astro-ph.CO - 宇宙学和天体物理学

    cs.AI - 人工智能 cs.CG - 计算几何学 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.GR - 计算机图形学 cs.GT - 计算机科学与博弈论 cs.IR - 信息检索 cs.IT - 信息论 cs.LG - 自动学习 cs.LO - 计算逻辑 cs.MA - 多代理系统 cs.NE - 神经与进化计算 cs.PL - 编程语言 cs.RO - 机器人学 cs.SI - 社交网络与信息网络 eess.IV - 图像与视频处理 eess.SP - 信号处理 math.ST - 统计理论 physics.ao-ph - 大气和海洋物理 physics.comp-ph - 计算物理学 physics.data-an - 数据分析、 统计和概率 physics.soc-ph - 物理学与社会 q-fin.ST - 统计金融学 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.CO]Gaussbock: Fast parallel-iterative cosmological parameter estimation with Bayesian nonparametrics
    • [cs.AI]Automatic Generation of Level Maps with the Do What’s Possible Representation
    • [cs.AI]Deep Q-Learning with Q-Matrix Transfer Learning for Novel Fire Evacuation Environment
    • [cs.AI]ENIGMAWatch: ProofWatch Meets ENIGMA
    • [cs.AI]Knowledge Graph Embedding Bi-Vector Models for Symmetric Relation
    • [cs.AI]Minimizing the Negative Side Effects of Planning with Reduced Models
    • [cs.AI]On modelling the emergence of logical thinking
    • [cs.AI]The African Wildlife Ontology tutorial ontologies: requirements, design, and content
    • [cs.CG]Automated Process Planning for Turning: A Feature-Free Approach
    • [cs.CL]An Investigation of Transfer Learning-Based Sentiment Analysis in Japanese
    • [cs.CL]Analyzing Multi-Head Self-Attention: Specialized Heads Do the Heavy Lifting, the Rest Can Be Pruned
    • [cs.CL]FastSpeech: Fast, Robust and Controllable Text to Speech
    • [cs.CL]GWU NLP Lab at SemEval-2019 Task 3: EmoContext: Effective Contextual Information in Models for Emotion Detection in Sentence-level in a Multigenre Corpus
    • [cs.CL]MCScript2.0: A Machine Comprehension Corpus Focused on Script Events and Participants
    • [cs.CL]Misspelling Oblivious Word Embeddings
    • [cs.CL]Multi-hop Reading Comprehension via Deep Reinforcement Learning based Document Traversal
    • [cs.CR]Detecting Malicious PowerShell Scripts Using Contextual Embeddings
    • [cs.CR]Elliptical Perturbations for Differential Privacy
    • [cs.CR]KNG: The K-Norm Gradient Mechanism
    • [cs.CR]Privacy-Preserving Obfuscation of Critical Infrastructure Networks
    • [cs.CR]StrongChain: Transparent and Collaborative Proof-of-Work Consensus
    • [cs.CV]A Convolutional Cost-Sensitive Crack Localization Algorithm for Automated and Reliable RC Bridge Inspection
    • [cs.CV]A Direct Approach to Robust Deep Learning Using Adversarial Networks
    • [cs.CV]AttentionRNN: A Structured Spatial Attention Mechanism
    • [cs.CV]Automating Whole Brain Histology to MRI Registration: Implementation of a Computational Pipeline
    • [cs.CV]Constrained Design of Deep Iris Networks
    • [cs.CV]Depth Estimation on Underwater Omni-directional Images Using a Deep Neural Network
    • [cs.CV]Image Fusion via Sparse Regularization with Non-Convex Penalties
    • [cs.CV]Network Pruning via Transformable Architecture Search
    • [cs.CV]Pose estimator and tracker using temporal flow maps for limbs
    • [cs.CV]PoseRBPF: A Rao-Blackwellized Particle Filter for 6D Object Pose Tracking
    • [cs.CV]Prototype Reminding for Continual Learning
    • [cs.CV]Robust Point Cloud Based Reconstruction of Large-Scale Outdoor Scenes
    • [cs.CV]Spatial Group-wise Enhance: Improving Semantic Feature Learning in Convolutional Networks
    • [cs.CV]Speech2Face: Learning the Face Behind a Voice
    • [cs.CV]Watermark retrieval from 3D printed objects via synthetic data training
    • [cs.CY]Digital Normativity: A challenge for human subjectivization and free will
    • [cs.CY]The tradeoff between the utility and risk of location data and implications for public good
    • [cs.DB]COBS: a Compact Bit-Sliced Signature Index
    • [cs.DB]Towards Global Asset Management in Blockchain Systems
    • [cs.DC]Cross-chain Deals and Adversarial Commerce
    • [cs.DC]KPynq: A Work-Efficient Triangle-Inequality based K-means on FPGA
    • [cs.DC]Kaleido: An Efficient Out-of-core Graph Mining System on A Single Machine
    • [cs.DC]Positional Encoding by Robots with Non-Rigid Movements
    • [cs.DC]Topological Characterization of Consensus under General Message Adversaries
    • [cs.DC]Workflow Design Analysis for High Resolution Satellite Image Analysis
    • [cs.DS]An Efficient Approach for Super and Nested Term Indexing and Retrieval
    • [cs.GR]Data-Driven Crowd Simulation with Generative Adversarial Networks
    • [cs.GT]Diffusion and Auction on Graphs
    • [cs.IR]Practice on Long Sequential User Behavior Modeling for Click-Through Rate Prediction
    • [cs.IT]A Simple Receive Diversity Technique for Distributed Beamforming
    • [cs.IT]Downlink Non-Orthogonal Multiple Access without SIC for Block Fading Channels: An Algebraic Rotation Approach
    • [cs.IT]Johnson-Lindenstrauss Property Implies Subspace Restricted Isometry Property
    • [cs.IT]Optimum Low-Complexity Decoder for Spatial Modulation
    • [cs.IT]Rate-Distortion-Memory Trade-offs in Heterogeneous Caching Networks
    • [cs.IT]Simple Bounds for the Symmetric Capacity of the Rayleigh Fading Multiple Access Channel
    • [cs.LG]Accelerating DNN Training in Wireless Federated Edge Learning System
    • [cs.LG]Action Assembly: Sparse Imitation Learning for Text Based Games with Combinatorial Action Spaces
    • [cs.LG]Adversarially Robust Distillation
    • [cs.LG]Augmenting correlation structures in spatial data using deep generative models
    • [cs.LG]Average reward reinforcement learning with unknown mixing times
    • [cs.LG]Binary Classification with Bounded Abstention Rate
    • [cs.LG]Causal Discovery with Cascade Nonlinear Additive Noise Models
    • [cs.LG]Cognitive Model Priors for Predicting Human Decisions
    • [cs.LG]Combination of linear classifiers using score function — analysis of possible combination strategies
    • [cs.LG]Combine PPO with NES to Improve Exploration
    • [cs.LG]Compression with Flows via Local Bits-Back Coding
    • [cs.LG]Convergence Analyses of Online ADAM Algorithm in Convex Setting and Two-Layer ReLU Neural Network
    • [cs.LG]DEEP-BO for Hyperparameter Optimization of Deep Networks
    • [cs.LG]Decentralized Learning of Generative Adversarial Networks from Multi-Client Non-iid Data
    • [cs.LG]Disentangling Redundancy for Multi-Task Pruning
    • [cs.LG]Ensemble Model Patching: A Parameter-Efficient Variational Bayesian Neural Network
    • [cs.LG]Estimating Risk and Uncertainty in Deep Reinforcement Learning
    • [cs.LG]FiBiNET: Combining Feature Importance and Bilinear feature Interaction for Click-Through Rate Prediction
    • [cs.LG]Fire Now, Fire Later: Alarm-Based Systems for Prescriptive Process Monitoring
    • [cs.LG]From semantics to execution: Integrating action planning with reinforcement learning for robotic tool use
    • [cs.LG]Fully Neural Network based Model for General Temporal Point Processes
    • [cs.LG]Generative Imputation and Stochastic Prediction
    • [cs.LG]Geometric Laplacian Eigenmap Embedding
    • [cs.LG]Glioma Grade Predictions using Scattering Wavelet Transform-Based Radiomics
    • [cs.LG]Gravity-Inspired Graph Autoencoders for Directed Link Prediction
