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.DL - 数字图书馆 cs.DS - 数据结构与算法 cs.FL - 形式语言与自动机理论 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.NE - 神经与进化计算 cs.RO - 机器人学 cs.SE - 软件工程 cs.SI - 社交网络与信息网络 econ.EM - 计量经济学 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.NA - 数值分析 math.OC - 优化与控制 math.ST - 统计理论 q-bio.GN - 基因组学 q-bio.QM - 定量方法 q-bio.TO - 组织和器官 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
• [cs.AI]A Framework for Parallelizing OWL Classification in Description Logic Reasoners
• [cs.AI]A new approach to forecast service parts demand by integrating user preferences into multi-objective optimization
• [cs.AI]Declarative Learning-Based Programming as an Interface to AI Systems
• [cs.AI]Memetic EDA-Based Approaches to Comprehensive Quality-Aware Automated Semantic Web Service Composition
• [cs.AI]Novelty Messages Filtering for Multi Agent Privacy-preserving Planning
• [cs.AI]Solving Multiagent Planning Problems with Concurrent Conditional Effects
• [cs.CL]Adaptation of Machine Translation Models with Back-translated Data using Transductive Data Selection Methods
• [cs.CL]Attention Guided Graph Convolutional Networks for Relation Extraction
• [cs.CL]Code-Switching Detection Using ASR-Generated Language Posteriors
• [cs.CL]EditNTS: An Neural Programmer-Interpreter Model for Sentence Simplification through Explicit Editing
• [cs.CL]Expressing Visual Relationships via Language
• [cs.CL]Large-Scale Speaker Diarization of Radio Broadcast Archives
• [cs.CL]Multilingual Multi-Domain Adaptation Approaches for Neural Machine Translation
• [cs.CL]Multimodal Abstractive Summarization for How2 Videos
• [cs.CL]Pre-Training with Whole Word Masking for Chinese BERT
• [cs.CL]Second-Order Semantic Dependency Parsing with End-to-End Neural Networks
• [cs.CL]Surf at MEDIQA 2019: Improving Performance of Natural Language Inference in the Clinical Domain by Adopting Pre-trained Language Model
• [cs.CL]The Effect of Translationese in Machine Translation Test Sets
• [cs.CL]XLNet: Generalized Autoregressive Pretraining for Language Understanding
• [cs.CR]BitcoinHeist: Topological Data Analysis for Ransomware Detection on the Bitcoin Blockchain
• [cs.CV]A generative approach to unsupervised deep local learning
• [cs.CV]A simple and effective postprocessing method for image classification
• [cs.CV]An Action Recognition network for specific target based on rMC and RPN
• [cs.CV]Analytical Derivatives for Differentiable Renderer: 3D Pose Estimation by Silhouette Consistency
• [cs.CV]Artistic Enhancement and Style Transfer of Image Edges using Directional Pseudo-coloring
• [cs.CV]Automatic Scale Estimation of Structure from Motion based 3D Models using Laser Scalers
• [cs.CV]Cloud-based Image Classification Service Is Not Robust To Simple Transformations: A Forgotten Battlefield
• [cs.CV]Crop Lodging Prediction from UAV-Acquired Images of Wheat and Canola using a DCNN Augmented with Handcrafted Texture Features
• [cs.CV]Event-based Star Tracking via Multiresolution Progressive Hough Transforms
• [cs.CV]Extended probabilistic Rand index and the adjustable moving window-based pixel-pair sampling method
• [cs.CV]Imbalanced Learning-based Automatic SAR Images Change Detection by Morphologically Supervised PCA-Net
• [cs.CV]Key Instance Selection for Unsupervised Video Object Segmentation
• [cs.CV]Learning to Reconstruct and Understand Indoor Scenes from Sparse Views
• [cs.CV]Model-based Deep MR Imaging: the roadmap of generalizing compressed sensing model using deep learning
• [cs.CV]Monocular 3D Object Detection and Box Fitting Trained End-to-End Using Intersection-over-Union Loss
• [cs.CV]Neural Point-Based Graphics
• [cs.CV]PoseConvGRU: A Monocular Approach for Visual Ego-motion Estimation by Learning
• [cs.CV]Quantifying and Leveraging Classification Uncertainty for Chest Radiograph Assessment
• [cs.CV]SAR Image Change Detection via Spatial Metric Learning with an Improved Mahalanobis Distance
• [cs.CV]SEN12MS — A Curated Dataset of Georeferenced Multi-Spectral Sentinel-1/2 Imagery for Deep Learning and Data Fusion
• [cs.CV]Tumor Saliency Estimation for Breast Ultrasound Images via Breast Anatomy Modeling
• [cs.CV]Unsupervised Learning of Object Structure and Dynamics from Videos
• [cs.CV]ViP: Virtual Pooling for Accelerating CNN-based Image Classification and Object Detection
• [cs.CY]Addressing behavioral change towards energy efficiency in European educational buildings
• [cs.CY]Controllable Planning, Responsibility, and Information in automatic Driving Technology
• [cs.CY]Ethically Aligned Design of Autonomous Systems: Industry viewpoint and an empirical study
• [cs.CY]Subtle Censorship via Adversarial Fakeness in Kyrgyzstan
• [cs.DB]Low-resource Deep Entity Resolution with Transfer and Active Learning
• [cs.DB]The Linked Open Data cloud is more abstract, flatter and less linked than you may think!
