen, Zhengping Jiang, Keisuke Sakaguchi, Benjamin Van Durme
    http://arxiv.org/abs/1909.03042v1

    • [cs.CL]User Evaluation of a Multi-dimensional Statistical Dialogue System
    Simon Keizer, Ondřej Dušek, Xingkun Liu, Verena Rieser
    http://arxiv.org/abs/1909.02965v1

    • [cs.CR]Full-text Search for Verifiable Credential Metadata on Distributed Ledgers
    Zoltán András Lux, Felix Beierle, Sebastian Zickau, Sebastian Göndör
    http://arxiv.org/abs/1909.02895v1

    • [cs.CR]Invisible Backdoor Attacks Against Deep Neural Networks
    Shaofeng Li, Benjamin Zi Hao Zhao, Jiahao Yu, Minhui Xue, Dali Kaafar, Haojin Zhu
    http://arxiv.org/abs/1909.02742v1

    • [cs.CV]Automatic Weight Estimation of Harvested Fish from Images
    Dmitry A. Konovalov, Alzayat Saleh, Dina B. Efremova, Jose A. Domingos, Dean R. Jerry
    http://arxiv.org/abs/1909.02710v1

    • [cs.CV]Coarse2Fine: A Two-stage Training Method for Fine-grained Visual Classification
    Amir Erfan Eshratifar, David Eigen, Michael Gormish, Massoud Pedram
    http://arxiv.org/abs/1909.02680v1

    • [cs.CV]Deep Iterative Frame Interpolation for Full-frame Video Stabilization
    Jinsoo Choi, In So Kweon
    http://arxiv.org/abs/1909.02641v1

    • [cs.CV]Deep Visual Template-Free Form Parsing
    Brian Davis, Bryan Morse, Scott Cohen, Brian Price, Chris Tensmeyer
    http://arxiv.org/abs/1909.02576v1

    • [cs.CV]Discriminative and Robust Online Learning for Siamese Visual Tracking
    Jinghao Zhou, Peng Wang, Haoyang Sun
    http://arxiv.org/abs/1909.02959v1

    • [cs.CV]Explicit Facial Expression Transfer via Fine-Grained Semantic Representations
    Zhiwen Shao, Hengliang Zhu, Junshu Tang, Xuequan Lu, Lizhuang Ma
    http://arxiv.org/abs/1909.02967v1

    • [cs.CV]Image anomaly detection with capsule networks and imbalanced datasets
    Claudio Piciarelli, Pankaj Mishra, Gian Luca Foresti
    http://arxiv.org/abs/1909.02755v1

    • [cs.CV]Multi-layer Domain Adaptation for Deep Convolutional Networks
    Ozan Ciga, Jianan Chen, Anne Martel
    http://arxiv.org/abs/1909.02620v1

    • [cs.CV]Neural Style-Preserving Visual Dubbing
    Hyeongwoo Kim, Mohamed Elgharib, Michael Zollhöfer, Hans-Peter Seidel, Thabo Beeler, Christian Richardt, Christian Theobalt
    http://arxiv.org/abs/1909.02518v2

    • [cs.CV]Running Event Visualization using Videos from Multiple Cameras
    Yeshwanth Napolean, Priadi Teguh Wibowo, Jan van Gemert
    http://arxiv.org/abs/1909.02835v1

    • [cs.CV]Semantic Correlation Promoted Shape-Variant Context for Segmentation
    Henghui Ding, Xudong Jiang, Bing Shuai, Ai Qun Liu, Gang Wang
    http://arxiv.org/abs/1909.02651v1

    • [cs.CV]Video Interpolation and Prediction with Unsupervised Landmarks
    Kevin J. Shih, Aysegul Dundar, Animesh Garg, Robert Pottorf, Andrew Tao, Bryan Catanzaro
    http://arxiv.org/abs/1909.02749v1

    • [cs.CV]Visual Semantic Reasoning for Image-Text Matching
    Kunpeng Li, Yulun Zhang, Kai Li, Yuanyuan Li, Yun Fu
    http://arxiv.org/abs/1909.02701v1