    • [cs.LG]Hierarchical Annotation of Images with Two-Alternative-Forced-Choice Metric Learning
    • [cs.LG]How degenerate is the parametrization of neural networks with the ReLU activation function?
    • [cs.LG]Imitation Learning from Video by Leveraging Proprioception
    • [cs.LG]Interpreting Adversarially Trained Convolutional Neural Networks
    • [cs.LG]Inverse Reinforcement Learning in Contextual MDPs
    • [cs.LG]Kernel Wasserstein Distance
    • [cs.LG]Learning Discrete and Continuous Factors of Data via Alternating Disentanglement
    • [cs.LG]Leveraging Uncertainty in Deep Learning for Selective Classification
    • [cs.LG]MATCHA: Speeding Up Decentralized SGD via Matching Decomposition Sampling
    • [cs.LG]MCP: Learning Composable Hierarchical Control with Multiplicative Compositional Policies
    • [cs.LG]MR-GNN: Multi-Resolution and Dual Graph Neural Network for Predicting Structured Entity Interactions
    • [cs.LG]Meta-GNN: On Few-shot Node Classification in Graph Meta-learning
    • [cs.LG]Multi-Service Mobile Traffic Forecasting via Convolutional Long Short-Term Memories
    • [cs.LG]Multi-relational Poincaré Graph Embeddings
    • [cs.LG]Multinomial Distribution Learning for Effective Neural Architecture Search
    • [cs.LG]New methods for SVM feature selection
    • [cs.LG]Non-monotone DR-submodular Maximization: Approximation and Regret Guarantees
    • [cs.LG]Outlier Robust Extreme Learning Machine for Multi-Target Regression
    • [cs.LG]Parsimonious Deep Learning: A Differential Inclusion Approach with Global Convergence
    • [cs.LG]Quantifying Long Range Dependence in Language and User Behavior to improve RNNs
    • [cs.LG]Recurrent Value Functions
    • [cs.LG]Some limitations of norm based generalization bounds in deep neural networks
    • [cs.LG]The Convolutional Tsetlin Machine
    • [cs.LG]The Journey is the Reward: Unsupervised Learning of Influential Trajectories
    • [cs.LG]Tucker Decomposition Network: Expressive Power and Comparison
    • [cs.LG]Zero-shot Knowledge Transfer via Adversarial Belief Matching
    • [cs.LO]Learning to Prove Theorems via Interacting with Proof Assistants
    • [cs.MA]Nature-Inspired Computational Model of Population Desegregation under Group Leaders Influence
    • [cs.NE]CUDA-Self-Organizing feature map based visual sentiment analysis of bank customer complaints for Analytical CRM
    • [cs.NE]Comparing and Combining Lexicase Selection and Novelty Search
    • [cs.NE]Effect of shapes of activation functions on predictability in the echo state network
    • [cs.NE]Improving Neural Networks by Adopting Amplifying and Attenuating Neurons
    • [cs.NE]Lexicase Selection of Specialists
    • [cs.NE]Multi-Sample Dropout for Accelerated Training and Better Generalization
    • [cs.PL]Hypothetical answers to continuous queries over data streams
    • [cs.RO]A ROS2 based communication architecture for control in collaborative and intelligent automation systems
    • [cs.RO]Deep Drone Racing: From Simulation to Reality with Domain Randomization
    • [cs.RO]Hierarchical Reinforcement Learning for Concurrent Discovery of Compound and Composable Policies
    • [cs.RO]IN2LAAMA: INertial Lidar Localisation Autocalibration And MApping
    • [cs.RO]Incorporating Human Domain Knowledge in 3D LiDAR-based Semantic Segmentation
    • [cs.RO]Nullspace Structure in Model Predictive Control
    • [cs.RO]Predictive Control for Chasing a Ground Vehicle using a UAV
    • [cs.RO]Reachable Space Characterization of Markov Decision Processes with Time Variability
    • [cs.RO]Teleoperator Imitation with Continuous-time Safety
    • [cs.RO]Towards Generation and Evaluation of Comprehensive Mapping Robot Datasets
    • [cs.RO]Towards Safety-Aware Computing System Design in Autonomous Vehicles
    • [cs.RO]Variational Inference with Mixture Model Approximation: Robotic Applications
    • [cs.SI]Network Density of States
    • [cs.SI]Price of Dependence: Stochastic Submodular Maximization with Dependent Items
    • [eess.IV]Fusion of heterogeneous bands and kernels in hyperspectral image processing
    • [eess.IV]Learning Fast Magnetic Resonance Imaging
    • [eess.IV]Self-supervised learning of inverse problem solvers in medical imaging
    • [eess.IV]Spatio-Temporal Deep Learning Models for Tip Force Estimation During Needle Insertion
    • [eess.IV]Underwater Stereo using Refraction-free Image Synthesized from Light Field Camera
    • [eess.SP]A Comparative Study of Analog/Digital Self-Interference Cancellation for Full Duplex Radios
    • [eess.SP]One-bit LFMCW Radar: Spectrum Analysis and Target Detection
    • [math.ST]On generalized Piterbarg-Berman function
    • [math.ST]Sparse Equisigned PCA: Algorithms and Performance Bounds in the Noisy Rank-1 Setting
    • [math.ST]Sparse Minimax Optimality of Bayes Predictive Density Estimates from Clustered Discrete Priors
    • [physics.ao-ph]Learning the Representations of Moist Convection with Convolutional Neural Networks
    • [physics.comp-ph]The Stabilized Explicit Variable-Load Solver with Machine Learning Acceleration for the Rapid Solution of Stiff Chemical Kinetics
    • [physics.data-an]A Predictive Model for Steady-State Multiphase Pipe Flow: Machine Learning on Lab Data
    • [physics.data-an]Shades of Dark Uncertainty and Consensus Value for the Newtonian Constant of Gravitation
    • [physics.soc-ph]Applied hybrid binary mixed logit to investigate pedestrian crossing safety at midblock and unsignalized intersection
    • [physics.soc-ph]Scale-free networks revealed from finite-size scaling
    • [physics.soc-ph]What is the Entropy of a Social Organization?
    • [q-fin.ST]Detection of Chinese Stock Market Bubbles with LPPLS Confidence Indicator
    • [q-fin.ST]Diagnosis and Prediction of the 2015 Chinese Stock Market Bubble
    • [q-fin.ST]Real-time Prediction of Bitcoin bubble Crashes
    • [stat.AP]Targeted Smooth Bayesian Causal Forests: An analysis of heterogeneous treatment effects for simultaneous versus interval medical abortion regimens over gestation
    • [stat.AP]semopy: A Python package for Structural Equation Modeling
    • [stat.CO]A Condition Number for Hamiltonian Monte Carlo
    • [stat.ME]Atlantic Causal Inference Conference (ACIC) Data Analysis Challenge 2017
    • [stat.ME]Learning When-to-Treat Policies
    • [stat.ME]Random Norming Aids Analysis of Non-linear Regression Models with Sequential Informative Dose Selection
    • [stat.ME]Restricted Spatial Regression Methods: Implications for Inference
    • [stat.ME]Slamming the sham: A Bayesian model for adaptive adjustment with noisy control data
    • [stat.ML]An Optimal Private Stochastic-MAB Algorithm Based on an Optimal Private Stopping Rule
    • [stat.ML]Bayesian Optimization over Sets
    • [stat.ML]Multi-Class Gaussian Process Classification Made Conjugate: Efficient Inference via Data Augmentation
    • [stat.ML]Population-based Global Optimisation Methods for Learning Long-term Dependencies with RNNs
    • [stat.ML]Replicated Vector Approximate Message Passing For Resampling Problem
    • [stat.ML]Revisiting Graph Neural Networks: All We Have is Low-Pass Filters