• [cs.DC]A Static Analysis-based Cross-Architecture Performance Prediction Using Machine Learning
• [cs.DC]An Outlier-aware Consensus Protocol for Blockchain-based IoT Networks Using Hyperledger Fabric
• [cs.DC]From Facility to Application Sensor Data: Modular, Continuous and Holistic Monitoring with DCDB
• [cs.DC]MaGPoS — A novel decentralized consensus mechanism combining magnetism and proof of stake
• [cs.DC]MediaPipe: A Framework for Building Perception Pipelines
• [cs.DC]Reduced I/O Latency with Futures
• [cs.DC]SeeMoRe: A Fault-Tolerant Protocol for Hybrid Cloud Environments
• [cs.DC]Write-Optimized and Consistent RDMA-based NVM Systems
• [cs.DL]Gender trends in computer science authorship
• [cs.DS]Indexing Graph Search Trees and Applications
• [cs.DS]On the Constrained Least-cost Tour Problem
• [cs.DS]Space Efficient Algorithms for Breadth-Depth Search
• [cs.DS]Tutorial on algebraic deletion correction codes
• [cs.FL]Learning with Partially Ordered Representations
• [cs.GR]Neural Volumes: Learning Dynamic Renderable Volumes from Images
• [cs.GT]Who is in Your Top Three? Optimizing Learning in Elections with Many Candidates
• [cs.IR]A survey of OpenRefine reconciliation services
• [cs.IR]Domain Adaptation for Enterprise Email Search
• [cs.IR]SEntNet: Source-aware Recurrent Entity Network for Dialogue Response Selection
• [cs.IT]Cooperative Rate-Splitting for MISO Broadcast Channel with User Relaying
• [cs.IT]Joint Learning of Geometric and Probabilistic Constellation Shaping
• [cs.IT]Linear Complexity of A Family of Binary $pq^2$-periodic Sequences From Euler Quotients
• [cs.IT]Low Probability of Detection Communication: Opportunities and Challenges
• [cs.IT]MPEG-2 Prediction Residue Analysis
• [cs.IT]The Effect of Spatial Correlation on the Performance of Uplink and Downlink Single-Carrier Massive MIMO Systems
• [cs.IT]Time Synchronization in 5G Wireless Edge: Requirements and Solutions for Critical-MTC
• [cs.LG]A unified view on differential privacy and robustness to adversarial examples
• [cs.LG]Adapting Behaviour via Intrinsic Reward: A Survey and Empirical Study
• [cs.LG]Adaptive Temporal-Difference Learning for Policy Evaluation with Per-State Uncertainty Estimates
• [cs.LG]Adversarial Task-Specific Privacy Preservation under Attribute Attack
• [cs.LG]Agnostic data debiasing through a local sanitizer learnt from an adversarial network approach
• [cs.LG]Automatic Source Code Summarization with Extended Tree-LSTM
• [cs.LG]Barron Spaces and the Compositional Function Spaces for Neural Network Models
• [cs.LG]Batch Active Learning Using Determinantal Point Processes
• [cs.LG]BatchBALD: Efficient and Diverse Batch Acquisition for Deep Bayesian Active Learning
• [cs.LG]Clustering with Fairness Constraints: A Flexible and Scalable Approach
• [cs.LG]Constrained Bilinear Factorization Multi-view Subspace Clustering
• [cs.LG]Control What You Can: Intrinsically Motivated Task-Planning Agent
• [cs.LG]Convergence of Adversarial Training in Overparametrized Networks
• [cs.LG]Deep Learning-Based Quantization of L-Values for Gray-Coded Modulation
• [cs.LG]Directed Exploration for Reinforcement Learning
• [cs.LG]Discovery of Physics from Data: Universal Laws and Discrepancy Models
• [cs.LG]Disentangling feature and lazy learning in deep neural networks: an empirical study
• [cs.LG]Efficient Algorithms for Set-Valued Prediction in Multi-Class Classification
• [cs.LG]Evaluating Protein Transfer Learning with TAPE
• [cs.LG]Generative Restricted Kernel Machines
• [cs.LG]Global Adversarial Attacks for Assessing Deep Learning Robustness
• [cs.LG]Gradient Dynamics of Shallow Univariate ReLU Networks
• [cs.LG]Hill Climbing on Value Estimates for Search-control in Dyna
• [cs.LG]Information matrices and generalization
• [cs.LG]Inverting Deep Generative models, One layer at a time
• [cs.LG]Joint Pruning on Activations and Weights for Efficient Neural Networks
• [cs.LG]LIA: Latently Invertible Autoencoder with Adversarial Learning
• [cs.LG]Learning Directed Graphical Models from Gaussian Data
• [cs.LG]Learning Disentangled Representations of Timbre and Pitch for Musical Instrument Sounds Using Gaussian Mixture Variational Autoencoders
• [cs.LG]Learning in Restless Multi-Armed Bandits via Adaptive Arm Sequencing Rules
• [cs.LG]On the Robustness of the Backdoor-based Watermarking in Deep Neural Networks
• [cs.LG]Poisoning Attacks with Generative Adversarial Nets
• [cs.LG]Provable Gradient Variance Guarantees for Black-Box Variational Inference
• [cs.LG]QXplore: Q-learning Exploration by Maximizing Temporal Difference Error
• [cs.LG]RadGrad: Active learning with loss gradients
• [cs.LG]SemanticAdv: Generating Adversarial Examples via Attribute-conditional Image Editing
• [cs.LG]Supervised Hierarchical Clustering with Exponential Linkage
• [cs.LG]Transfer NAS: Knowledge Transfer between Search Spaces with Transformer Agents
• [cs.LG]Unsupervised State Representation Learning in Atari
• [cs.LG]Wasserstein Adversarial Imitation Learning
• [cs.LG]When to Trust Your Model: Model-Based Policy Optimization
• [cs.LG]XNAS: Neural Architecture Search with Expert Advice
• [cs.NE]A general methodology to assess symbolic regression algorithms using the generation of random equations with uniform random sampling
• [cs.RO]A Grasping-centered Analysis for Cloth Manipulation
• [cs.RO]Characterizing the Uncertainty of Jointly Distributed Poses in the Lie Algebra
• [cs.RO]Development of a robotic system for automatic organic chemistry synthesis
• [cs.RO]Providentia — A Large Scale Sensing System for the Assistance of Autonomous Vehicles
• [cs.RO]PyRobot: An Open-source Robotics Framework for Research and Benchmarking
• [cs.RO]Standalone and RTK GNSS on 30,000 km of North American Highways
• [cs.SE]Ethical Interviews in Software Engineering
• [cs.SI]Detecting problematic transactions in a consumer-to-consumer e-commerce network
• [cs.SI]Predicting Drug Responses by Propagating Interactions through Text-Enhanced Drug-Gene Networks
• [cs.SI]Predicting Evacuation Decisions using Representations of Individuals’ Pre-Disaster Web Search Behavior
• [econ.EM]From Local to Global: External Validity in a Fertility Natural Experiment
• [eess.AS]Multi-Stream End-to-End Speech Recognition
• [eess.AS]Real to H-space Encoder for Speech Recognition
• [eess.AS]Robust End to End Speaker Verification Using EEG
• [eess.AS]Speech Recognition With No Speech Or With Noisy Speech Beyond English
• [eess.AS]The Second DIHARD Diarization Challenge: Dataset, task, and baselines
• [eess.IV]Automated Computer Evaluation of Acute Ischemic Stroke and Large Vessel Occlusion
• [eess.IV]Image Super Resolution via Bilinear Pooling: Application to Confocal Endomicroscopy
• [eess.SP]Multi-user Resource Control with Deep Reinforcement Learning in IoT Edge Computing
• [eess.SY]Clock synchronization over networks — Identifiability of the sawtooth model
• [eess.SY]Joint Optimization of Transmission and Propulsion in UAV-Assisted Communication Networks
• [math.NA]A scalable multilevel domain decomposition preconditioner with a subspace-based coarsening algorithm for the neutron transport calculations
• [math.NA]Sparse approximate matrix multiplication in a fully recursive distributed task-based parallel framework
• [math.OC]Engineering and Business Applications of Sum of Squares Polynomials
• [math.OC]Locally Accelerated Conditional Gradients
• [math.ST]Bump detection in the presence of dependency: Does it ease or does it load?