    • [cs.CY]Blockchain Technologies for Smart Energy Systems: Fundamentals, Challenges and Solutions
    Naveed UL Hassan, Chau Yuen, Dusit Niyato
    http://arxiv.org/abs/1909.02914v1

    • [cs.CY]Reproducibility via Crowdsourced Reverse Engineering: A Neural Network Case Study With DeepMind’s Alpha Zero
    Dustin Tanksley, Donald C. Wunsch II
    http://arxiv.org/abs/1909.03032v1

    • [cs.CY]The Difficulties of Addressing Interdisciplinary Challenges at the Foundations of Data Science
    Michael W. Mahoney
    http://arxiv.org/abs/1909.03033v1

    • [cs.DB]Agora: Towards An Open Ecosystem for Democratizing Data Science & Artificial Intelligence
    Jonas Traub, Jorge-Arnulfo Quiané-Ruiz, Zoi Kaoudi, Volker Markl
    http://arxiv.org/abs/1909.03026v1

    • [cs.DC]A Proposed Framework for Interactive Virtual Reality In Situ Visualization of Parallel Numerical Simulations
    Aryaman Gupta, Ulrik Günther, Pietro Incardona, Ata Deniz Aydin, Raimund Dachselt, Stefan Gumhold, Ivo F. Sbalzarini
    http://arxiv.org/abs/1909.02986v1

    • [cs.DC]An Automatic Debugging Tool of Instruction-Driven Multicore Systems with Synchronization Points
    Yuzhe Luo, Xin Yu
    http://arxiv.org/abs/1909.02791v1

    • [cs.DC]Asynchronous Byzantine Consensus on Undirected Graphs under Local Broadcast Model
    Muhammad Samir Khan, Nitin Vaidya
    http://arxiv.org/abs/1909.02865v1

    • [cs.DC]HNMTP Conv: Optimize Convolution Algorithm for Single-Image Convolution Neural Network Inference on Mobile GPUs
    Zhuoran Ji
    http://arxiv.org/abs/1909.02765v1

    • [cs.DC]iFDK: A Scalable Framework for Instant High-resolution Image Reconstruction
    Peng Chen, Mohamed Wahib, Shinichiro Takizawa, Ryousei Takano, Satoshi Matsuoka
    http://arxiv.org/abs/1909.02724v1

    • [cs.DM]An Effective Upperbound on Treewidth Using Partial Fill-in of Separators
    Boi Faltings, Martin Charles Golumbic
    http://arxiv.org/abs/1909.02789v1

    • [cs.HC](Un)informed Consent: Studying GDPR Consent Notices in the Field
    Christine Utz, Martin Degeling, Sascha Fahl, Florian Schaub, Thorsten Holz
    http://arxiv.org/abs/1909.02638v1

    • [cs.IR]Pairwise Learning to Rank by Neural Networks Revisited: Reconstruction, Theoretical Analysis and Practical Performance
    Marius Köppel, Alexander Segner, Martin Wagener, Lukas Pensel, Andreas Karwath, Stefan Kramer
    http://arxiv.org/abs/1909.02768v1

    • [cs.IT]Asymptotic Optimality in Byzantine Distributed Quickest Change Detection
    Yu-Chih Huang, Yu-Jui Huang, Shih-Chun Lin
    http://arxiv.org/abs/1909.02686v1

    • [cs.IT]Deep Learning for Spectrum Sensing
    Jiabao Gao, Xuemei Yi, Caijun Zhong, Xiaoming Chen, Zhaoyang Zhang
    http://arxiv.org/abs/1909.02730v1

    • [cs.IT]Encoders and Decoders for Quantum Expander Codes Using Machine Learning
    Sathwik Chadaga, Mridul Agarwal, Vaneet Aggarwal
    http://arxiv.org/abs/1909.02945v1

    • [cs.LG]A Baseline for Few-Shot Image Classification
    Guneet S. Dhillon, Pratik Chaudhari, Avinash Ravichandran, Stefano Soatto
    http://arxiv.org/abs/1909.02729v1

    • [cs.LG]A Reinforcement Learning Based Approach for Joint Multi-Agent Decision Making
    Mridul Agarwal, Vaneet Aggarwal
    http://arxiv.org/abs/1909.02940v1