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

    • [astro-ph.CO]Gaussbock: Fast parallel-iterative cosmological parameter estimation with Bayesian nonparametrics
    Ben Moews, Joe Zuntz
    http://arxiv.org/abs/1905.09800v1

    • [cs.AI]Automatic Generation of Level Maps with the Do What’s Possible Representation
    Daniel Ashlock, Christoph Salge
    http://arxiv.org/abs/1905.09618v1

    • [cs.AI]Deep Q-Learning with Q-Matrix Transfer Learning for Novel Fire Evacuation Environment
    Jivitesh Sharma, Per-Arne Andersen, Ole-Chrisoffer Granmo, Morten Goodwin
    http://arxiv.org/abs/1905.09673v1

    • [cs.AI]ENIGMAWatch: ProofWatch Meets ENIGMA
    Zarathustra Goertzel, Jan Jakubův, Josef Urban
    http://arxiv.org/abs/1905.09565v1

    • [cs.AI]Knowledge Graph Embedding Bi-Vector Models for Symmetric Relation
    Jinkui Yao, Lianghua Xu
    http://arxiv.org/abs/1905.09557v1

    • [cs.AI]Minimizing the Negative Side Effects of Planning with Reduced Models
    Sandhya Saisubramanian, Shlomo Zilberstein
    http://arxiv.org/abs/1905.09355v1

    • [cs.AI]On modelling the emergence of logical thinking
    Cristian Ivan, Bipin Indurkhya
    http://arxiv.org/abs/1905.09730v1

    • [cs.AI]The African Wildlife Ontology tutorial ontologies: requirements, design, and content
    C Maria Keet
    http://arxiv.org/abs/1905.09519v1

    • [cs.CG]Automated Process Planning for Turning: A Feature-Free Approach
    Morad Behandish, Saigopal Nelaturi, Chaman Singh Verma, Mats Allard
    http://arxiv.org/abs/1905.09434v1

    • [cs.CL]An Investigation of Transfer Learning-Based Sentiment Analysis in Japanese
    Enkhbold Bataa, Joshua Wu
    http://arxiv.org/abs/1905.09642v1

    • [cs.CL]Analyzing Multi-Head Self-Attention: Specialized Heads Do the Heavy Lifting, the Rest Can Be Pruned
    Elena Voita, David Talbot, Fedor Moiseev, Rico Sennrich, Ivan Titov
    http://arxiv.org/abs/1905.09418v1

    • [cs.CL]FastSpeech: Fast, Robust and Controllable Text to Speech
    Yi Ren, Yangjun Ruan, Xu Tan, Tao Qin, Sheng Zhao, Zhou Zhao, Tie-Yan Liu
    http://arxiv.org/abs/1905.09263v2

    • [cs.CL]GWU NLP Lab at SemEval-2019 Task 3: EmoContext: Effective Contextual Information in Models for Emotion Detection in Sentence-level in a Multigenre Corpus
    Shabnam Tafreshi, Mona Diab
    http://arxiv.org/abs/1905.09439v1

    • [cs.CL]MCScript2.0: A Machine Comprehension Corpus Focused on Script Events and Participants
    Simon Ostermann, Michael Roth, Manfred Pinkal
    http://arxiv.org/abs/1905.09531v1

    • [cs.CL]Misspelling Oblivious Word Embeddings
    Bora Edizel, Aleksandra Piktus, Piotr Bojanowski, Rui Ferreira, Edouard Grave, Fabrizio Silvestri
    http://arxiv.org/abs/1905.09755v1

    • [cs.CL]Multi-hop Reading Comprehension via Deep Reinforcement Learning based Document Traversal
    Alex Long, Joel Mason, Alan Blair, Wei Wang
    http://arxiv.org/abs/1905.09438v1

    • [cs.CR]Detecting Malicious PowerShell Scripts Using Contextual Embeddings
    Amir Rubin, Shay Kels, Danny Hendler
    http://arxiv.org/abs/1905.09538v1

    • [cs.CR]Elliptical Perturbations for Differential Privacy
    Matthew Reimherr, Jordan Awan
    http://arxiv.org/abs/1905.09420v1

    • [cs.CR]KNG: The K-Norm Gradient Mechanism
    Matthew Reimherr, Jordan Awan
    http://arxiv.org/abs/1905.09436v1

    • [cs.CR]Privacy-Preserving Obfuscation of Critical Infrastructure Networks
    Ferdinando Fioretto, Terrence W. K. Mak, Pascal Van Hentenryck
    http://arxiv.org/abs/1905.09778v1