• [math.ST]Central limit theorem for a partially observed interacting system of Hawkes processes
• [math.ST]Improper vs finitely additive distributions as limits of countably additive probabilities
• [math.ST]Knowledge Gradient for Selection with Covariates: Consistency and Computation
• [math.ST]Rate-optimal estimation of the Blumenthal-Getoor index of a Lévy process
• [math.ST]Recovering low-rank structure from multiple networks with unknown edge distributions
• [math.ST]Safe Testing
• [math.ST]Variances of surface area estimators based on pixel configuration counts
• [q-bio.GN]Convolutional neural network models for cancer type prediction based on gene expression
• [q-bio.QM]Automatic estimation of heading date of paddy rice using deep learning
• [q-bio.TO]Automated Definition of Skeletal Disease Burden in Metastatic Prostate Carcinoma: a 3D analysis of SPECT/CT images
• [stat.AP]Killings of social leaders in the Colombian post-conflict: Data analysis for investigative journalism
• [stat.AP]Learning data representation using modified autoencoder for the integrative analysis of multi-omics data
• [stat.CO]Bayesian inverse regression for supervised dimension reduction with small datasets
• [stat.ME]A spatial dependence graph model for multivariate spatial hybrid processes
• [stat.ME]Estimation of Markovian-regime-switching models with independent regimes
• [stat.ME]Finite sample properties of the Buckland-Burnham-Augustin confidence interval centered on a model averaged estimator
• [stat.ME]Multiple Testing Embedded in an Aggregation Tree to Identify where Two Distributions Differ
• [stat.ME]Should Observations be Grouped for Effective Monitoring of Multivariate Process Variability?
• [stat.ME]Vecchia-Laplace approximations of generalized Gaussian processes for big non-Gaussian spatial data
• [stat.ML]Explanations can be manipulated and geometry is to blame
• [stat.ML]Identification and Estimation of Hierarchical Latent Attribute Models
• [stat.ML]Kernel quadrature with DPPs
• [stat.ML]Local Bures-Wasserstein Transport: A Practical and Fast Mapping Approximation
• [stat.ML]Semi-supervised Logistic Learning Based on Exponential Tilt Mixture Models
• [stat.ML]Stochastic Runge-Kutta Accelerates Langevin Monte Carlo and Beyond
• [stat.ML]Variational Gaussian Processes with Signature Covariances
·····································
• [cs.AI]A Framework for Parallelizing OWL Classification in Description Logic Reasoners
Zixi Quan, Volker Haarslev
http://arxiv.org/abs/1906.07749v1
• [cs.AI]A new approach to forecast service parts demand by integrating user preferences into multi-objective optimization
Wenli Ouyang
http://arxiv.org/abs/1906.06816v2
• [cs.AI]Declarative Learning-Based Programming as an Interface to AI Systems
Parisa Kordjamshidi, Dan Roth, Kristian Kersting
http://arxiv.org/abs/1906.07809v1
• [cs.AI]Memetic EDA-Based Approaches to Comprehensive Quality-Aware Automated Semantic Web Service Composition
Chen Wang, Hui Ma, Gang Chen, Sven Hartmann
http://arxiv.org/abs/1906.07900v1
• [cs.AI]Novelty Messages Filtering for Multi Agent Privacy-preserving Planning
Alfonso E. Gerevini, Nir Lipovetzky, Nico Peli, Francesco Percassi, Alessandro Saetti, Ivan Serina
http://arxiv.org/abs/1906.08061v1
• [cs.AI]Solving Multiagent Planning Problems with Concurrent Conditional Effects
Daniel Furelos-Blanco, Anders Jonsson
http://arxiv.org/abs/1906.08157v1
• [cs.CL]Adaptation of Machine Translation Models with Back-translated Data using Transductive Data Selection Methods
Alberto Poncelas, Gideon Maillette de Buy Wenniger, Andy Way
http://arxiv.org/abs/1906.07808v1
• [cs.CL]Attention Guided Graph Convolutional Networks for Relation Extraction
Zhijiang Guo, Yan Zhang, Wei Lu
http://arxiv.org/abs/1906.07510v2
• [cs.CL]Code-Switching Detection Using ASR-Generated Language Posteriors
Qinyi Wang, Emre Yılmaz, Adem Derinel, Haizhou Li
http://arxiv.org/abs/1906.08003v1
• [cs.CL]EditNTS: An Neural Programmer-Interpreter Model for Sentence Simplification through Explicit Editing
Yue Dong, Zichao Li, Mehdi Rezagholizadeh, Jackie Chi Kit Cheung
http://arxiv.org/abs/1906.08104v1
• [cs.CL]Expressing Visual Relationships via Language
Hao Tan, Franck Dernoncourt, Zhe Lin, Trung Bui, Mohit Bansal
http://arxiv.org/abs/1906.07689v2
• [cs.CL]Large-Scale Speaker Diarization of Radio Broadcast Archives
Emre Yılmaz, Adem Derinel, Zhou Kun, Henk van den Heuvel, Niko Brummer, Haizhou Li, David A. van Leeuwen
http://arxiv.org/abs/1906.07955v1
• [cs.CL]Multilingual Multi-Domain Adaptation Approaches for Neural Machine Translation
Chenhui Chu, Raj Dabre
http://arxiv.org/abs/1906.07978v1
• [cs.CL]Multimodal Abstractive Summarization for How2 Videos
