astro-ph.CO - 宇宙学和天体物理学

    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.DS - 数据结构与算法 cs.IR - 信息检索 cs.IT - 信息论 cs.LG - 自动学习 cs.NE - 神经与进化计算 cs.RO - 机器人学 cs.SI - 社交网络与信息网络 econ.EM - 计量经济学 eess.IV - 图像与视频处理 eess.SP - 信号处理 eess.SY - 系统和控制 math.AT - 代数拓扑 math.OC - 优化与控制 math.ST - 统计理论 physics.comp-ph - 计算物理学 physics.soc-ph - 物理学与社会 stat.AP - 应用统计 stat.ME - 统计方法论 stat.ML - (统计)机器学习

    • [astro-ph.CO]From Dark Matter to Galaxies with Convolutional Neural Networks
    • [astro-ph.IM]Speeding simulation analysis up with yt and Intel Distribution for Python
    • [cs.AI]Explainable AI for Intelligence Augmentation in Multi-Domain Operations
    • [cs.AI]Exploring Semi-Automatic Map Labeling
    • [cs.AI]MAPEL: Multi-Agent Pursuer-Evader Learning using Situation Report
    • [cs.AI]Visual Hide and Seek
    • [cs.CL]BIG MOOD: Relating Transformers to Explicit Commonsense Knowledge
    • [cs.CL]Contextual Joint Factor Acoustic Embeddings
    • [cs.CL]Cross-lingual Parsing with Polyglot Training and Multi-treebank Learning: A Faroese Case Study
    • [cs.CL]H-VECTORS: Utterance-level Speaker Embedding Using A Hierarchical Attention Model
    • [cs.CL]LibriVoxDeEn: A Corpus for German-to-English Speech Translation and Speech Recognition
    • [cs.CL]PLATO: Pre-trained Dialogue Generation Model with Discrete Latent Variable
    • [cs.CL]Right-wing German Hate Speech on Twitter: Analysis and Automatic Detection
    • [cs.CL]Topical Keyphrase Extraction with Hierarchical Semantic Networks
    • [cs.CL]Towards Annotating and Creating Sub-Sentence Summary Highlights
    • [cs.CL]Universal Text Representation from BERT: An Empirical Study
    • [cs.CL]Using a KG-Copy Network for Non-Goal Oriented Dialogues
    • [cs.CR]Information-theoretic metrics for Local Differential Privacy protocols
    • [cs.CR]Lateral Astroturfing Attacks on Twitter Trending Topics
    • [cs.CR]Not Just Cloud Privacy: Protecting Client Privacy in Teacher-Student Learning
    • [cs.CV]A Combined Deep Learning-Gradient Boosting Machine Framework for Fluid Intelligence Prediction
    • [cs.CV]Can I teach a robot to replicate a line art
    • [cs.CV]Context-Aware Saliency Detection for Image Retargeting Using Convolutional Neural Networks
    • [cs.CV]Convolutional Character Networks
    • [cs.CV]Cross Attention Network for Few-shot Classification
    • [cs.CV]Deep Contextual Attention for Human-Object Interaction Detection
    • [cs.CV]Deep Semantic Segmentation of Natural and Medical Images: A Review
    • [cs.CV]DeepErase: Weakly Supervised Ink Artifact Removal in Document Text Images
    • [cs.CV]Detecting Urban Changes with Recurrent Neural Networks from Multitemporal Sentinel-2 Data
    • [cs.CV]Discrete Residual Flow for Probabilistic Pedestrian Behavior Prediction
    • [cs.CV]Global Saliency: Aggregating Saliency Maps to Assess Dataset Artefact Bias
    • [cs.CV]Go with the Flow: Perception-refined Physics Simulation
    • [cs.CV]Making Third Person Techniques Recognize First-Person Actions in Egocentric Videos
    • [cs.CV]Meta-learning for fast classifier adaptation to new users of Signature Verification systems
    • [cs.CV]NAMF: A Non-local Adaptive Mean Filter for Salt-and-Pepper Noise Removal
    • [cs.CV]On the Reliability of Cancelable Biometrics: Revisit the Irreversibility
    • [cs.CV]RGB-D Individual Segmentation
    • [cs.CV]Video Person Re-Identification using Learned Clip Similarity Aggregation
    • [cs.CY]Exploring the Role of Common Model of Cognition in Designing Adaptive Coaching Interactions for Health Behavior Change
    • [cs.CY]How to eliminate detour behaviors in E-hailing: On-line detection and Pricing regulation
    • [cs.CY]Scaling up Psychology via Scientific Regret Minimization: A Case Study in Moral Decision-Making
    • [cs.CY]The other side of the Coin: Risks of the Libra Blockchain
    • [cs.DB]Service Wrapper: a system for converting web data into web services
    • [cs.DC]A Tool for Automatically Suggesting Source-Code Optimizations for Complex GPU Kernels
    • [cs.DC]Adaptive Normalization in Streaming Data
    • [cs.DC]SNF: Serverless Network Functions
    • [cs.DC]The NebulaStream Platform: Data and Application Management for the Internet of Things
    • [cs.DC]UMap: Enabling Application-driven Optimizations for Page Management
    • [cs.DS]The Distributed Bloom Filter
    • [cs.IR]Cascading: Association Augmented Sequential Recommendation
    • [cs.IR]Indoor Information Retrieval using Lifelog Data
    • [cs.IR]Keyphrase Extraction from Disaster-related Tweets
    • [cs.IT]Few-weight codes over $\Bbb Fp+u\Bbb F_p$ associated with down sets and their distance optimal Gray image
    • [cs.IT]Mixed Monotonic Programming for Fast Global Optimization
    • [cs.IT]On the Kernel of $\Z    {2^s}$-Linear Simplex and MacDonald Codes
    • [cs.IT]Stochastic Geometry-Based Analysis of Airborne Base Stations with Laser-powered UAVs
    • [cs.IT]The Role of Coded Side Information in Single-Server Private Information Retrieval
    • [cs.LG]A Double Residual Compression Algorithm for Efficient Distributed Learning
    • [cs.LG]A New Defense Against Adversarial Images: Turning a Weakness into a Strength
    • [cs.LG]A Stochastic Variance Reduced Nesterov’s Accelerated Quasi-Newton Method
    • [cs.LG]A Survey of Deep Learning Techniques for Autonomous Driving
    • [cs.LG]Active Learning for Graph Neural Networks via Node Feature Propagation
    • [cs.LG]Adaptive Transfer Learning of Multi-View Time Series Classification
    • [cs.LG]An Exponential Learning Rate Schedule for Deep Learning
    • [cs.LG]Autoregressive Models: What Are They Good For?
    • [cs.LG]Collaborative Filtering with Label Consistent Restricted Boltzmann Machine
    • [cs.LG]Communication-Efficient Asynchronous Stochastic Frank-Wolfe over Nuclear-norm Balls
    • [cs.LG]Deep clustering with concrete k-means
    • [cs.LG]Design, Benchmarking and Explainability Analysis of a Game-Theoretic Framework towards Energy Efficiency in Smart Infrastructure
    • [cs.LG]Dynamic Local Regret for Non-convex Online Forecasting
    • [cs.LG]Effect of Superpixel Aggregation on Explanations in LIME — A Case Study with Biological Data
    • [cs.LG]Generalized Clustering by Learning to Optimize Expected Normalized Cuts
    • [cs.LG]Graph Embedding VAE: A Permutation Invariant Model of Graph Structure