    • [cs.CR]StrongChain: Transparent and Collaborative Proof-of-Work Consensus
    Pawel Szalachowski, Daniel Reijsbergen, Ivan Homoliak, Siwei Sun
    http://arxiv.org/abs/1905.09655v1

    • [cs.CV]A Convolutional Cost-Sensitive Crack Localization Algorithm for Automated and Reliable RC Bridge Inspection
    Seyed Omid Sajedi, Xiao Liang
    http://arxiv.org/abs/1905.09716v1

    • [cs.CV]A Direct Approach to Robust Deep Learning Using Adversarial Networks
    Huaxia Wang, Chun-Nam Yu
    http://arxiv.org/abs/1905.09591v1

    • [cs.CV]AttentionRNN: A Structured Spatial Attention Mechanism
    Siddhesh Khandelwal, Leonid Sigal
    http://arxiv.org/abs/1905.09400v1

    • [cs.CV]Automating Whole Brain Histology to MRI Registration: Implementation of a Computational Pipeline
    Maryana Alegro, Eduardo J. L. Alho, Maria da Graca Morais Martin, Lea Teneholz Grinberg, Helmut Heinsen, Roseli de Deus Lopes, Edson Amaro-Jr, Lilla Zöllei
    http://arxiv.org/abs/1905.09339v1

    • [cs.CV]Constrained Design of Deep Iris Networks
    Kien Nguyen, Clinton Fookes, Sridha Sridharan
    http://arxiv.org/abs/1905.09481v1

    • [cs.CV]Depth Estimation on Underwater Omni-directional Images Using a Deep Neural Network
    Haofei Kuang, Qingwen Xu, Sören Schwertfeger
    http://arxiv.org/abs/1905.09441v1

    • [cs.CV]Image Fusion via Sparse Regularization with Non-Convex Penalties
    Nantheera Anantrasirichai, Rencheng Zheng, Ivan Selesnick, Alin Achim
    http://arxiv.org/abs/1905.09645v1

    • [cs.CV]Network Pruning via Transformable Architecture Search
    Xuanyi Dong, Yi Yang
    http://arxiv.org/abs/1905.09717v1

    • [cs.CV]Pose estimator and tracker using temporal flow maps for limbs
    Jihye Hwang, Jieun Lee, Sungheon Park, Nojun Kwak
    http://arxiv.org/abs/1905.09500v1

    • [cs.CV]PoseRBPF: A Rao-Blackwellized Particle Filter for 6D Object Pose Tracking
    Xinke Deng, Arsalan Mousavian, Yu Xiang, Fei Xia, Timothy Bretl, Dieter Fox
    http://arxiv.org/abs/1905.09304v1

    • [cs.CV]Prototype Reminding for Continual Learning
    Mengmi Zhang, Tao Wang, Joo Hwee Lim, Jiashi Feng
    http://arxiv.org/abs/1905.09447v1

    • [cs.CV]Robust Point Cloud Based Reconstruction of Large-Scale Outdoor Scenes
    Ziquan Lan, Zi Jian Yew, Gim Hee Lee
    http://arxiv.org/abs/1905.09634v1

    • [cs.CV]Spatial Group-wise Enhance: Improving Semantic Feature Learning in Convolutional Networks
    Xiang Li, Xiaolin Hu, Jian Yang
    http://arxiv.org/abs/1905.09646v1

    • [cs.CV]Speech2Face: Learning the Face Behind a Voice
    Tae-Hyun Oh, Tali Dekel, Changil Kim, Inbar Mosseri, William T. Freeman, Michael Rubinstein, Wojciech Matusik
    http://arxiv.org/abs/1905.09773v1

    • [cs.CV]Watermark retrieval from 3D printed objects via synthetic data training
    Xin Zhang, Ning Jia, Ioannis Ivrissimtzis
    http://arxiv.org/abs/1905.09706v1

    • [cs.CY]Digital Normativity: A challenge for human subjectivization and free will
    Éric Fourneret, Blaise Yvert
    http://arxiv.org/abs/1905.09735v1

    • [cs.CY]The tradeoff between the utility and risk of location data and implications for public good
    Dan Calacci, Alex Berke, Kent Larson, Alex, Pentland
    http://arxiv.org/abs/1905.09350v1

    • [cs.DB]COBS: a Compact Bit-Sliced Signature Index
    Timo Bingmann, Phelim Bradley, Florian Gauger, Zamin Iqbal
    http://arxiv.org/abs/1905.09624v1

    • [cs.DB]Towards Global Asset Management in Blockchain Systems
    Victor Zakhary, Mohammad Javad Amiri, Sujaya Maiyya, Divyakant Agrawal, Amr El Abbadi
    http://arxiv.org/abs/1905.09359v1

    • [cs.DC]Cross-chain Deals and Adversarial Commerce
    Maurice Herlihy, Barbara Liskov, Liuba Shrira
    http://arxiv.org/abs/1905.09743v1

    • [cs.DC]KPynq: A Work-Efficient Triangle-Inequality based K-means on FPGA
    Yuke Wang, Zhaorui Zeng, Boyuan Feng, Lei Deng, Yufei Ding
    http://arxiv.org/abs/1905.09345v1

    • [cs.DC]Kaleido: An Efficient Out-of-core Graph Mining System on A Single Machine
    Cheng Zhao, Zhibin Zhang, Peng Xu, Tianqi Zheng, Xueqi Cheng
    http://arxiv.org/abs/1905.09572v1

    • [cs.DC]Positional Encoding by Robots with Non-Rigid Movements
    Kaustav Bose, Ranendu Adhikary, Manash Kumar Kundu, Buddhadeb Sau
    http://arxiv.org/abs/1905.09786v1

    • [cs.DC]Topological Characterization of Consensus under General Message Adversaries
    Thomas Nowak, Ulrich Schmid, Kyrill Winkler
    http://arxiv.org/abs/1905.09590v1

    • [cs.DC]Workflow Design Analysis for High Resolution Satellite Image Analysis
    Ioannis Paraskevakos, Matteo Turrili, Bento Collares Gonçalves, Heather J. Lynch, Shantenu Jha
    http://arxiv.org/abs/1905.09766v1

    • [cs.DS]An Efficient Approach for Super and Nested Term Indexing and Retrieval
    Md Faisal Mahbub Chowdhury, Robert Farrell
    http://arxiv.org/abs/1905.09761v1

    • [cs.GR]Data-Driven Crowd Simulation with Generative Adversarial Networks
    Javad Amirian, Wouter van Toll, Jean-Bernard Hayet, Julien Pettré
    http://arxiv.org/abs/1905.09661v1

    • [cs.GT]Diffusion and Auction on Graphs
    Bin Li, Dong Hao, Dengji Zhao, Makoto Yokoo
    http://arxiv.org/abs/1905.09604v1