    • [cs.LG]A review on ranking problems in statistical learning
    Tino Werner
    http://arxiv.org/abs/1909.02998v1

    • [cs.LG]Adaptive Trust Region Policy Optimization: Global Convergence and Faster Rates for Regularized MDPs
    Lior Shani, Yonathan Efroni, Shie Mannor
    http://arxiv.org/abs/1909.02769v1

    • [cs.LG]Approaching Machine Learning Fairness through Adversarial Network
    Xiaoqian Wang, Heng Huang
    http://arxiv.org/abs/1909.03013v1

    • [cs.LG]AutoGMM: Automatic Gaussian Mixture Modeling in Python
    Thomas L. Athey, Joshua T. Vogelstein
    http://arxiv.org/abs/1909.02688v1

    • [cs.LG]Bayes-Factor-VAE: Hierarchical Bayesian Deep Auto-Encoder Models for Factor Disentanglement
    Minyoung Kim, Yuting Wang, Pritish Sahu, Vladimir Pavlovic
    http://arxiv.org/abs/1909.02820v1

    • [cs.LG]Better PAC-Bayes Bounds for Deep Neural Networks using the Loss Curvature
    Konstantinos Pitas
    http://arxiv.org/abs/1909.03009v1

    • [cs.LG]Blackbox Attacks on Reinforcement Learning Agents Using Approximated Temporal Information
    Yiren Zhao, Ilia Shumailov, Han Cui, Xitong Gao, Robert Mullins, Ross Anderson
    http://arxiv.org/abs/1909.02918v1

    • [cs.LG]Classification with Costly Features as a Sequential Decision-Making Problem
    Jaromír Janisch, Tomáš Pevný, Viliam Lisý
    http://arxiv.org/abs/1909.02564v1

    • [cs.LG]DRLViz: Understanding Decisions and Memory in Deep Reinforcement Learning
    Theo Jaunet, Romain Vuillemot, Christian Wolf
    http://arxiv.org/abs/1909.02982v1

    • [cs.LG]Decentralized Stochastic Gradient Tracking for Empirical Risk Minimization
    Jiaqi Zhang, Keyou You
    http://arxiv.org/abs/1909.02712v1

    • [cs.LG]Diversely Stale Parameters for Efficient Training of CNNs
    An Xu, Zhouyuan Huo, Heng Huang
    http://arxiv.org/abs/1909.02625v1

    • [cs.LG]Efficient Communication in Multi-Agent Reinforcement Learning via Variance Based Control
    Sai Qian Zhang, Qi Zhang, Jieyu Lin
    http://arxiv.org/abs/1909.02682v1

    • [cs.LG]Efficient Multivariate Bandit Algorithm with Path Planning
    Keyu Nie, Zezhong Zhang, Ted Tao Yuan, Rong Song, Pauline Berry Burke
    http://arxiv.org/abs/1909.02705v1

    • [cs.LG]Gradient Q$(σ, λ)$: A Unified Algorithm with Function Approximation for Reinforcement Learning
    Long Yang, Yu Zhang, Qian Zheng, Pengfei Li, Gang Pan
    http://arxiv.org/abs/1909.02877v1

    • [cs.LG]Improved Patient Classification with Language Model Pretraining Over Clinical Notes
    Jonas Kemp, Alvin Rajkomar, Andrew M. Dai
    http://arxiv.org/abs/1909.03039v1

    • [cs.LG]Mass Personalization of Deep Learning
    Johannes Schneider, Michail Vlachos
    http://arxiv.org/abs/1909.02803v1

    • [cs.LG]Master your Metrics with Calibration
    Wissam Siblini, Jordan Fréry, Liyun He-Guelton, Frédéric Oblé, Yi-Qing Wang
    http://arxiv.org/abs/1909.02827v1

    • [cs.LG]NEAR: Neighborhood Edge AggregatoR for Graph Classification
    Cheolhyeong Kim, Haeseong Moon, Hyung Ju Hwang
    http://arxiv.org/abs/1909.02746v1

    • [cs.LG]Optimizing Generalized Rate Metrics through Game Equilibrium
    Harikrishna Narasimhan, Andrew Cotter, Maya Gupta
    http://arxiv.org/abs/1909.02939v1