    • [cs.LG]Instance adaptive adversarial training: Improved accuracy tradeoffs in neural nets
    • [cs.LG]KDE sampling for imbalanced class distribution
    • [cs.LG]Learning chordal extensions
    • [cs.LG]Mixture-of-Experts Variational Autoencoder for clustering and generating from similarity-based representations
    • [cs.LG]On Concept-Based Explanations in Deep Neural Networks
    • [cs.LG]Optimal Transport Based Generative Autoencoders
    • [cs.LG]Overcoming Forgetting in Federated Learning on Non-IID Data
    • [cs.LG]Predicting retrosynthetic pathways using a combined linguistic model and hyper-graph exploration strategy
    • [cs.LG]Probabilistic Deterministic Finite Automata and Recurrent Networks, Revisited
    • [cs.LG]Reducing the Computational Complexity of Pseudoinverse for the Incremental Broad Learning System on Added Inputs
    • [cs.LG]Reinforcement Learning for Robotic Manipulation using Simulated Locomotion Demonstrations
    • [cs.LG]Sharper bounds for uniformly stable algorithms
    • [cs.LG]Single Episode Policy Transfer in Reinforcement Learning
    • [cs.LG]Teaching Vehicles to Anticipate: A Systematic Study on Probabilistic Behavior Prediction using Large Data Sets
    • [cs.LG]Tensor Graph Convolutional Networks for Prediction on Dynamic Graphs
    • [cs.LG]Toward Subject Invariant and Class Disentangled Representation in BCI via Cross-Domain Mutual Information Estimator
    • [cs.LG]Towards a Precipitation Bias Corrector against Noise and Maldistribution
    • [cs.LG]WOTBoost: Weighted Oversampling Technique in Boosting for imbalanced learning
    • [cs.LG]ZO-AdaMM: Zeroth-Order Adaptive Momentum Method for Black-Box Optimization
    • [cs.NE]Parameter Optimization and Learning in a Spiking Neural Network for UAV Obstacle Avoidance targeting Neuromorphic Processors
    • [cs.RO]Adaptive Curriculum Generation from Demonstrations for Sim-to-Real Visuomotor Control
    • [cs.RO]Conditional Driving from Natural Language Instructions
    • [cs.RO]Learning from My Partner’s Actions: Roles in Decentralized Robot Teams
    • [cs.RO]Self-supervised 3D Shape and Viewpoint Estimation from Single Images for Robotics
    • [cs.SI]Beyond Fortune 500: Women in a Global Network of Directors
    • [cs.SI]Community Detection in Multiplex Networks
    • [cs.SI]DeepFork: Supervised Prediction of Information Diffusion in GitHub
    • [cs.SI]Minimum entropy stochastic block models neglect edge distribution heterogeneity
    • [cs.SI]SGP: Spotting Groups Polluting the Online Political Discourse
    • [cs.SI]Uncritical polarized groups: The impact of spreading fake news as fact in social networks
    • [cs.SI]Understanding Social Networks using Transfer Learning
    • [econ.EM]A General Framework for Inference on Shape Restrictions
    • [eess.IV]A New Three-stage Curriculum Learning Approach to Deep Network Based Liver Tumor Segmentation
    • [eess.IV]A Parametric Perceptual Deficit Modeling and Diagnostics Framework for Retina Damage using Mixed Reality
    • [eess.IV]CFEA: Collaborative Feature Ensembling Adaptation for Domain Adaptation in Unsupervised Optic Disc and Cup Segmentation
    • [eess.IV]End-to-End Cascaded U-Nets with a Localization Network for Kidney Tumor Segmentation
    • [eess.IV]Introducing Hann windows for reducing edge-effects in patch-based image segmentation
    • [eess.IV]Organ At Risk Segmentation with Multiple Modality
    • [eess.SP]Analysis of Cooperative Hybrid ARQ with Adaptive Modulation and Coding on a Correlated Fading Channel
    • [eess.SP]Max-min Fairness of K-user Cooperative Rate-Splitting in MISO Broadcast Channel with User Relaying
    • [eess.SY]Robust Planning and Control For Polygonal Environments via Linear Programming
    • [math.AT]Path homologies of deep feedforward networks
    • [math.OC]Calculating Optimistic Likelihoods Using (Geodesically) Convex Optimization
    • [math.ST]Asymptotic Theory of $L$-Statistics and Integrable Empirical Processes
    • [math.ST]Consistency of the Buckley-Osthus model and the hierarchical preferential attachment model
    • [math.ST]Forecast Evaluation of Set-Valued Functionals
    • [math.ST]Hypothesis Test and Confidence Analysis with Wasserstein Distance on General Dimension
    • [math.ST]Multiscale Analysis of Bayesian CART
    • [math.ST]Nearly unstable family of stochastic processes given by stochastic differential equations with time delay
    • [physics.comp-ph]Computationally Efficient CFD Prediction of Bubbly Flow using Physics-Guided Deep Learning
    • [physics.comp-ph]Visualizing the world’s largest turbulence simulation
    • [physics.soc-ph]Cross-sectional Urban Scaling Fails in Predicting Temporal Growth of Cities
    • [stat.AP]Estimating Spatially-Smoothed Fiber Orientation Distribution
    • [stat.AP]Intelligent Surveillance of World Health Organization (WHO) Integrated Disease Surveillance and Response (IDSR) Data in Cameroon Using Multivariate Cross-Correlation
    • [stat.AP]Nonparametric tests for circular regression
    • [stat.AP]Selection of link function in binary regression: A case-study with world happiness report on immigration
    • [stat.ME]Bayesian analysis of multifidelity computer models with local features and non-nested experimental designs: Application to the WRF model
    • [stat.ME]Comment: Reflections on the Deconfounder
    • [stat.ME]Interim recruitment prediction for multi-centre clinical trials
    • [stat.ME]Ranking variables and interactions using predictive uncertainty measures
    • [stat.ME]Using Bayes Linear Emulators to Analyse Networks of Simulators
    • [stat.ML]A Unified Framework for Tuning Hyperparameters in Clustering Problems
    • [stat.ML]Achieving Robustness to Aleatoric Uncertainty with Heteroscedastic Bayesian Optimisation
    • [stat.ML]An Information-Theoretic Perspective on the Relationship Between Fairness and Accuracy
    • [stat.ML]Annealed Denoising Score Matching: Learning Energy-Based Models in High-Dimensional Spaces
    • [stat.ML]Dropping forward-backward algorithms for feature selection
    • [stat.ML]Notes on Lipschitz Margin, Lipschitz Margin Training, and Lipschitz Margin p-Values for Deep Neural Network Classifiers
    • [stat.ML]The Blessings of Multiple Causes: A Reply to Ogburn et al. (2019)
    • [stat.ML]The Rényi Gaussian Process
    • [stat.ML]Why bigger is not always better: on finite and infinite neural networks