    • [cs.IR]Practice on Long Sequential User Behavior Modeling for Click-Through Rate Prediction
    Qi Pi, Weijie Bian, Guorui Zhou, Xiaoqiang Zhu, Kun Gai
    http://arxiv.org/abs/1905.09248v2

    • [cs.IT]A Simple Receive Diversity Technique for Distributed Beamforming
    Elad Domanovitz, Uri Erez
    http://arxiv.org/abs/1905.09321v1

    • [cs.IT]Downlink Non-Orthogonal Multiple Access without SIC for Block Fading Channels: An Algebraic Rotation Approach
    Min Qiu, Yu-Chih Huang, Jinhong Yuan
    http://arxiv.org/abs/1905.09514v1

    • [cs.IT]Johnson-Lindenstrauss Property Implies Subspace Restricted Isometry Property
    Xingyu Xv, Gen Li, Yuantao Gu
    http://arxiv.org/abs/1905.09608v1

    • [cs.IT]Optimum Low-Complexity Decoder for Spatial Modulation
    Ibrahim Al-Nahhal, Ertugrul Basar, Octavia A. Dobre, Salama Ikki
    http://arxiv.org/abs/1905.09401v1

    • [cs.IT]Rate-Distortion-Memory Trade-offs in Heterogeneous Caching Networks
    Parisa Hassanzadeh, Antonia M. Tulino, Jaime Llorca, Elza Erkip
    http://arxiv.org/abs/1905.09446v1

    • [cs.IT]Simple Bounds for the Symmetric Capacity of the Rayleigh Fading Multiple Access Channel
    Elad Domanovitz, Uri Erez
    http://arxiv.org/abs/1905.09486v1

    • [cs.LG]Accelerating DNN Training in Wireless Federated Edge Learning System
    Jinke Ren, Guanding Yu, Guangyao Ding
    http://arxiv.org/abs/1905.09712v1

    • [cs.LG]Action Assembly: Sparse Imitation Learning for Text Based Games with Combinatorial Action Spaces
    Chen Tessler, Tom Zahavy, Deborah Cohen, Daniel J. Mankowitz, Shie Mannor
    http://arxiv.org/abs/1905.09700v1

    • [cs.LG]Adversarially Robust Distillation
    Micah Goldblum, Liam Fowl, Soheil Feizi, Tom Goldstein
    http://arxiv.org/abs/1905.09747v1

    • [cs.LG]Augmenting correlation structures in spatial data using deep generative models
    Konstantin Klemmer, Adriano Koshiyama, Sebastian Flennerhag
    http://arxiv.org/abs/1905.09796v1

    • [cs.LG]Average reward reinforcement learning with unknown mixing times
    Tom Zahavy, Alon Cohen, Haim Kaplan, Yishay Mansour
    http://arxiv.org/abs/1905.09704v1

    • [cs.LG]Binary Classification with Bounded Abstention Rate
    Shubhanshu Shekhar, Mohammad Ghavamzadeh, Tara Javidi
    http://arxiv.org/abs/1905.09561v1

    • [cs.LG]Causal Discovery with Cascade Nonlinear Additive Noise Models
    Ruichu Cai, Jie Qiao, Kun Zhang, Zhenjie Zhang, Zhifeng Hao
    http://arxiv.org/abs/1905.09442v1

    • [cs.LG]Cognitive Model Priors for Predicting Human Decisions
    David D. Bourgin, Joshua C. Peterson, Daniel Reichman, Thomas L. Griffiths, Stuart J. Russell
    http://arxiv.org/abs/1905.09397v1

    • [cs.LG]Combination of linear classifiers using score function — analysis of possible combination strategies
    Pawel Trajdos, Robert Burduk
    http://arxiv.org/abs/1905.09522v1

    • [cs.LG]Combine PPO with NES to Improve Exploration
    Lianjiang Li, Yunrong Yang, Bingna Li
    http://arxiv.org/abs/1905.09492v1

    • [cs.LG]Compression with Flows via Local Bits-Back Coding
    Jonathan Ho, Evan Lohn, Pieter Abbeel
    http://arxiv.org/abs/1905.08500v2

    • [cs.LG]Convergence Analyses of Online ADAM Algorithm in Convex Setting and Two-Layer ReLU Neural Network
    Biyi Fang, Diego Klabjan
    http://arxiv.org/abs/1905.09356v1

    • [cs.LG]DEEP-BO for Hyperparameter Optimization of Deep Networks
    Hyunghun Cho, Yongjin Kim, Eunjung Lee, Daeyoung Choi, Yongjae Lee, Wonjong Rhee
    http://arxiv.org/abs/1905.09680v1

    • [cs.LG]Decentralized Learning of Generative Adversarial Networks from Multi-Client Non-iid Data
    Ryo Yonetani, Tomohiro Takahashi, Atsushi Hashimoto, Yoshitaka Ushiku
    http://arxiv.org/abs/1905.09684v1

    • [cs.LG]Disentangling Redundancy for Multi-Task Pruning
    Xiaoxi He, Dawei Gao, Zimu Zhou, Yongxin Tong, Lothar Thiele
    http://arxiv.org/abs/1905.09676v1

    • [cs.LG]Ensemble Model Patching: A Parameter-Efficient Variational Bayesian Neural Network
    Oscar Chang, Yuling Yao, David Williams-King, Hod Lipson
    http://arxiv.org/abs/1905.09453v1

    • [cs.LG]Estimating Risk and Uncertainty in Deep Reinforcement Learning
    William R. Clements, Benoît-Marie Robaglia, Bastien Van Delft, Reda Bahi Slaoui, Sébastien Toth
    http://arxiv.org/abs/1905.09638v1

    • [cs.LG]FiBiNET: Combining Feature Importance and Bilinear feature Interaction for Click-Through Rate Prediction
    Tongwen Huang, Zhiqi Zhang, Junlin Zhang
    http://arxiv.org/abs/1905.09433v1

    • [cs.LG]Fire Now, Fire Later: Alarm-Based Systems for Prescriptive Process Monitoring
    Stephan A. Fahrenkrog-Petersen, Niek Tax, Irene Teinemaa, Marlon Dumas, Massimiliano de Leoni, Fabrizio Maria Maggi, Matthias Weidlich
    http://arxiv.org/abs/1905.09568v1

    • [cs.LG]From semantics to execution: Integrating action planning with reinforcement learning for robotic tool use
    Manfred Eppe, Phuong D. H. Nguyen, Stefan Wermter
    http://arxiv.org/abs/1905.09683v1

    • [cs.LG]Fully Neural Network based Model for General Temporal Point Processes
    Takahiro Omi, Naonori Ueda, Kazuyuki Aihara
    http://arxiv.org/abs/1905.09690v1