Shruti Palaskar, Jindrich Libovický, Spandana Gella, Florian Metze
http://arxiv.org/abs/1906.07901v1
• [cs.CL]Pre-Training with Whole Word Masking for Chinese BERT
Yiming Cui, Wanxiang Che, Ting Liu, Bing Qin, Ziqing Yang, Shijin Wang, Guoping Hu
http://arxiv.org/abs/1906.08101v1
• [cs.CL]Second-Order Semantic Dependency Parsing with End-to-End Neural Networks
Xinyu Wang, Jingxian Huang, Kewei Tu
http://arxiv.org/abs/1906.07880v1
• [cs.CL]Surf at MEDIQA 2019: Improving Performance of Natural Language Inference in the Clinical Domain by Adopting Pre-trained Language Model
Jiin Nam, Seunghyun Yoon, Kyomin Jung
http://arxiv.org/abs/1906.07854v1
• [cs.CL]The Effect of Translationese in Machine Translation Test Sets
Mike Zhang, Antonio Toral
http://arxiv.org/abs/1906.08069v1
• [cs.CL]XLNet: Generalized Autoregressive Pretraining for Language Understanding
Zhilin Yang, Zihang Dai, Yiming Yang, Jaime Carbonell, Ruslan Salakhutdinov, Quoc V. Le
http://arxiv.org/abs/1906.08237v1
• [cs.CR]BitcoinHeist: Topological Data Analysis for Ransomware Detection on the Bitcoin Blockchain
Cuneyt Gurcan Akcora, Yitao Li, Yulia R. Gel, Murat Kantarcioglu
http://arxiv.org/abs/1906.07852v1
• [cs.CV]A generative approach to unsupervised deep local learning
Changlu Chen, Chaoxi Niu, Xia Zhan, Kun Zhan
http://arxiv.org/abs/1906.07947v1
• [cs.CV]A simple and effective postprocessing method for image classification
Yan Liu, Yun Li, Yunhao Yuan, jipeng qiang
http://arxiv.org/abs/1906.07934v1
• [cs.CV]An Action Recognition network for specific target based on rMC and RPN
Mingjie Li, Youqian Feng, Zhonghai Yin, Cheng Zhou, Fanghao Dong, Yuan Lin, Yuhao Dong
http://arxiv.org/abs/1906.07944v1
• [cs.CV]Analytical Derivatives for Differentiable Renderer: 3D Pose Estimation by Silhouette Consistency
Zaiqiang Wu, Wei Jiang
http://arxiv.org/abs/1906.07870v1
• [cs.CV]Artistic Enhancement and Style Transfer of Image Edges using Directional Pseudo-coloring
Shouvik Mani
http://arxiv.org/abs/1906.07981v1
• [cs.CV]Automatic Scale Estimation of Structure from Motion based 3D Models using Laser Scalers
Klemen Istenic, Nuno Gracias, Aurelien Arnaubec, Javier Escartin, Rafael Garcia
http://arxiv.org/abs/1906.08019v1
• [cs.CV]Cloud-based Image Classification Service Is Not Robust To Simple Transformations: A Forgotten Battlefield
Dou Goodman, Tao Wei
http://arxiv.org/abs/1906.07997v1
• [cs.CV]Crop Lodging Prediction from UAV-Acquired Images of Wheat and Canola using a DCNN Augmented with Handcrafted Texture Features
Sara Mardanisamani, Farhad Maleki, Sara Hosseinzadeh Kassani, Sajith Rajapaksa, Hema Duddu, Menglu Wang, Steve Shirtliffe, Seungbum Ryu, Anique Josuttes, Ti Zhang, Sally Vail, Curtis Pozniak, Isobel Parkin, Ian Stavness, Mark Eramian
http://arxiv.org/abs/1906.07771v1
• [cs.CV]Event-based Star Tracking via Multiresolution Progressive Hough Transforms
Tat-Jun Chin, Samya Bagchi
http://arxiv.org/abs/1906.07866v1
• [cs.CV]Extended probabilistic Rand index and the adjustable moving window-based pixel-pair sampling method
Hisashi Shimodaira
http://arxiv.org/abs/1906.07893v1
• [cs.CV]Imbalanced Learning-based Automatic SAR Images Change Detection by Morphologically Supervised PCA-Net
Rongfang Wang, Jie Zhang, Jia-Wei Chen, Licheng Jiao, Mi Wang
http://arxiv.org/abs/1906.07923v1
• [cs.CV]Key Instance Selection for Unsupervised Video Object Segmentation
Donghyeon Cho, Sungeun Hong, Sungil Kang, Jiwon Kim
http://arxiv.org/abs/1906.07851v1
• [cs.CV]Learning to Reconstruct and Understand Indoor Scenes from Sparse Views
Jingyu Yang, Ji Xu, Kun Li, Yu-Kun Lai, Huanjing Yue, Jianzhi Lu, Hao Wu, Yebin Liu
http://arxiv.org/abs/1906.07892v1
• [cs.CV]Model-based Deep MR Imaging: the roadmap of generalizing compressed sensing model using deep learning
Jing Cheng, Haifeng Wang, Yanjie Zhu, Qiegen Liu, Leslie Ying, Dong Liang
http://arxiv.org/abs/1906.08143v1
• [cs.CV]Monocular 3D Object Detection and Box Fitting Trained End-to-End Using Intersection-over-Union Loss
Eskil Jörgensen, Christopher Zach, Fredrik Kahl
http://arxiv.org/abs/1906.08070v1
• [cs.CV]Neural Point-Based Graphics
Kara-Ali Aliev, Dmitry Ulyanov, Victor Lempitsky
http://arxiv.org/abs/1906.08240v1
• [cs.CV]PoseConvGRU: A Monocular Approach for Visual Ego-motion Estimation by Learning
Guangyao Zhai, Liang Liu, Linjian Zhang, Yong Liu
http://arxiv.org/abs/1906.08095v1
• [cs.CV]Quantifying and Leveraging Classification Uncertainty for Chest Radiograph Assessment
Florin C. Ghesu, Bogdan Georgescu, Eli Gibson, Sebastian Guendel, Mannudeep K. Kalra, Ramandeep Singh, Subba R. Digumarthy, Sasa Grbic, Dorin Comaniciu
http://arxiv.org/abs/1906.07775v1