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    • [astro-ph.CO]From Dark Matter to Galaxies with Convolutional Neural Networks
    Jacky H. T. Yip, Xinyue Zhang, Yanfang Wang, Wei Zhang, Yueqiu Sun, Gabriella Contardo, Francisco Villaescusa-Navarro, Siyu He, Shy Genel, Shirley Ho
    http://arxiv.org/abs/1910.07813v1

    • [astro-ph.IM]Speeding simulation analysis up with yt and Intel Distribution for Python
    Salvatore Cielo, Luigi Iapichino, Fabio Baruffa
    http://arxiv.org/abs/1910.07855v1

    • [cs.AI]Explainable AI for Intelligence Augmentation in Multi-Domain Operations
    Alun Preece, Dave Braines, Federico Cerutti, Tien Pham
    http://arxiv.org/abs/1910.07563v1

    • [cs.AI]Exploring Semi-Automatic Map Labeling
    Fabian Klute, Guangping Li, Raphael Löffler, Martin Nöllenburg, Manuela Schmidt
    http://arxiv.org/abs/1910.07799v1

    • [cs.AI]MAPEL: Multi-Agent Pursuer-Evader Learning using Situation Report
    Sagar Verma, Richa Verma, P. B. Sujit
    http://arxiv.org/abs/1910.07780v1

    • [cs.AI]Visual Hide and Seek
    Boyuan Chen, Shuran Song, Hod Lipson, Carl Vondrick
    http://arxiv.org/abs/1910.07882v1

    • [cs.CL]BIG MOOD: Relating Transformers to Explicit Commonsense Knowledge
    Jeff Da
    http://arxiv.org/abs/1910.07713v1

    • [cs.CL]Contextual Joint Factor Acoustic Embeddings
    Yanpei Shi, Qiang Huang, Thomas Hain
    http://arxiv.org/abs/1910.07601v1

    • [cs.CL]Cross-lingual Parsing with Polyglot Training and Multi-treebank Learning: A Faroese Case Study
    James Barry, Joachim Wagner, Jennifer Foster
    http://arxiv.org/abs/1910.07938v1

    • [cs.CL]H-VECTORS: Utterance-level Speaker Embedding Using A Hierarchical Attention Model
    Yanpei Shi, Qiang Huang, Thomas Hain
    http://arxiv.org/abs/1910.07900v1

    • [cs.CL]LibriVoxDeEn: A Corpus for German-to-English Speech Translation and Speech Recognition
    Benjamin Beilharz, Xin Sun, Sariya Karimova, Stefan Riezler
    http://arxiv.org/abs/1910.07924v1

    • [cs.CL]PLATO: Pre-trained Dialogue Generation Model with Discrete Latent Variable
    Siqi Bao, Huang He, Fan Wang, Hua Wu
    http://arxiv.org/abs/1910.07931v1

    • [cs.CL]Right-wing German Hate Speech on Twitter: Analysis and Automatic Detection
    Sylvia Jaki, Tom De Smedt
    http://arxiv.org/abs/1910.07518v1

    • [cs.CL]Topical Keyphrase Extraction with Hierarchical Semantic Networks
    Yoo yeon Sung, Seoung Bum Kim
    http://arxiv.org/abs/1910.07848v1

    • [cs.CL]Towards Annotating and Creating Sub-Sentence Summary Highlights
    Kristjan Arumae, Parminder Bhatia, Fei Liu
    http://arxiv.org/abs/1910.07659v1

    • [cs.CL]Universal Text Representation from BERT: An Empirical Study
    Xiaofei Ma, Peng Xu, Zhiguo Wang, Ramesh Nallapati, Bing Xiang
    http://arxiv.org/abs/1910.07973v1

    • [cs.CL]Using a KG-Copy Network for Non-Goal Oriented Dialogues
    Debanjan Chaudhuri, Md Rashad Al Hasan Rony, Simon Jordan, Jens Lehmann
    http://arxiv.org/abs/1910.07834v1