    • [cs.LG]Generative Imputation and Stochastic Prediction
    Mohammad Kachuee, Kimmo Karkkainen, Orpaz Goldstein, Sajad Darabi, Majid Sarrafzadeh
    http://arxiv.org/abs/1905.09340v1

    • [cs.LG]Geometric Laplacian Eigenmap Embedding
    Leo Torres, Kevin S Chan, Tina Eliassi-Rad
    http://arxiv.org/abs/1905.09763v1

    • [cs.LG]Glioma Grade Predictions using Scattering Wavelet Transform-Based Radiomics
    Qijian Chen, Lihui Wang, Li Wang, Zeyu Deng, Jian Zhang, Yuemin Zhu
    http://arxiv.org/abs/1905.09589v1

    • [cs.LG]Gravity-Inspired Graph Autoencoders for Directed Link Prediction
    Guillaume Salha, Stratis Limnios, Romain Hennequin, Viet Anh Tran, Michalis Vazirgiannis
    http://arxiv.org/abs/1905.09570v1

    • [cs.LG]Hierarchical Annotation of Images with Two-Alternative-Forced-Choice Metric Learning
    Niels Hellinga, Vlado Menkovski
    http://arxiv.org/abs/1905.09523v1

    • [cs.LG]How degenerate is the parametrization of neural networks with the ReLU activation function?
    Julius Berner, Dennis Elbrächter, Philipp Grohs
    http://arxiv.org/abs/1905.09803v1

    • [cs.LG]Imitation Learning from Video by Leveraging Proprioception
    Faraz Torabi, Garrett Warnell, Peter Stone
    http://arxiv.org/abs/1905.09335v1

    • [cs.LG]Interpreting Adversarially Trained Convolutional Neural Networks
    Tianyuan Zhang, Zhanxing Zhu
    http://arxiv.org/abs/1905.09797v1

    • [cs.LG]Inverse Reinforcement Learning in Contextual MDPs
    Philip Korsunsky, Stav Belo, Tom Zahavy, Chen Tessler, Shie Mannor
    http://arxiv.org/abs/1905.09710v1

    • [cs.LG]Kernel Wasserstein Distance
    Jung Hun Oh, Maryam Pouryahya, Aditi Iyer, Aditya P. Apte, Allen Tannenbaum, Joseph O. Deasy
    http://arxiv.org/abs/1905.09314v1

    • [cs.LG]Learning Discrete and Continuous Factors of Data via Alternating Disentanglement
    Yeonwoo Jeong, Hyun Oh Song
    http://arxiv.org/abs/1905.09432v1

    • [cs.LG]Leveraging Uncertainty in Deep Learning for Selective Classification
    Mehmet Yigit Yildirim, Mert Ozer, Hasan Davulcu
    http://arxiv.org/abs/1905.09509v1

    • [cs.LG]MATCHA: Speeding Up Decentralized SGD via Matching Decomposition Sampling
    Jianyu Wang, Anit Kumar Sahu, Zhouyi Yang, Gauri Joshi, Soummya Kar
    http://arxiv.org/abs/1905.09435v1

    • [cs.LG]MCP: Learning Composable Hierarchical Control with Multiplicative Compositional Policies
    Xue Bin Peng, Michael Chang, Grace Zhang, Pieter Abbeel, Sergey Levine
    http://arxiv.org/abs/1905.09808v1

    • [cs.LG]MR-GNN: Multi-Resolution and Dual Graph Neural Network for Predicting Structured Entity Interactions
    Nuo Xu, Pinghui Wang, Long Chen, Jing Tao, Junzhou Zhao
    http://arxiv.org/abs/1905.09558v1

    • [cs.LG]Meta-GNN: On Few-shot Node Classification in Graph Meta-learning
    Fan Zhou, Chengtai Cao, Kunpeng Zhang, Goce Trajcevski, Ting Zhong, Ji Geng
    http://arxiv.org/abs/1905.09718v1

    • [cs.LG]Multi-Service Mobile Traffic Forecasting via Convolutional Long Short-Term Memories
    Chaoyun Zhang, Marco Fiore, Paul Patras
    http://arxiv.org/abs/1905.09771v1

    • [cs.LG]Multi-relational Poincaré Graph Embeddings
    Ivana Balažević, Carl Allen, Timothy Hospedales
    http://arxiv.org/abs/1905.09791v1

    • [cs.LG]Multinomial Distribution Learning for Effective Neural Architecture Search
    Xiawu Zheng, Rongrong Ji, Lang Tang, Baochang Zhang, Jianzhuang Liu, Qi Tian
    http://arxiv.org/abs/1905.07529v2

    • [cs.LG]New methods for SVM feature selection
    Tangui Aladjidi, François Pasqualini
    http://arxiv.org/abs/1905.09653v1

    • [cs.LG]Non-monotone DR-submodular Maximization: Approximation and Regret Guarantees
    Christoph Dürr, Nguyen Kim Thang, Abhinav Srivastav, Léo Tible
    http://arxiv.org/abs/1905.09595v1

    • [cs.LG]Outlier Robust Extreme Learning Machine for Multi-Target Regression
    Bruno Légora Souza da Silva, Fernando Kentaro Inaba, Evandro Ottoni Teatini Salles, Patrick Marques Ciarelli
    http://arxiv.org/abs/1905.09368v1

    • [cs.LG]Parsimonious Deep Learning: A Differential Inclusion Approach with Global Convergence
    Yanwei Fu, Chen Liu, Donghao Li, Xinwei Sun, Jinshan Zeng, Yuan Yao
    http://arxiv.org/abs/1905.09449v1

    • [cs.LG]Quantifying Long Range Dependence in Language and User Behavior to improve RNNs
    Francois Belletti, Minmin Chen, Ed H. Chi
    http://arxiv.org/abs/1905.09414v1

    • [cs.LG]Recurrent Value Functions
    Pierre Thodoroff, Nishanth Anand, Lucas Caccia, Doina Precup, Joelle Pineau
    http://arxiv.org/abs/1905.09562v1

    • [cs.LG]Some limitations of norm based generalization bounds in deep neural networks
    Konstantinos Pitas, Andreas Loukas, Mike Davies, Pierre Vandergheynst
    http://arxiv.org/abs/1905.09677v1

    • [cs.LG]The Convolutional Tsetlin Machine
    Ole-Christoffer Granmo, Sondre Glimsdal, Lei Jiao, Morten Goodwin, Christian W. Omlin, Geir Thore Berge
    http://arxiv.org/abs/1905.09688v1

    • [cs.LG]The Journey is the Reward: Unsupervised Learning of Influential Trajectories
    Jonathan Binas, Sherjil Ozair, Yoshua Bengio
    http://arxiv.org/abs/1905.09334v1

    • [cs.LG]Tucker Decomposition Network: Expressive Power and Comparison
    Ye Liu, Junjun Pan, Michael Ng
    http://arxiv.org/abs/1905.09635v1