• [cs.CV]SAR Image Change Detection via Spatial Metric Learning with an Improved Mahalanobis Distance
Rongfang Wang, Jia-Wei Chen, Yule Wang, Licheng Jiao, Mi Wang
http://arxiv.org/abs/1906.07930v1
• [cs.CV]SEN12MS — A Curated Dataset of Georeferenced Multi-Spectral Sentinel-1/2 Imagery for Deep Learning and Data Fusion
Michael Schmitt, Lloyd Haydn Hughes, Chunping Qiu, Xiao Xiang Zhu
http://arxiv.org/abs/1906.07789v1
• [cs.CV]Tumor Saliency Estimation for Breast Ultrasound Images via Breast Anatomy Modeling
Fei Xu, Yingtao Zhang, Min Xian, H. D. Cheng, Boyu Zhang, Jianrui Ding, Chunping Ning, Ying Wang
http://arxiv.org/abs/1906.07760v1
• [cs.CV]Unsupervised Learning of Object Structure and Dynamics from Videos
Matthias Minderer, Chen Sun, Ruben Villegas, Forrester Cole, Kevin Murphy, Honglak Lee
http://arxiv.org/abs/1906.07889v1
• [cs.CV]ViP: Virtual Pooling for Accelerating CNN-based Image Classification and Object Detection
Zhuo Chen, Jiyuan Zhang, Ruizhou Ding, Diana Marculescu
http://arxiv.org/abs/1906.07912v1
• [cs.CY]Addressing behavioral change towards energy efficiency in European educational buildings
G. Mylonas, D. Amaxilatis, H. Leligou, T. Zahariadis, E. Zacharioudakis, J. Hofstaetter, A. Friedl, F. Paganelli, G. Cuffaro, J. Lerch
http://arxiv.org/abs/1906.07960v1
• [cs.CY]Controllable Planning, Responsibility, and Information in automatic Driving Technology
Wan Dan, Zhan Hao
http://arxiv.org/abs/1906.07861v1
• [cs.CY]Ethically Aligned Design of Autonomous Systems: Industry viewpoint and an empirical study
Ville Vakkuri, Kai-Kristian Kemell, Joni Kultanen, Mikko Siponen, Pekka Abrahamsson
http://arxiv.org/abs/1906.07946v1
• [cs.CY]Subtle Censorship via Adversarial Fakeness in Kyrgyzstan
Christopher Schwartz, Rebekah Overdorf
http://arxiv.org/abs/1906.08021v1
• [cs.DB]Low-resource Deep Entity Resolution with Transfer and Active Learning
Jungo Kasai, Kun Qian, Sairam Gurajada, Yunyao Li, Lucian Popa
http://arxiv.org/abs/1906.08042v1
• [cs.DB]The Linked Open Data cloud is more abstract, flatter and less linked than you may think!
Luigi Asprino, Wouter Beek, Paolo Ciancarini, Frank van Harmelen, Valentina Presutti
http://arxiv.org/abs/1906.08097v1
• [cs.DC]A Static Analysis-based Cross-Architecture Performance Prediction Using Machine Learning
Newsha Ardalani, Urmish Thakker, Aws Albarghouthi, Karu Sankaralingam
http://arxiv.org/abs/1906.07840v1
• [cs.DC]An Outlier-aware Consensus Protocol for Blockchain-based IoT Networks Using Hyperledger Fabric
Mehrdad Salimitari, Mohsen Joneidi, Mainak Chatterjee
http://arxiv.org/abs/1906.08177v1
• [cs.DC]From Facility to Application Sensor Data: Modular, Continuous and Holistic Monitoring with DCDB
Alessio Netti, Micha Mueller, Axel Auweter, Carla Guillen, Michael Ott, Daniele Tafani, Martin Schulz
http://arxiv.org/abs/1906.07509v2
• [cs.DC]MaGPoS — A novel decentralized consensus mechanism combining magnetism and proof of stake
Tommy Mckinnon
http://arxiv.org/abs/1906.08176v1
• [cs.DC]MediaPipe: A Framework for Building Perception Pipelines
Camillo Lugaresi, Jiuqiang Tang, Hadon Nash, Chris McClanahan, Esha Uboweja, Michael Hays, Fan Zhang, Chuo-Ling Chang, Ming Guang Yong, Juhyun Lee, Wan-Teh Chang, Wei Hua, Manfred Georg, Matthias Grundmann
http://arxiv.org/abs/1906.08172v1
• [cs.DC]Reduced I/O Latency with Futures
Kyle Singer, Kunal Agrawal, I-Ting Angelina Lee
http://arxiv.org/abs/1906.08239v1
• [cs.DC]SeeMoRe: A Fault-Tolerant Protocol for Hybrid Cloud Environments
Mohammad Javad Amiri, Sujaya Maiyya, Divyakant Agrawal, Amr El Abbadi
http://arxiv.org/abs/1906.07850v1
• [cs.DC]Write-Optimized and Consistent RDMA-based NVM Systems
Xinxin Liu, Yu Hua, Xuan Li, Qifan Liu
http://arxiv.org/abs/1906.08173v1
• [cs.DL]Gender trends in computer science authorship
Lucy Lu Wang, Gabriel Stanovsky, Luca Weihs, Oren Etzioni
http://arxiv.org/abs/1906.07883v1
• [cs.DS]Indexing Graph Search Trees and Applications
Sankardeep Chakraborty, Kunihiko Sadakane
http://arxiv.org/abs/1906.07871v1
• [cs.DS]On the Constrained Least-cost Tour Problem
Patrick O’Hara, M. S. Ramanujan, Theodoros Damoulas
http://arxiv.org/abs/1906.07754v1
• [cs.DS]Space Efficient Algorithms for Breadth-Depth Search
Sankardeep Chakraborty, Anish Mukherjee, Srinivasa Rao Satti
http://arxiv.org/abs/1906.07874v1
• [cs.DS]Tutorial on algebraic deletion correction codes
Kedar Tatwawadi, Shubham Chandak
http://arxiv.org/abs/1906.07887v1
• [cs.FL]Learning with Partially Ordered Representations
Jane Chandlee, Remi Eyraud, Jeffrey Heinz, Adam Jardine, Jonathan Rawski
http://arxiv.org/abs/1906.07886v1
• [cs.GR]Neural Volumes: Learning Dynamic Renderable Volumes from Images
Stephen Lombardi, Tomas Simon, Jason Saragih, Gabriel Schwartz, Andreas Lehrmann, Yaser Sheikh