    • [cs.CR]Information-theoretic metrics for Local Differential Privacy protocols
    Milan Lopuhaä-Zwakenberg, Boris Škorić, Ninghui Li
    http://arxiv.org/abs/1910.07826v1

    • [cs.CR]Lateral Astroturfing Attacks on Twitter Trending Topics
    Tuğrulcan Elmas, Rebekah Overdorf, Ahmed Furkan Özkalay, Karl Aberer
    http://arxiv.org/abs/1910.07783v1

    • [cs.CR]Not Just Cloud Privacy: Protecting Client Privacy in Teacher-Student Learning
    Lichao Sun, Ji Wang, Philip S. Yu, Lifang He
    http://arxiv.org/abs/1910.08038v1

    • [cs.CV]A Combined Deep Learning-Gradient Boosting Machine Framework for Fluid Intelligence Prediction
    Yeeleng S. Vang, Yingxin Cao, Xiaohui Xie
    http://arxiv.org/abs/1910.07640v1

    • [cs.CV]Can I teach a robot to replicate a line art
    Raghav Brahmadesam Venkataramaiyer, Subham Kumar, Vinay P. Namboodiri
    http://arxiv.org/abs/1910.07860v1

    • [cs.CV]Context-Aware Saliency Detection for Image Retargeting Using Convolutional Neural Networks
    Mahdi Ahmadi, Nader Karimi, Shadrokh Samavi
    http://arxiv.org/abs/1910.08071v1

    • [cs.CV]Convolutional Character Networks
    Linjie Xing, Zhi Tian, Weilin Huang, Matthew R. Scott
    http://arxiv.org/abs/1910.07954v1

    • [cs.CV]Cross Attention Network for Few-shot Classification
    Ruibing Hou, Hong Chang, Bingpeng Ma, Shiguang Shan, Xilin Chen
    http://arxiv.org/abs/1910.07677v1

    • [cs.CV]Deep Contextual Attention for Human-Object Interaction Detection
    Tiancai Wang, Rao Muhammad Anwer, Muhammad Haris Khan, Fahad Shahbaz Khan, Yanwei Pang, Ling Shao, Jorma Laaksonen
    http://arxiv.org/abs/1910.07721v1

    • [cs.CV]Deep Semantic Segmentation of Natural and Medical Images: A Review
    Saed Asgari Taghanaki, Kumar Abhishek, Joseph Paul Cohen, Julien Cohen-Adad, Ghassan Hamarneh
    http://arxiv.org/abs/1910.07655v1

    • [cs.CV]DeepErase: Weakly Supervised Ink Artifact Removal in Document Text Images
    W. Ronny Huang, Yike Qi, Qianqian Li, Jonathan Degange
    http://arxiv.org/abs/1910.07070v2

    • [cs.CV]Detecting Urban Changes with Recurrent Neural Networks from Multitemporal Sentinel-2 Data
    Maria Papadomanolaki, Sagar Verma, Maria Vakalopoulou, Siddharth Gupta, Konstantinos Karantzalos
    http://arxiv.org/abs/1910.07778v1

    • [cs.CV]Discrete Residual Flow for Probabilistic Pedestrian Behavior Prediction
    Ajay Jain, Sergio Casas, Renjie Liao, Yuwen Xiong, Song Feng, Sean Segal, Raquel Urtasun
    http://arxiv.org/abs/1910.08041v1

    • [cs.CV]Global Saliency: Aggregating Saliency Maps to Assess Dataset Artefact Bias
    Jacob Pfau, Albert T. Young, Maria L. Wei, Michael J. Keiser
    http://arxiv.org/abs/1910.07604v1

    • [cs.CV]Go with the Flow: Perception-refined Physics Simulation
    Tom F. H. Runia, Kirill Gavrilyuk, Cees G. M. Snoek, Arnold W. M. Smeulders
    http://arxiv.org/abs/1910.07861v1

    • [cs.CV]Making Third Person Techniques Recognize First-Person Actions in Egocentric Videos
    Sagar Verma, Pravin Nagar, Divam Gupta, Chetan Arora
    http://arxiv.org/abs/1910.07766v1

    • [cs.CV]Meta-learning for fast classifier adaptation to new users of Signature Verification systems
    Luiz G. Hafemann, Robert Sabourin, Luiz S. Oliveira
    http://arxiv.org/abs/1910.08060v1

    • [cs.CV]NAMF: A Non-local Adaptive Mean Filter for Salt-and-Pepper Noise Removal
    Houwang Zhang, Chong Wu, Hanying Zheng, Le Zhang
    http://arxiv.org/abs/1910.07787v1

    • [cs.CV]On the Reliability of Cancelable Biometrics: Revisit the Irreversibility
    Xingbo Dong, Zhe Jin, Andrew Beng Jin Teoh, Massimo Tistarelli, KokSheik Wong
    http://arxiv.org/abs/1910.07770v1

    • [cs.CV]RGB-D Individual Segmentation
    Wenqiang Xu, Yanjun Fu, Yuchen Luo, Chang Liu, Cewu Lu
    http://arxiv.org/abs/1910.07641v1

    • [cs.CV]Video Person Re-Identification using Learned Clip Similarity Aggregation
    Neeraj Matiyali, Gaurav Sharma
    http://arxiv.org/abs/1910.08055v1

    • [cs.CY]Exploring the Role of Common Model of Cognition in Designing Adaptive Coaching Interactions for Health Behavior Change
    Shiwali Mohan
    http://arxiv.org/abs/1910.07728v1

    • [cs.CY]How to eliminate detour behaviors in E-hailing: On-line detection and Pricing regulation
    Qiong Tian, Yue Yang, Jiaqi Wen, Fan Ding, Jing He
    http://arxiv.org/abs/1910.06949v2

    • [cs.CY]Scaling up Psychology via Scientific Regret Minimization: A Case Study in Moral Decision-Making
    Mayank Agrawal, Joshua C. Peterson, Thomas L. Griffiths
    http://arxiv.org/abs/1910.07581v1

    • [cs.CY]The other side of the Coin: Risks of the Libra Blockchain
    Louis Abraham, Dominique Guégan
    http://arxiv.org/abs/1910.07775v1