    • [cs.LG]Zero-shot Knowledge Transfer via Adversarial Belief Matching
    Paul Micaelli, Amos Storkey
    http://arxiv.org/abs/1905.09768v1

    • [cs.LO]Learning to Prove Theorems via Interacting with Proof Assistants
    Kaiyu Yang, Jia Deng
    http://arxiv.org/abs/1905.09381v1

    • [cs.MA]Nature-Inspired Computational Model of Population Desegregation under Group Leaders Influence
    Kashif Zia, Dinesh Kumar Saini, Arshad Muhammad, Alois Ferscha
    http://arxiv.org/abs/1905.09795v1

    • [cs.NE]CUDA-Self-Organizing feature map based visual sentiment analysis of bank customer complaints for Analytical CRM
    Rohit Gavval, Vadlamani Ravi, Kalavala Revanth Harshal, Akhilesh Gangwar, Kumar Ravi
    http://arxiv.org/abs/1905.09598v1

    • [cs.NE]Comparing and Combining Lexicase Selection and Novelty Search
    Lia Jundt, Thomas Helmuth
    http://arxiv.org/abs/1905.09374v1

    • [cs.NE]Effect of shapes of activation functions on predictability in the echo state network
    Hanten Chang, Shinji Nakaoka, Hiroyasu Ando
    http://arxiv.org/abs/1905.09419v1

    • [cs.NE]Improving Neural Networks by Adopting Amplifying and Attenuating Neurons
    Seongmun Jung, Oh Joon Kwon
    http://arxiv.org/abs/1905.09574v1

    • [cs.NE]Lexicase Selection of Specialists
    Thomas Helmuth, Edward Pantridge, Lee Spector
    http://arxiv.org/abs/1905.09372v1

    • [cs.NE]Multi-Sample Dropout for Accelerated Training and Better Generalization
    Hiroshi Inoue
    http://arxiv.org/abs/1905.09788v1

    • [cs.PL]Hypothetical answers to continuous queries over data streams
    Luís Cruz-Filipe, Graça Gaspar, Isabel Nunes
    http://arxiv.org/abs/1905.09610v1

    • [cs.RO]A ROS2 based communication architecture for control in collaborative and intelligent automation systems
    Endre Erős, Martin Dahl, Kristofer Bengtsson, Atieh Hanna, Petter Falkman
    http://arxiv.org/abs/1905.09654v1

    • [cs.RO]Deep Drone Racing: From Simulation to Reality with Domain Randomization
    Antonio Loquercio, Elia Kaufmann, René Ranftl, Alexey Dosovitskiy, Vladlen Koltun, Davide Scaramuzza
    http://arxiv.org/abs/1905.09727v1

    • [cs.RO]Hierarchical Reinforcement Learning for Concurrent Discovery of Compound and Composable Policies
    Domingo Esteban, Leonel Rozo, Darwin G. Caldwell
    http://arxiv.org/abs/1905.09668v1

    • [cs.RO]IN2LAAMA: INertial Lidar Localisation Autocalibration And MApping
    Cedric Le Gentil, Teresa Vidal-Calleja, Shoudong Huang
    http://arxiv.org/abs/1905.09517v1

    • [cs.RO]Incorporating Human Domain Knowledge in 3D LiDAR-based Semantic Segmentation
    Jilin Mei, Huijing Zhao
    http://arxiv.org/abs/1905.09533v1

    • [cs.RO]Nullspace Structure in Model Predictive Control
    Hakan Girgin, Sylvain Calinon
    http://arxiv.org/abs/1905.09679v1

    • [cs.RO]Predictive Control for Chasing a Ground Vehicle using a UAV
    Jaeseung Byun, Karan P. Jain, Siddharth H. Nair, Haoyun Xu, Jiaming Zha
    http://arxiv.org/abs/1905.09396v1

    • [cs.RO]Reachable Space Characterization of Markov Decision Processes with Time Variability
    Junhong Xu, Yin Kai, Lantao Liu
    http://arxiv.org/abs/1905.09342v1

    • [cs.RO]Teleoperator Imitation with Continuous-time Safety
    Bachir El Khadir, Jake Varley, Vikas Sindhwani
    http://arxiv.org/abs/1905.09499v1

    • [cs.RO]Towards Generation and Evaluation of Comprehensive Mapping Robot Datasets
    Hongyu Chen, Xiting Zhao, Jianwen Luo, Zhijie Yang, Zehao Zhao, Haochuan Wan, Xiaoya Ye, Guangyuan Weng, Zhenpeng He, Tian Dong, Sören Schwertfeger
    http://arxiv.org/abs/1905.09483v1

    • [cs.RO]Towards Safety-Aware Computing System Design in Autonomous Vehicles
    Hengyu Zhao, Yubo Zhang, Pingfan Meng, Hui Shi, Li Erran Li, Tiancheng Lou, Jishen Zhao
    http://arxiv.org/abs/1905.08453v2

    • [cs.RO]Variational Inference with Mixture Model Approximation: Robotic Applications
    Emmanuel Pignat, Teguh Lembono, Sylvain Calinon
    http://arxiv.org/abs/1905.09597v1

    • [cs.SI]Network Density of States
    Kun Dong, Austin R. Benson, David Bindel
    http://arxiv.org/abs/1905.09758v1

    • [cs.SI]Price of Dependence: Stochastic Submodular Maximization with Dependent Items
    Shaojie Tang
    http://arxiv.org/abs/1905.09719v1

    • [eess.IV]Fusion of heterogeneous bands and kernels in hyperspectral image processing
    Muhammad Aminul Islam, Derek T. Anderson, John E. Ball, Nicolas H. Younan
    http://arxiv.org/abs/1905.09698v1

    • [eess.IV]Learning Fast Magnetic Resonance Imaging
    Tomer Weiss, Sanketh Vedula, Ortal Senouf, Alex Bronstein, Oleg Michailovich, Michael Zibulevsky
    http://arxiv.org/abs/1905.09324v1

    • [eess.IV]Self-supervised learning of inverse problem solvers in medical imaging
    Ortal Senouf, Sanketh Vedula, Tomer Weiss, Alex Bronstein, Oleg Michailovich, Michael Zibulevsky
    http://arxiv.org/abs/1905.09325v1

    • [eess.IV]Spatio-Temporal Deep Learning Models for Tip Force Estimation During Needle Insertion
    Nils Gessert, Torben Priegnitz, Thore Saathoff, Sven-Thomas Antoni, David Meyer, Moritz Franz Hamann, Klaus-Peter Jünemann, Christoph Otte, Alexander Schlaefer
    http://arxiv.org/abs/1905.09282v1

    • [eess.IV]Underwater Stereo using Refraction-free Image Synthesized from Light Field Camera
    Kazuto Ichimaru, Hiroshi Kawasaki
    http://arxiv.org/abs/1905.09588v1