    • [cs.LG]Parallel Computation of Graph Embeddings
    Chi Thang Duong, Hongzhi Yin, Thanh Dat Hoang, Truong Giang Le Ba, Matthias Weidlich, Quoc Viet Hung Nguyen, Karl Aberer
    http://arxiv.org/abs/1909.02977v1

    • [cs.LG]Regression Under Human Assistance
    Abir De, Paramita Koley, Niloy Ganguly, Manuel Gomez-Rodriguez
    http://arxiv.org/abs/1909.02963v1

    • [cs.LG]Robust Logistic Regression against Attribute and Label Outliers via Information Theoretic Learning
    Yuanhao Li, Badong Chen, Natsue Yoshimura, Yasuharu Koike
    http://arxiv.org/abs/1909.02707v1

    • [cs.LG]Set Flow: A Permutation Invariant Normalizing Flow
    Kashif Rasul, Ingmar Schuster, Roland Vollgraf, Urs Bergmann
    http://arxiv.org/abs/1909.02775v1

    • [cs.LG]Show Your Work: Improved Reporting of Experimental Results
    Jesse Dodge, Suchin Gururangan, Dallas Card, Roy Schwartz, Noah A. Smith
    http://arxiv.org/abs/1909.03004v1

    • [cs.LG]Spatiotemporally Constrained Action Space Attacks on Deep Reinforcement Learning Agents
    Xian Yeow Lee, Sambit Ghadai, Kai Liang Tan, Chinmay Hegde, Soumik Sarkar
    http://arxiv.org/abs/1909.02583v1

    • [cs.LG]TFCheck : A TensorFlow Library for Detecting Training Issues in Neural Network Programs
    Houssem Ben Braiek, Foutse Khomh
    http://arxiv.org/abs/1909.02562v1

    • [cs.MA]Multi-Objective Multi-Agent Decision Making: A Utility-based Analysis and Survey
    Roxana Rădulescu, Patrick Mannion, Diederik M. Roijers, Ann Nowé
    http://arxiv.org/abs/1909.02964v1

    • [cs.NE]Additive function approximation in the brain
    Kameron Decker Harris
    http://arxiv.org/abs/1909.02603v1

    • [cs.NE]Port-Hamiltonian Approach to Neural Network Training
    Stefano Massaroli, Michael Poli, Federico Califano, Angela Faragasso, Jinkyoo Park, Atsushi Yamashita, Hajime Asama
    http://arxiv.org/abs/1909.02702v1

    • [cs.RO]AR-based interaction for safe human-robot collaborative manufacturing
    Antti Hietanen, Jyrki Latokartano, Roel Pieters, Minna Lanz, Joni-Kristian Kämäräinen
    http://arxiv.org/abs/1909.02933v1

    • [cs.RO]Automatic Failure Recovery for End-User Programs on Service Mobile Robots
    Jenna Claire Hammond, Joydeep Biswas, Arjun Guha
    http://arxiv.org/abs/1909.02778v1

    • [cs.RO]Real-time Joint Motion Analysis and Instrument Tracking for Robot-Assisted Orthopaedic Surgery
    Mario Strydom, Artur Banach, Liao Wu, Ross Crawford, Jonathan Roberts, Anjali Jaiprakash
    http://arxiv.org/abs/1909.02721v1

    • [cs.RO]Robust Barrier Functions for a Fully Autonomous, Remotely Accessible Swarm-Robotics Testbed
    Yousef Emam, Paul Glotfelter, Magnus Egerstedt
    http://arxiv.org/abs/1909.02966v1

    • [cs.RO]SocNav1: A Dataset to Benchmark and Learn Social Navigation Conventions
    Luis J. Manso, Pedro Nunez, Luis V. Calderita, Diego R. Faria, Pilar Bachiller
    http://arxiv.org/abs/1909.02993v1

    • [cs.SD]Neural Network-Based Modeling of Phonetic Durations
    Xizi Wei, Melvyn Hunt, Adrian Skilling
    http://arxiv.org/abs/1909.03030v1

    • [cs.SI]Analyzing Network Effects on a Fanfiction Community
    Andrés Carvallo, Denis Parra, Eduardo Graells-Garrido
    http://arxiv.org/abs/1909.02886v1