    • [cs.DB]Service Wrapper: a system for converting web data into web services
    Naibo Wang, Zhiling Luo, Xiya Lyu, Zitong Yang, Jianwei Yin
    http://arxiv.org/abs/1910.07786v1

    • [cs.DC]A Tool for Automatically Suggesting Source-Code Optimizations for Complex GPU Kernels
    Saeed Taheri, Apan Qasem, Martin Burtscher
    http://arxiv.org/abs/1910.07776v1

    • [cs.DC]Adaptive Normalization in Streaming Data
    Vibhuti Gupta, Rattikorn Hewett
    http://arxiv.org/abs/1910.07696v1

    • [cs.DC]SNF: Serverless Network Functions
    Arjun Singhvi, Junaid Khalid, Aditya Akella, Sujata Banerjee
    http://arxiv.org/abs/1910.07700v1

    • [cs.DC]The NebulaStream Platform: Data and Application Management for the Internet of Things
    Steffen Zeuch, Ankit Chaudhary, Bonaventura Del Monte, Haralampos Gavriilidis, Dimitrios Giouroukis, Philipp M. Grulich, Sebastian Bress, Jonas Traub, Volker Markl
    http://arxiv.org/abs/1910.07867v1

    • [cs.DC]UMap: Enabling Application-driven Optimizations for Page Management
    Ivy B. Peng, Marty McFadden, Eric Green, Keita Iwabuchi, Kai Wu, Dong Li, Roger Pearce, Maya Gokhale
    http://arxiv.org/abs/1910.07566v1

    • [cs.DS]The Distributed Bloom Filter
    Lum Ramabaja, Arber Avdullahu
    http://arxiv.org/abs/1910.07782v1

    • [cs.IR]Cascading: Association Augmented Sequential Recommendation
    Xu Chen, Kenan Cui, Ya Zhang, Yanfeng Wang
    http://arxiv.org/abs/1910.07792v1

    • [cs.IR]Indoor Information Retrieval using Lifelog Data
    Deepanwita Datta
    http://arxiv.org/abs/1910.07784v1

    • [cs.IR]Keyphrase Extraction from Disaster-related Tweets
    Jishnu Ray Chowdhury, Cornelia Caragea, Doina Caragea
    http://arxiv.org/abs/1910.07897v1

    • [cs.IT]Few-weight codes over $\Bbb F_p+u\Bbb F_p$ associated with down sets and their distance optimal Gray image
    Yansheng Wu, Jong Yoon Hyun
    http://arxiv.org/abs/1910.07668v1

    • [cs.IT]Mixed Monotonic Programming for Fast Global Optimization
    Bho Matthiesen, Christoph Hellings, Eduard A. Jorswieck, Wolfgang Utschick
    http://arxiv.org/abs/1910.07853v1

    • [cs.IT]On the Kernel of $\Z_{2^s}$-Linear Simplex and MacDonald Codes
    Cristina Fernández-Córdoba, Carlos Vela, Mercè Villanueva
    http://arxiv.org/abs/1910.07911v1

    • [cs.IT]Stochastic Geometry-Based Analysis of Airborne Base Stations with Laser-powered UAVs
    Mohamed-Amine Lahmeri, Mustafa A. Kishk, Mohamed-Slim Alouini
    http://arxiv.org/abs/1910.07794v1

    • [cs.IT]The Role of Coded Side Information in Single-Server Private Information Retrieval
    Anoosheh Heidarzadeh, Fatemeh Kazemi, Alex Sprintson
    http://arxiv.org/abs/1910.07612v1

    • [cs.LG]A Double Residual Compression Algorithm for Efficient Distributed Learning
    Xiaorui Liu, Yao Li, Jiliang Tang, Ming Yan
    http://arxiv.org/abs/1910.07561v1

    • [cs.LG]A New Defense Against Adversarial Images: Turning a Weakness into a Strength
    Tao Yu, Shengyuan Hu, Chuan Guo, Wei-Lun Chao, Kilian Q. Weinberger
    http://arxiv.org/abs/1910.07629v1

    • [cs.LG]A Stochastic Variance Reduced Nesterov’s Accelerated Quasi-Newton Method
    Sota Yasuda, Shahrzad Mahboubi, S. Indrapriyadarsini, Hiroshi Ninomiya, Hideki Asai
    http://arxiv.org/abs/1910.07939v1

    • [cs.LG]A Survey of Deep Learning Techniques for Autonomous Driving
    Sorin Grigorescu, Bogdan Trasnea, Tiberiu Cocias, Gigel Macesanu
    http://arxiv.org/abs/1910.07738v1

    • [cs.LG]Active Learning for Graph Neural Networks via Node Feature Propagation
    Yuexin Wu, Yichong Xu, Aarti Singh, Yiming Yang, Artur Dubrawski
    http://arxiv.org/abs/1910.07567v1

    • [cs.LG]Adaptive Transfer Learning of Multi-View Time Series Classification
    Donglin Zhan, Shiyu Yi, Dongli Xu, Xiao Yu, Denglin Jiang, Siqi Yu, Haoting Zhang, Wenfang Shangguan, Weihua Zhang
    http://arxiv.org/abs/1910.07632v1

    • [cs.LG]An Exponential Learning Rate Schedule for Deep Learning
    Zhiyuan Li, Sanjeev Arora
    http://arxiv.org/abs/1910.07454v2

    • [cs.LG]Autoregressive Models: What Are They Good For?
    Murtaza Dalal, Alexander C. Li, Rohan Taori
    http://arxiv.org/abs/1910.07737v1

    • [cs.LG]Collaborative Filtering with Label Consistent Restricted Boltzmann Machine
    Sagar Verma, Prince Patel, Angshul Majumdar
    http://arxiv.org/abs/1910.07724v1

    • [cs.LG]Communication-Efficient Asynchronous Stochastic Frank-Wolfe over Nuclear-norm Balls
    Jiacheng Zhuo, Qi Lei, Alexandros G. Dimakis, Constantine Caramanis
    http://arxiv.org/abs/1910.07703v1