http://arxiv.org/abs/1906.07751v1
• [cs.GT]Who is in Your Top Three? Optimizing Learning in Elections with Many Candidates
Nikhil Garg, Lodewijk Gelauff, Sukolsak Sakshuwong, Ashish Goel
http://arxiv.org/abs/1906.08160v1
• [cs.IR]A survey of OpenRefine reconciliation services
Antonin Delpeuch
http://arxiv.org/abs/1906.08092v1
• [cs.IR]Domain Adaptation for Enterprise Email Search
Brandon Tran, Maryam Karimzadehgan, Rama Kumar Pasumarthi, Michael Bendersky, Donald Metzler
http://arxiv.org/abs/1906.07897v1
• [cs.IR]SEntNet: Source-aware Recurrent Entity Network for Dialogue Response Selection
Jiahuan Pei, Arent Stienstra, Julia Kiseleva, Maarten de Rijke
http://arxiv.org/abs/1906.06788v2
• [cs.IT]Cooperative Rate-Splitting for MISO Broadcast Channel with User Relaying
Jian Zhang, Bruno Clerckx, Jianhua Ge, Yijie Mao
http://arxiv.org/abs/1906.07761v1
• [cs.IT]Joint Learning of Geometric and Probabilistic Constellation Shaping
Maximilian Stark, Fayçal Ait Aoudia, Jakob Hoydis
http://arxiv.org/abs/1906.07748v1
• [cs.IT]Linear Complexity of A Family of Binary $pq^2$-periodic Sequences From Euler Quotients
Jingwei Zhang, Shuhong Gao, Chang-An Zhao
http://arxiv.org/abs/1906.08083v1
• [cs.IT]Low Probability of Detection Communication: Opportunities and Challenges
Shihao Yan, Xiangyun Zhou, Jinsong Hu, Stephen V. Hanly
http://arxiv.org/abs/1906.07895v1
• [cs.IT]MPEG-2 Prediction Residue Analysis
David Vázquez-Padín, Fernando Pérez-González
http://arxiv.org/abs/1906.07003v1
• [cs.IT]The Effect of Spatial Correlation on the Performance of Uplink and Downlink Single-Carrier Massive MIMO Systems
Nader Beigiparast, Gokhan M. Guvensen, Ender Ayanoglu
http://arxiv.org/abs/1906.07766v1
• [cs.IT]Time Synchronization in 5G Wireless Edge: Requirements and Solutions for Critical-MTC
Aamir Mahmood, Muhammad Ikram Ashraf, Mikael Gidlund, Johan Torsner, Joachim Sachs
http://arxiv.org/abs/1906.06380v1
• [cs.LG]A unified view on differential privacy and robustness to adversarial examples
Rafael Pinot, Florian Yger, Cédric Gouy-Pailler, Jamal Atif
http://arxiv.org/abs/1906.07982v1
• [cs.LG]Adapting Behaviour via Intrinsic Reward: A Survey and Empirical Study
Cam Linke, Nadia M. Ady, Martha White, Thomas Degris, Adam White
http://arxiv.org/abs/1906.07865v1
• [cs.LG]Adaptive Temporal-Difference Learning for Policy Evaluation with Per-State Uncertainty Estimates
Hugo Penedones, Carlos Riquelme, Damien Vincent, Hartmut Maennel, Timothy Mann, Andre Barreto, Sylvain Gelly, Gergely Neu
http://arxiv.org/abs/1906.07987v1
• [cs.LG]Adversarial Task-Specific Privacy Preservation under Attribute Attack
Han Zhao, Jianfeng Chi, Yuan Tian, Geoffrey J. Gordon
http://arxiv.org/abs/1906.07902v1
• [cs.LG]Agnostic data debiasing through a local sanitizer learnt from an adversarial network approach
Ulrich Aïvodji, François Bidet, Sébastien Gambs, Rosin Claude Ngueveu, Alain Tapp
http://arxiv.org/abs/1906.07858v1
• [cs.LG]Automatic Source Code Summarization with Extended Tree-LSTM
Yusuke Shido, Yasuaki Kobayashi, Akihiro Yamamoto, Atsushi Miyamoto, Tadayuki Matsumura
http://arxiv.org/abs/1906.08094v1
• [cs.LG]Barron Spaces and the Compositional Function Spaces for Neural Network Models
Weinan E, Chao Ma, Lei Wu
http://arxiv.org/abs/1906.08039v1
• [cs.LG]Batch Active Learning Using Determinantal Point Processes
Erdem Bıyık, Kenneth Wang, Nima Anari, Dorsa Sadigh
http://arxiv.org/abs/1906.07975v1
• [cs.LG]BatchBALD: Efficient and Diverse Batch Acquisition for Deep Bayesian Active Learning
Andreas Kirsch, Joost van Amersfoort, Yarin Gal
http://arxiv.org/abs/1906.08158v1
• [cs.LG]Clustering with Fairness Constraints: A Flexible and Scalable Approach
Imtiaz Masud Ziko, Eric Granger, Jing Yuan, Ismail Ben Ayed
http://arxiv.org/abs/1906.08207v1
• [cs.LG]Constrained Bilinear Factorization Multi-view Subspace Clustering
Qinghai Zheng, Jihua Zhu, Zhiqiang Tian, Zhongyu Li, Shanmin Pang, Xiuyi Jia
http://arxiv.org/abs/1906.08107v1
• [cs.LG]Control What You Can: Intrinsically Motivated Task-Planning Agent
Sebastian Blaes, Marin Vlastelica Pogančić, Jia-Jie Zhu, Georg Martius
http://arxiv.org/abs/1906.08190v1
• [cs.LG]Convergence of Adversarial Training in Overparametrized Networks
Ruiqi Gao, Tianle Cai, Haochuan Li, Liwei Wang, Cho-Jui Hsieh, Jason D. Lee
http://arxiv.org/abs/1906.07916v1
• [cs.LG]Deep Learning-Based Quantization of L-Values for Gray-Coded Modulation
Marius Arvinte, Sriram Vishwanath, Ahmed H. Tewfik
http://arxiv.org/abs/1906.07849v1
• [cs.LG]Directed Exploration for Reinforcement Learning
Zhaohan Daniel Guo, Emma Brunskill
http://arxiv.org/abs/1906.07805v1
• [cs.LG]Discovery of Physics from Data: Universal Laws and Discrepancy Models