    • [cs.SI]Causal Effects of Brevity on Style and Success in Social Media
    Kristina Gligoric, Ashton Anderson, Robert West
    http://arxiv.org/abs/1909.02565v1

    • [cs.SI]Graph Representation Ensemble Learning
    Palash Goyal, Di Huang, Sujit Rokka Chhetri, Arquimedes Canedo, Jaya Shree, Evan Patterson
    http://arxiv.org/abs/1909.02811v1

    • [eess.AS]Bandwidth Embeddings for Mixed-bandwidth Speech Recognition
    Gautam Mantena, Ozlem Kalinli, Ossama Abdel-Hamid, Don McAllaster
    http://arxiv.org/abs/1909.02667v1

    • [eess.IV]A new operation mode for depth-focused high-sensitivity ToF range finding
    Sebastian Werner, Henrik Schäfer, Matthias Hullin
    http://arxiv.org/abs/1909.02759v1

    • [eess.IV]Deep CNN frameworks comparison for malaria diagnosis
    Priyadarshini Adyasha Pattanaik, Zelong Wang, Patrick Horain
    http://arxiv.org/abs/1909.02829v1

    • [eess.IV]Deep Learning for Brain Tumor Segmentation in Radiosurgery: Prospective Clinical Evaluation
    Boris Shirokikh, Alexandra Dalechina, Alexey Shevtsov, Egor Krivov, Valery Kostjuchenko, Amayak Durgaryan, Mikhail Galkin, Ivan Osinov, Andrey Golanov, Mikhail Belyaev
    http://arxiv.org/abs/1909.02799v1

    • [eess.IV]Eelgrass beds and oyster farming at a lagoon before and after the Great East Japan Earthquake 2011: potential to apply deep learning at a coastal area
    Takehisa Yamakita
    http://arxiv.org/abs/1909.02747v1

    • [eess.IV]Geolocation of an aircraft using image registration coupling modes for autonomous navigation
    Nima Ziaei
    http://arxiv.org/abs/1909.02875v1

    • [eess.IV]Intensity augmentation for domain transfer of whole breast segmentation in MRI
    Linde S. Hesse, Grey Kuling, Mitko Veta, Anne L. Martel
    http://arxiv.org/abs/1909.02642v1

    • [eess.IV]Unsupervised Clustering of Quantitative Imaging Phenotypes using Autoencoder and Gaussian Mixture Model
    Jianan Chen, Laurent Milot, Helen M. C. Cheung, Anne L. Martel
    http://arxiv.org/abs/1909.02953v1

    • [eess.SP]Automated Polysomnography Analysis for Detection of Non-Apneic and Non-Hypopneic Arousals using Feature Engineering and a Bidirectional LSTM Network
    Ali Bahrami Rad, Morteza Zabihi, Zheng Zhao, Moncef Gabbouj, Aggelos K. Katsaggelos, Simo Särkkä
    http://arxiv.org/abs/1909.02971v1

    • [eess.SP]Low-Latency Communication with Computational Complexity Constraints
    Hasan Basri Celebi, Antonios Pitarokoilis, Mikael Skoglund
    http://arxiv.org/abs/1909.02740v1

    • [eess.SP]Supervised Learning Based Super Resolution DOA Estimation Utilizing Antenna Array Subsets
    Udaya Sampath K. P. Miriya Thanthrige, Aya Mostafa Ahmed, Aydin Sezgin
    http://arxiv.org/abs/1909.02825v1

    • [eess.SY]Fast Trajectory Planning for Multiple Quadrotors using Relative Safe Flight Corridor
    Jungwon Park, H. Jin Kim
    http://arxiv.org/abs/1909.02896v1

    • [eess.SY]On Epidemic Spreading under Mobility on Networks
    Vishal Abhishek, Vaibhav Srivastava
    http://arxiv.org/abs/1909.02647v1

    • [math.ST]BNB autoregressions for modeling integer-valued time series with extreme observations
    Paolo Gorgi
    http://arxiv.org/abs/1909.02929v1

    • [math.ST]Block bootstrap optimality for density estimation with dependent data
    Todd A. Kuffner, Stephen M. -S. Lee, G. Alastair Young
    http://arxiv.org/abs/1909.02662v1