    • [cs.LG]Deep clustering with concrete k-means
    Boyan Gao, Yongxin Yang, Henry Gouk, Timothy M. Hospedales
    http://arxiv.org/abs/1910.08031v1

    • [cs.LG]Design, Benchmarking and Explainability Analysis of a Game-Theoretic Framework towards Energy Efficiency in Smart Infrastructure
    Ioannis C. Konstantakopoulos, Hari Prasanna Das, Andrew R. Barkan, Shiying He, Tanya Veeravalli, Huihan Liu, Aummul Baneen Manasawala, Yu-Wen Lin, Costas J. Spanos
    http://arxiv.org/abs/1910.07899v1

    • [cs.LG]Dynamic Local Regret for Non-convex Online Forecasting
    Sergul Aydore, Tianhao Zhu, Dean Foster
    http://arxiv.org/abs/1910.07927v1

    • [cs.LG]Effect of Superpixel Aggregation on Explanations in LIME — A Case Study with Biological Data
    Ludwig Schallner, Johannes Rabold, Oliver Scholz, Ute Schmid
    http://arxiv.org/abs/1910.07856v1

    • [cs.LG]Generalized Clustering by Learning to Optimize Expected Normalized Cuts
    Azade Nazi, Will Hang, Anna Goldie, Sujith Ravi, Azalia Mirhoseini
    http://arxiv.org/abs/1910.07623v1

    • [cs.LG]Graph Embedding VAE: A Permutation Invariant Model of Graph Structure
    Tony Duan, Juho Lee
    http://arxiv.org/abs/1910.08057v1

    • [cs.LG]Instance adaptive adversarial training: Improved accuracy tradeoffs in neural nets
    Yogesh Balaji, Tom Goldstein, Judy Hoffman
    http://arxiv.org/abs/1910.08051v1

    • [cs.LG]KDE sampling for imbalanced class distribution
    Firuz Kamalov
    http://arxiv.org/abs/1910.07842v1

    • [cs.LG]Learning chordal extensions
    Defeng Liu, Andrea Lodi, Mathieu Tanneau
    http://arxiv.org/abs/1910.07600v1

    • [cs.LG]Mixture-of-Experts Variational Autoencoder for clustering and generating from similarity-based representations
    Andreas Kopf, Vincent Fortuin, Vignesh Ram Somnath, Manfred Claassen
    http://arxiv.org/abs/1910.07763v1

    • [cs.LG]On Concept-Based Explanations in Deep Neural Networks
    Chih-Kuan Yeh, Been Kim, Sercan O. Arik, Chun-Liang Li, Pradeep Ravikumar, Tomas Pfister
    http://arxiv.org/abs/1910.07969v1

    • [cs.LG]Optimal Transport Based Generative Autoencoders
    Oliver Zhang, Ruei-Sung Lin, Yuchuan Gou
    http://arxiv.org/abs/1910.07636v1

    • [cs.LG]Overcoming Forgetting in Federated Learning on Non-IID Data
    Neta Shoham, Tomer Avidor, Aviv Keren, Nadav Israel, Daniel Benditkis, Liron Mor-Yosef, Itai Zeitak
    http://arxiv.org/abs/1910.07796v1

    • [cs.LG]Predicting retrosynthetic pathways using a combined linguistic model and hyper-graph exploration strategy
    Philippe Schwaller, Riccardo Petraglia, Valerio Zullo, Vishnu H Nair, Rico Andreas Haeuselmann, Riccardo Pisoni, Costas Bekas, Anna Iuliano, Teodoro Laino
    http://arxiv.org/abs/1910.08036v1

    • [cs.LG]Probabilistic Deterministic Finite Automata and Recurrent Networks, Revisited
    S. E. Marzen, J. P. Crutchfield
    http://arxiv.org/abs/1910.07663v1

    • [cs.LG]Reducing the Computational Complexity of Pseudoinverse for the Incremental Broad Learning System on Added Inputs
    Hufei Zhu, Chenghao Wei
    http://arxiv.org/abs/1910.07755v1

    • [cs.LG]Reinforcement Learning for Robotic Manipulation using Simulated Locomotion Demonstrations
    Ozsel Kilinc, Yang Hu, Giovanni Montana
    http://arxiv.org/abs/1910.07294v2

    • [cs.LG]Sharper bounds for uniformly stable algorithms
    Olivier Bousquet, Yegor Klochkov, Nikita Zhivotovskiy
    http://arxiv.org/abs/1910.07833v1

    • [cs.LG]Single Episode Policy Transfer in Reinforcement Learning
    Jiachen Yang, Brenden Petersen, Hongyuan Zha, Daniel Faissol
    http://arxiv.org/abs/1910.07719v1

    • [cs.LG]Teaching Vehicles to Anticipate: A Systematic Study on Probabilistic Behavior Prediction using Large Data Sets
    Florian Wirthmüller, Julian Schlechtriemen, Jochen Hipp, Manfred Reichert
    http://arxiv.org/abs/1910.07772v1

    • [cs.LG]Tensor Graph Convolutional Networks for Prediction on Dynamic Graphs
    Osman Asif Malik, Shashanka Ubaru, Lior Horesh, Misha E. Kilmer, Haim Avron
    http://arxiv.org/abs/1910.07643v1

    • [cs.LG]Toward Subject Invariant and Class Disentangled Representation in BCI via Cross-Domain Mutual Information Estimator
    Eunjin Jeon, Wonjun Ko, Jee Seok Yoon, Heung-Il Suk
    http://arxiv.org/abs/1910.07747v1

    • [cs.LG]Towards a Precipitation Bias Corrector against Noise and Maldistribution
    Xiaoyang Xu, Yiqun Liu, Hanqing Chao, Youcheng Luo, Hai Chu, Lei Chen, Junping Zhang, Leiming Ma
    http://arxiv.org/abs/1910.07633v1

    • [cs.LG]WOTBoost: Weighted Oversampling Technique in Boosting for imbalanced learning
    Wenhao Zhang, Ramin Ramezani, Arash Naeim
    http://arxiv.org/abs/1910.07892v1