    • [eess.SP]A Comparative Study of Analog/Digital Self-Interference Cancellation for Full Duplex Radios
    Jong Woo Kwak, Min Soo Sim, In-Woong Kang, Jong Sung Park, Jaedon Park, Chan-Byoung Chae
    http://arxiv.org/abs/1905.09616v1

    • [eess.SP]One-bit LFMCW Radar: Spectrum Analysis and Target Detection
    Benzhou Jin, Jiang Zhu, Qihui Wu, Yuhong Zhang, Zhiwei Xu
    http://arxiv.org/abs/1905.09440v1

    • [math.ST]On generalized Piterbarg-Berman function
    Chengxiu Ling, Hong Zhang, Long Bai
    http://arxiv.org/abs/1905.09599v1

    • [math.ST]Sparse Equisigned PCA: Algorithms and Performance Bounds in the Noisy Rank-1 Setting
    Arvind Prasadan, Raj Rao Nadakuditi, Debashis Paul
    http://arxiv.org/abs/1905.09369v1

    • [math.ST]Sparse Minimax Optimality of Bayes Predictive Density Estimates from Clustered Discrete Priors
    Ujan Gangopadhyay, Gourab Mukherjee
    http://arxiv.org/abs/1905.09451v1

    • [physics.ao-ph]Learning the Representations of Moist Convection with Convolutional Neural Networks
    Shih-Wen Tsou, Chun-Yian Su, Chien-Ming Wu
    http://arxiv.org/abs/1905.09614v1

    • [physics.comp-ph]The Stabilized Explicit Variable-Load Solver with Machine Learning Acceleration for the Rapid Solution of Stiff Chemical Kinetics
    Kyle Buchheit, Opeoluwa Owoyele, Terry Jordan, Dirk Van Essendelft
    http://arxiv.org/abs/1905.09395v1

    • [physics.data-an]A Predictive Model for Steady-State Multiphase Pipe Flow: Machine Learning on Lab Data
    Evgenii Kanin, Andrei Osiptsov, Albert Vainshtein, Evgeny Burnaev
    http://arxiv.org/abs/1905.09746v1

    • [physics.data-an]Shades of Dark Uncertainty and Consensus Value for the Newtonian Constant of Gravitation
    Christos Merkatas, Blaza Toman, Antonio Possolo, Stephan Schlamminger
    http://arxiv.org/abs/1905.09551v1

    • [physics.soc-ph]Applied hybrid binary mixed logit to investigate pedestrian crossing safety at midblock and unsignalized intersection
    Mohammad Ali Arman, Amir Rafe, Tobias Kretz
    http://arxiv.org/abs/1905.09403v1

    • [physics.soc-ph]Scale-free networks revealed from finite-size scaling
    Matteo Serafino, Giulio Cimini, Amos Maritan, Samir Suweis, Jayanth R. Banavar, Guido Caldarelli
    http://arxiv.org/abs/1905.09512v1

    • [physics.soc-ph]What is the Entropy of a Social Organization?
    Christian Zingg, Giona Casiraghi, Giacomo Vaccario, Frank Schweitzer
    http://arxiv.org/abs/1905.09772v1

    • [q-fin.ST]Detection of Chinese Stock Market Bubbles with LPPLS Confidence Indicator
    Min Shu, Wei Zhu
    http://arxiv.org/abs/1905.09640v1

    • [q-fin.ST]Diagnosis and Prediction of the 2015 Chinese Stock Market Bubble
    Min Shu, Wei Zhu
    http://arxiv.org/abs/1905.09633v1

    • [q-fin.ST]Real-time Prediction of Bitcoin bubble Crashes
    Min Shu, Wei Zhu
    http://arxiv.org/abs/1905.09647v1

    • [stat.AP]Targeted Smooth Bayesian Causal Forests: An analysis of heterogeneous treatment effects for simultaneous versus interval medical abortion regimens over gestation
    Jennifer E. Starling, Jared S. Murray, Patricia A. Lohr, Abigail R. A. Aiken, Carlos M. Carvalho, James G. Scott
    http://arxiv.org/abs/1905.09405v1

    • [stat.AP]semopy: A Python package for Structural Equation Modeling
    Meshcheryakov Georgy, Igolkina Anna
    http://arxiv.org/abs/1905.09376v1

    • [stat.CO]A Condition Number for Hamiltonian Monte Carlo
    Ian Langmore
    http://arxiv.org/abs/1905.09813v1

    • [stat.ME]Atlantic Causal Inference Conference (ACIC) Data Analysis Challenge 2017
    P. Richard Hahn, Vincent Dorie, Jared S. Murray
    http://arxiv.org/abs/1905.09515v1

    • [stat.ME]Learning When-to-Treat Policies
    Xinkun Nie, Emma Brunskill, Stefan Wager
    http://arxiv.org/abs/1905.09751v1

    • [stat.ME]Random Norming Aids Analysis of Non-linear Regression Models with Sequential Informative Dose Selection
    Zhantao Lin, Nancy Flournoy, William F. Rosenberger
    http://arxiv.org/abs/1905.09722v1

    • [stat.ME]Restricted Spatial Regression Methods: Implications for Inference
    Kori Khan, Catherine A. Calder
    http://arxiv.org/abs/1905.09371v1

    • [stat.ME]Slamming the sham: A Bayesian model for adaptive adjustment with noisy control data
    Andrew Gelman, Matthijs Vákár
    http://arxiv.org/abs/1905.09693v1

    • [stat.ML]An Optimal Private Stochastic-MAB Algorithm Based on an Optimal Private Stopping Rule
    Touqir Sajed, Or Sheffet
    http://arxiv.org/abs/1905.09383v1

    • [stat.ML]Bayesian Optimization over Sets
    Jungtaek Kim, Michael McCourt, Tackgeun You, Saehoon Kim, Seungjin Choi
    http://arxiv.org/abs/1905.09780v1

    • [stat.ML]Multi-Class Gaussian Process Classification Made Conjugate: Efficient Inference via Data Augmentation
    Théo Galy-Fajou, Florian Wenzel, Christian Donner, Manfred Opper
    http://arxiv.org/abs/1905.09670v1

    • [stat.ML]Population-based Global Optimisation Methods for Learning Long-term Dependencies with RNNs
    Bryan Lim, Stefan Zohren, Stephen Roberts
    http://arxiv.org/abs/1905.09691v1

    • [stat.ML]Replicated Vector Approximate Message Passing For Resampling Problem
    Takashi Takahashi, Yoshiyuki Kabashima
    http://arxiv.org/abs/1905.09545v1

    • [stat.ML]Revisiting Graph Neural Networks: All We Have is Low-Pass Filters
    Hoang NT, Takanori Maehara
    http://arxiv.org/abs/1905.09550v1