Brian de Silva, David M. Higdon, Steven L. Brunton, J. Nathan Kutz
http://arxiv.org/abs/1906.07906v1
• [cs.LG]Disentangling feature and lazy learning in deep neural networks: an empirical study
Mario Geiger, Stefano Spigler, Arthur Jacot, Matthieu Wyart
http://arxiv.org/abs/1906.08034v1
• [cs.LG]Efficient Algorithms for Set-Valued Prediction in Multi-Class Classification
Thomas Mortier, Marek Wydmuch, Eyke Hüllermeier, Krzysztof Dembczyński, Willem Waegeman
http://arxiv.org/abs/1906.08129v1
• [cs.LG]Evaluating Protein Transfer Learning with TAPE
Roshan Rao, Nicholas Bhattacharya, Neil Thomas, Yan Duan, Xi Chen, John Canny, Pieter Abbeel, Yun S. Song
http://arxiv.org/abs/1906.08230v1
• [cs.LG]Generative Restricted Kernel Machines
Arun Pandey, Joachim Schreurs, Johan A. K. Suykens
http://arxiv.org/abs/1906.08144v1
• [cs.LG]Global Adversarial Attacks for Assessing Deep Learning Robustness
Hanbin Hu, Mit Shah, Jianhua Z. Huang, Peng Li
http://arxiv.org/abs/1906.07920v1
• [cs.LG]Gradient Dynamics of Shallow Univariate ReLU Networks
Francis Williams, Matthew Trager, Claudio Silva, Daniele Panozzo, Denis Zorin, Joan Bruna
http://arxiv.org/abs/1906.07842v1
• [cs.LG]Hill Climbing on Value Estimates for Search-control in Dyna
Yangchen Pan, Hengshuai Yao, Amir-massoud Farahmand, Martha White
http://arxiv.org/abs/1906.07791v1
• [cs.LG]Information matrices and generalization
Valentin Thomas, Fabian Pedregosa, Bart van Merriënboer, Pierre-Antoine Mangazol, Yoshua Bengio, Nicolas Le Roux
http://arxiv.org/abs/1906.07774v1
• [cs.LG]Inverting Deep Generative models, One layer at a time
Qi Lei, Ajil Jalal, Inderjit S. Dhillon, Alexandros G. Dimakis
http://arxiv.org/abs/1906.07437v2
• [cs.LG]Joint Pruning on Activations and Weights for Efficient Neural Networks
Qing Yang, Wei Wen, Zuoguan Wang, Hai Li
http://arxiv.org/abs/1906.07875v1
• [cs.LG]LIA: Latently Invertible Autoencoder with Adversarial Learning
Jiapeng Zhu, Deli Zhao, Bo Zhang
http://arxiv.org/abs/1906.08090v1
• [cs.LG]Learning Directed Graphical Models from Gaussian Data
Katherine Fitch
http://arxiv.org/abs/1906.08050v1
• [cs.LG]Learning Disentangled Representations of Timbre and Pitch for Musical Instrument Sounds Using Gaussian Mixture Variational Autoencoders
Yin-Jyun Luo, Kat Agres, Dorien Herremans
http://arxiv.org/abs/1906.08152v1
• [cs.LG]Learning in Restless Multi-Armed Bandits via Adaptive Arm Sequencing Rules
Tomer Gafni, Kobi Cohen
http://arxiv.org/abs/1906.08120v1
• [cs.LG]On the Robustness of the Backdoor-based Watermarking in Deep Neural Networks
Masoumeh Shafieinejad, Jiaqi Wang, Nils Lukas, Florian Kerschbaum
http://arxiv.org/abs/1906.07745v1
• [cs.LG]Poisoning Attacks with Generative Adversarial Nets
Luis Muñoz-González, Bjarne Pfitzner, Matteo Russo, Javier Carnerero-Cano, Emil C. Lupu
http://arxiv.org/abs/1906.07773v1
• [cs.LG]Provable Gradient Variance Guarantees for Black-Box Variational Inference
Justin Domke
http://arxiv.org/abs/1906.08241v1
• [cs.LG]QXplore: Q-learning Exploration by Maximizing Temporal Difference Error
Riley Simmons-Edler, Ben Eisner, Eric Mitchell, Sebastian Seung, Daniel Lee
http://arxiv.org/abs/1906.08189v1
• [cs.LG]RadGrad: Active learning with loss gradients
Paul Budnarain, Renato Ferreira Pinto Junior, Ilan Kogan
http://arxiv.org/abs/1906.07838v1
• [cs.LG]SemanticAdv: Generating Adversarial Examples via Attribute-conditional Image Editing
Haonan Qiu, Chaowei Xiao, Lei Yang, Xinchen Yan, Honglak Lee, Bo Li
http://arxiv.org/abs/1906.07927v1
• [cs.LG]Supervised Hierarchical Clustering with Exponential Linkage
Nishant Yadav, Ari Kobren, Nicholas Monath, Andrew McCallum
http://arxiv.org/abs/1906.07859v1
• [cs.LG]Transfer NAS: Knowledge Transfer between Search Spaces with Transformer Agents
Zalán Borsos, Andrey Khorlin, Andrea Gesmundo
http://arxiv.org/abs/1906.08102v1
• [cs.LG]Unsupervised State Representation Learning in Atari
Ankesh Anand, Evan Racah, Sherjil Ozair, Yoshua Bengio, Marc-Alexandre Côté, R Devon Hjelm
http://arxiv.org/abs/1906.08226v1
• [cs.LG]Wasserstein Adversarial Imitation Learning
Huang Xiao, Michael Herman, Joerg Wagner, Sebastian Ziesche, Jalal Etesami, Thai Hong Linh
http://arxiv.org/abs/1906.08113v1
• [cs.LG]When to Trust Your Model: Model-Based Policy Optimization
Michael Janner, Justin Fu, Marvin Zhang, Sergey Levine
http://arxiv.org/abs/1906.08253v1
• [cs.LG]XNAS: Neural Architecture Search with Expert Advice
Niv Nayman, Asaf Noy, Tal Ridnik, Itamar Friedman, Rong Jin, Lihi Zelnik-Manor
http://arxiv.org/abs/1906.08031v1
• [cs.NE]A general methodology to assess symbolic regression algorithms using the generation of random equations with uniform random sampling
Sohrab Towfighi