    • [cs.LG]ZO-AdaMM: Zeroth-Order Adaptive Momentum Method for Black-Box Optimization
    Xiangyi Chen, Sijia Liu, Kaidi Xu, Xingguo Li, Xue Lin, Mingyi Hong, David Cox
    http://arxiv.org/abs/1910.06513v2

    • [cs.NE]Parameter Optimization and Learning in a Spiking Neural Network for UAV Obstacle Avoidance targeting Neuromorphic Processors
    Llewyn Salt, David Howard, Giacomo Indiveri, Yulia Sandamirskaya
    http://arxiv.org/abs/1910.07960v1

    • [cs.RO]Adaptive Curriculum Generation from Demonstrations for Sim-to-Real Visuomotor Control
    Lukas Hermann, Max Argus, Andreas Eitel, Artemij Amiranashvili, Wolfram Burgard, Thomas Brox
    http://arxiv.org/abs/1910.07972v1

    • [cs.RO]Conditional Driving from Natural Language Instructions
    Junha Roh, Chris Paxton, Andrzej Pronobis, Ali Farhadi, Dieter Fox
    http://arxiv.org/abs/1910.07615v1

    • [cs.RO]Learning from My Partner’s Actions: Roles in Decentralized Robot Teams
    Dylan P. Losey, Mengxi Li, Jeannette Bohg, Dorsa Sadigh
    http://arxiv.org/abs/1910.07613v1

    • [cs.RO]Self-supervised 3D Shape and Viewpoint Estimation from Single Images for Robotics
    Oier Mees, Maxim Tatarchenko, Thomas Brox, Wolfram Burgard
    http://arxiv.org/abs/1910.07948v1

    • [cs.SI]Beyond Fortune 500: Women in a Global Network of Directors
    Anna Evtushenko, Michael T. Gastner
    http://arxiv.org/abs/1910.07441v2

    • [cs.SI]Community Detection in Multiplex Networks
    Obaida Hanteer, Roberto Interdonato, Matteo Magnani, Andrea Tagarelli, Luca Rossi
    http://arxiv.org/abs/1910.07646v1

    • [cs.SI]DeepFork: Supervised Prediction of Information Diffusion in GitHub
    Ramya Akula, Niloofar Yousefi, Ivan Garibay
    http://arxiv.org/abs/1910.07999v1

    • [cs.SI]Minimum entropy stochastic block models neglect edge distribution heterogeneity
    Louis Duvivier, Rémy Cazabet, Céline Robardet
    http://arxiv.org/abs/1910.07879v1

    • [cs.SI]SGP: Spotting Groups Polluting the Online Political Discourse
    Junhao Wang, Sacha Levy, Ren Wang, Aayushi Kulshrestha, Reihaneh Rabbany
    http://arxiv.org/abs/1910.07130v2

    • [cs.SI]Uncritical polarized groups: The impact of spreading fake news as fact in social networks
    Jesus San Martin, Fatima Drubi, Daniel Rodriguez-Perez
    http://arxiv.org/abs/1910.08010v1

    • [cs.SI]Understanding Social Networks using Transfer Learning
    Jun Sun, Steffen Staab, Jérôme Kunegis
    http://arxiv.org/abs/1910.07918v1

    • [econ.EM]A General Framework for Inference on Shape Restrictions
    Zheng Fang, Juwon Seo
    http://arxiv.org/abs/1910.07689v1

    • [eess.IV]A New Three-stage Curriculum Learning Approach to Deep Network Based Liver Tumor Segmentation
    Huiyu Li, Xiabi Liu, Said Boumaraf, Weihua Liu, Xiaopeng Gong, Xiaohong Ma
    http://arxiv.org/abs/1910.07895v1

    • [eess.IV]A Parametric Perceptual Deficit Modeling and Diagnostics Framework for Retina Damage using Mixed Reality
    Prithul Aniruddha, Nasif Zaman, Alireza Tavakkoli, Stewart Zuckerbrod
    http://arxiv.org/abs/1910.07688v1

    • [eess.IV]CFEA: Collaborative Feature Ensembling Adaptation for Domain Adaptation in Unsupervised Optic Disc and Cup Segmentation
    Peng Liu, Bin Kong, Zhongyu Li, Shaoting Zhang, Ruogu Fang
    http://arxiv.org/abs/1910.07638v1

    • [eess.IV]End-to-End Cascaded U-Nets with a Localization Network for Kidney Tumor Segmentation
    Minh H. Vu, Guus Grimbergen, Attila Simkó, Tufve Nyholm, Tommy Löfstedt
    http://arxiv.org/abs/1910.07521v1

    • [eess.IV]Introducing Hann windows for reducing edge-effects in patch-based image segmentation
    Nicolas Pielawski, Carolina Wählby
    http://arxiv.org/abs/1910.07831v1

    • [eess.IV]Organ At Risk Segmentation with Multiple Modality
    Kuan-Lun Tseng, Winston Hsu, Chun-ting Wu, Ya-Fang Shih, Fan-Yun Sun
    http://arxiv.org/abs/1910.07800v1

    • [eess.SP]Analysis of Cooperative Hybrid ARQ with Adaptive Modulation and Coding on a Correlated Fading Channel
    Ibrahim Ozkan
    http://arxiv.org/abs/1910.07959v1

    • [eess.SP]Max-min Fairness of K-user Cooperative Rate-Splitting in MISO Broadcast Channel with User Relaying
    Yijie Mao, Bruno Clerckx, Jian Zhang, Victor O. K. Li, Mohammed Arafah
    http://arxiv.org/abs/1910.07843v1

    • [eess.SY]Robust Planning and Control For Polygonal Environments via Linear Programming
    Mahroo Bahreinian, Erfan Aasi, Roberto Tron
    http://arxiv.org/abs/1910.07976v1

    • [math.AT]Path homologies of deep feedforward networks
    Samir Chowdhury, Thomas Gebhart, Steve Huntsman, Matvey Yutin
    http://arxiv.org/abs/1910.07617v1

    • [math.OC]Calculating Optimistic Likelihoods Using (Geodesically) Convex Optimization
    Viet Anh Nguyen, Soroosh Shafieezadeh-Abadeh, Man-Chung Yue, Daniel Kuhn, Wolfram Wiesemann
    http://arxiv.org/abs/1910.07817v1