    • [math.ST]Generalization of the simplicial depth: no vanishment outside the convex hull of the distribution support
    Giacomo Francisci, Alicia Nieto-Reyes, Claudio Agostinelli
    http://arxiv.org/abs/1909.02739v1

    • [math.ST]Optimal unbiased estimators via convex hulls
    Nabil Kahale
    http://arxiv.org/abs/1909.02876v1

    • [q-bio.QM]Contextual Minimum-Norm Estimates (CMNE): A Deep Learning Method for Source Estimation in Neuronal Networks
    Christoph Dinh, John GW Samuelsson, Alexander Hunold, Matti S Hämäläinen, Sheraz Khan
    http://arxiv.org/abs/1909.02636v1

    • [q-bio.QM]Network-Based Approach for Modeling and Analyzing Coronary Angiography
    Babak Ravandi, Arash Ravandi
    http://arxiv.org/abs/1909.02664v1

    • [quant-ph]Extreme dimensional compression with quantum modelling
    Thomas J. Elliott, Chengran Yang, Felix C. Binder, Andrew J. P. Garner, Jayne Thompson, Mile Gu
    http://arxiv.org/abs/1909.02817v1

    • [stat.AP]A simulation study of methods for handling disease progression in dose-finding clinical trials
    Lucie Biard, Bin Cheng, Gulam A. Manji, Shing M. Lee
    http://arxiv.org/abs/1909.02913v1

    • [stat.AP]Changepoint analysis of historical battle deaths
    Brennen T. Fagan, Marina I. Knight, Niall J. MacKay, A. Jamie Wood
    http://arxiv.org/abs/1909.02626v1

    • [stat.AP]Demand Forecasting in the Presence of Systematic Events: Cases in Capturing Sales Promotions
    Mahdi Abolghasemi, Ali Eshragh, Jason Hurley, Behnam Fahimnia
    http://arxiv.org/abs/1909.02716v1

    • [stat.AP]The Role of Shopping Mission in Retail Customer Segmentation
    Ondřej Sokol, Vladimír Holý
    http://arxiv.org/abs/1909.02996v1

    • [stat.CO]A review of Approximate Bayesian Computation methods via density estimation: inference for simulator-models
    Clara Grazian, Yanan Fan
    http://arxiv.org/abs/1909.02736v1

    • [stat.ME]A Bayesian Approach to Multiple-Output Quantile Regression
    Michael Guggisberg
    http://arxiv.org/abs/1909.02623v1

    • [stat.ME]A Pólya-Gamma Sampler for a Generalized Logistic Regression
    Luciana Dalla Valle, Fabrizio Leisen, Luca Rossini, Weixuan Zhu
    http://arxiv.org/abs/1909.02989v1

    • [stat.ME]Bayesian Semiparametric Estimation with Nonignorable Nonresponse
    Shonosuke Sugasawa, Kosuke Morikawa, Keisuke Takahata
    http://arxiv.org/abs/1909.02878v1

    • [stat.ME]Covariate Selection for Generalizing Experimental Results
    Naoki Egami, Erin Hartman
    http://arxiv.org/abs/1909.02669v1

    • [stat.ME]Estimation and inference in metabolomics with non-random missing data and latent factors
    Chris McKennan, Carole Ober, Dan Nicolae
    http://arxiv.org/abs/1909.02644v1

    • [stat.ME]Optimal curtailed designs for single arm phase II clinical trials
    Martin Law, Michael J. Grayling, Adrian P. Mander
    http://arxiv.org/abs/1909.03017v1

    • [stat.ML]Differentially Private Precision Matrix Estimation
    Wenqing Su, Xiao Guo, Hai Zhang
    http://arxiv.org/abs/1909.02750v1

    • [stat.ML]On the Estimation of Network Complexity: Dimension of Graphons
    Yann Issartel
    http://arxiv.org/abs/1909.02900v1

    • [stat.ML]Quantized Fisher Discriminant Analysis
    Benyamin Ghojogh, Ali Saheb Pasand, Fakhri Karray, Mark Crowley
    http://arxiv.org/abs/1909.03037v1