    • [math.ST]Asymptotic Theory of $L$-Statistics and Integrable Empirical Processes
    Tetsuya Kaji
    http://arxiv.org/abs/1910.07572v1

    • [math.ST]Consistency of the Buckley-Osthus model and the hierarchical preferential attachment model
    Xin Guo, Fengmin Tang, Wenpin Tang
    http://arxiv.org/abs/1910.07698v1

    • [math.ST]Forecast Evaluation of Set-Valued Functionals
    Tobias Fissler, Jana Hlavinová, Birgit Rudloff
    http://arxiv.org/abs/1910.07912v1

    • [math.ST]Hypothesis Test and Confidence Analysis with Wasserstein Distance on General Dimension
    Masaaki Imaizumi, Hirofumi Ota, Takuo Hamaguchi
    http://arxiv.org/abs/1910.07773v1

    • [math.ST]Multiscale Analysis of Bayesian CART
    Ismael Castillo, Veronika Rockova
    http://arxiv.org/abs/1910.07635v1

    • [math.ST]Nearly unstable family of stochastic processes given by stochastic differential equations with time delay
    János Marcell Benke, Gyula Pap
    http://arxiv.org/abs/1910.07816v1

    • [physics.comp-ph]Computationally Efficient CFD Prediction of Bubbly Flow using Physics-Guided Deep Learning
    Han Bao, Jinyong Feng, Nam Dinh, Hongbin Zhang
    http://arxiv.org/abs/1910.08037v1

    • [physics.comp-ph]Visualizing the world’s largest turbulence simulation
    Salvatore Cielo, Luigi Iapichino, Johannes Günther, Christoph Federrath, Elisabeth Mayer, Markus Wiedemann
    http://arxiv.org/abs/1910.07850v1

    • [physics.soc-ph]Cross-sectional Urban Scaling Fails in Predicting Temporal Growth of Cities
    Gang Xu, Zhengzi Zhou, Limin Jiao, Ting Dong, Ruiqi Li
    http://arxiv.org/abs/1910.06732v2

    • [stat.AP]Estimating Spatially-Smoothed Fiber Orientation Distribution
    Jilei Yang, Jie Peng
    http://arxiv.org/abs/1910.07712v1

    • [stat.AP]Intelligent Surveillance of World Health Organization (WHO) Integrated Disease Surveillance and Response (IDSR) Data in Cameroon Using Multivariate Cross-Correlation
    Jianzhi Liu, Ziming Yang, Jesse E. Engelberg, Frankline S. Nsai, Serge Bataliack, Vikash Singh
    http://arxiv.org/abs/1910.07741v1

    • [stat.AP]Nonparametric tests for circular regression
    María Alonso-Pena, Jose Ameijeiras-Alonso, Rosa M. Crujeiras
    http://arxiv.org/abs/1910.07825v1

    • [stat.AP]Selection of link function in binary regression: A case-study with world happiness report on immigration
    Ardhendu Banerjee, Subrata Chakraborty, Aniket Biswas
    http://arxiv.org/abs/1910.07748v1

    • [stat.ME]Bayesian analysis of multifidelity computer models with local features and non-nested experimental designs: Application to the WRF model
    Bledar A. Konomi, Georgios Karagiannis
    http://arxiv.org/abs/1910.08063v1

    • [stat.ME]Comment: Reflections on the Deconfounder
    Alexander D’Amour
    http://arxiv.org/abs/1910.08042v1

    • [stat.ME]Interim recruitment prediction for multi-centre clinical trials
    Szymon Urbas, Chris Sherlock, Paul Metcalfe
    http://arxiv.org/abs/1910.07965v1

    • [stat.ME]Ranking variables and interactions using predictive uncertainty measures
    Topi Paananen, Michael Riis Andersen, Aki Vehtari
    http://arxiv.org/abs/1910.07942v1

    • [stat.ME]Using Bayes Linear Emulators to Analyse Networks of Simulators
    Samuel E. Jackson, David C. Woods
    http://arxiv.org/abs/1910.08003v1

    • [stat.ML]A Unified Framework for Tuning Hyperparameters in Clustering Problems
    Xinjie Fan, Yuguang Yue, Purnamrita Sarkar, Y. X. Rachel Wang
    http://arxiv.org/abs/1910.08018v1

    • [stat.ML]Achieving Robustness to Aleatoric Uncertainty with Heteroscedastic Bayesian Optimisation
    Ryan-Rhys Griffiths, Miguel Garcia-Ortegon, Alexander A. Aldrick, Alpha A. Lee
    http://arxiv.org/abs/1910.07779v1

    • [stat.ML]An Information-Theoretic Perspective on the Relationship Between Fairness and Accuracy
    Sanghamitra Dutta, Dennis Wei, Hazar Yueksel, Pin-Yu Chen, Sijia Liu, Kush R. Varshney
    http://arxiv.org/abs/1910.07870v1

    • [stat.ML]Annealed Denoising Score Matching: Learning Energy-Based Models in High-Dimensional Spaces
    Zengyi Li, Yubei Chen, Friedrich T. Sommer
    http://arxiv.org/abs/1910.07762v1

    • [stat.ML]Dropping forward-backward algorithms for feature selection
    Thu Nguyen
    http://arxiv.org/abs/1910.08007v1

    • [stat.ML]Notes on Lipschitz Margin, Lipschitz Margin Training, and Lipschitz Margin p-Values for Deep Neural Network Classifiers
    George Kesidis, David J. Miller
    http://arxiv.org/abs/1910.08032v1

    • [stat.ML]The Blessings of Multiple Causes: A Reply to Ogburn et al. (2019)
    Yixin Wang, David M. Blei
    http://arxiv.org/abs/1910.07320v2

    • [stat.ML]The Rényi Gaussian Process
    Xubo Yue, Raed Kontar
    http://arxiv.org/abs/1910.06990v2

    • [stat.ML]Why bigger is not always better: on finite and infinite neural networks
    Laurence Aitchison
    http://arxiv.org/abs/1910.08013v1