http://arxiv.org/abs/1906.07848v1
• [cs.RO]A Grasping-centered Analysis for Cloth Manipulation
Júlia Borràs, Guillem Alenya, Carme Torras
http://arxiv.org/abs/1906.08202v1
• [cs.RO]Characterizing the Uncertainty of Jointly Distributed Poses in the Lie Algebra
Joshua G. Mangelson, Maani Ghaffari, Ram Vasudevan, Ryan M. Eustice
http://arxiv.org/abs/1906.07795v1
• [cs.RO]Development of a robotic system for automatic organic chemistry synthesis
Joyce Xin-Yan Lim, Dasheng Leow, Quang-Cuong Pham, Choon-Hong Tan
http://arxiv.org/abs/1906.07939v1
• [cs.RO]Providentia — A Large Scale Sensing System for the Assistance of Autonomous Vehicles
Annkathrin Krämmer, Christoph Schöller, Dhiraj Gulati, Alois Knoll
http://arxiv.org/abs/1906.06789v3
• [cs.RO]PyRobot: An Open-source Robotics Framework for Research and Benchmarking
Adithyavairavan Murali, Tao Chen, Kalyan Vasudev Alwala, Dhiraj Gandhi, Lerrel Pinto, Saurabh Gupta, Abhinav Gupta
http://arxiv.org/abs/1906.08236v1
• [cs.RO]Standalone and RTK GNSS on 30,000 km of North American Highways
Tyler G. R. Reid, Nahid Pervez, Umair Ibrahim, Sarah E. Houts, Gaurav Pandey, Naveen K. R. Alla, Andy Hsia
http://arxiv.org/abs/1906.08180v1
• [cs.SE]Ethical Interviews in Software Engineering
Per Erik Strandberg
http://arxiv.org/abs/1906.07993v1
• [cs.SI]Detecting problematic transactions in a consumer-to-consumer e-commerce network
Shun Kodate, Ryusuke Chiba, Shunya Kimura, Naoki Masuda
http://arxiv.org/abs/1906.07974v1
• [cs.SI]Predicting Drug Responses by Propagating Interactions through Text-Enhanced Drug-Gene Networks
Shiyin Wang
http://arxiv.org/abs/1906.08089v1
• [cs.SI]Predicting Evacuation Decisions using Representations of Individuals’ Pre-Disaster Web Search Behavior
Takahiro Yabe, Kota Tsubouchi, Toru Shimizu, Yoshihide Sekimoto, Satish V. Ukkusuri
http://arxiv.org/abs/1906.07770v1
• [econ.EM]From Local to Global: External Validity in a Fertility Natural Experiment
Rajeev Dehejia, Cristian Pop-Eleches, Cyrus Samii
http://arxiv.org/abs/1906.08096v1
• [eess.AS]Multi-Stream End-to-End Speech Recognition
Ruizhi Li, Xiaofei Wang, Sri Harish Mallidi, Shinji Watanabe, Takaaki Hori, Hynek Hermansky
http://arxiv.org/abs/1906.08041v1
• [eess.AS]Real to H-space Encoder for Speech Recognition
Titouan Parcollet, Mohamed Morchid, Georges Linarès, Renato De Mori
http://arxiv.org/abs/1906.08043v1
• [eess.AS]Robust End to End Speaker Verification Using EEG
Yan Han, Gautam Krishna, Co Tran, Mason Carnahan, Ahmed H Tewfik
http://arxiv.org/abs/1906.08044v1
• [eess.AS]Speech Recognition With No Speech Or With Noisy Speech Beyond English
Gautam Krishna, Co Tran, Yan Han, Mason Carnahan, Ahmed H Tewfik
http://arxiv.org/abs/1906.08045v1
• [eess.AS]The Second DIHARD Diarization Challenge: Dataset, task, and baselines
Neville Ryant, Kenneth Church, Christopher Cieri, Alejandrina Cristia, Jun Du, Sriram Ganapathy, Mark Liberman
http://arxiv.org/abs/1906.07839v1
• [eess.IV]Automated Computer Evaluation of Acute Ischemic Stroke and Large Vessel Occlusion
Jia You, Philip L. H. Yu, Anderson C. O. Tsang, Eva L. H. Tsui, Pauline P. S. Woo, Gilberto K. K. Leung
http://arxiv.org/abs/1906.08059v1
• [eess.IV]Image Super Resolution via Bilinear Pooling: Application to Confocal Endomicroscopy
Saeed Izadi, Darren Sutton, Ghassan Hamarneh
http://arxiv.org/abs/1906.07802v1
• [eess.SP]Multi-user Resource Control with Deep Reinforcement Learning in IoT Edge Computing
Lei Lei, Huijuan Xu, Xiong Xiong, Kan Zheng, Wei Xiang, Xianbin Wang
http://arxiv.org/abs/1906.07860v1
• [eess.SY]Clock synchronization over networks — Identifiability of the sawtooth model
Pol del Aguila Pla, Lissy Pellaco, Satyam Dwivedi, Peter Händel, Joakim Jaldén
http://arxiv.org/abs/1906.08208v1
• [eess.SY]Joint Optimization of Transmission and Propulsion in UAV-Assisted Communication Networks
Omar J. Faqir, Eric C. Kerrigan, Deniz Gündüz, Yuanbo Nie
http://arxiv.org/abs/1906.08024v1
• [math.NA]A scalable multilevel domain decomposition preconditioner with a subspace-based coarsening algorithm for the neutron transport calculations
Fande Kong, Yaqi Wang, Derek R. Gaston, Alexander D. Lindsay, Cody J. Permann, Richard C. Martineau
http://arxiv.org/abs/1906.07743v1
• [math.NA]Sparse approximate matrix multiplication in a fully recursive distributed task-based parallel framework
Anton G. Artemov
http://arxiv.org/abs/1906.08148v1
• [math.OC]Engineering and Business Applications of Sum of Squares Polynomials
Georgina Hall
http://arxiv.org/abs/1906.07961v1
• [math.OC]Locally Accelerated Conditional Gradients
Alejandro Carderera, Jelena Diakonikolas, Sebastian Pokutta
http://arxiv.org/abs/1906.07867v1
• [math.ST]Bump detection in the presence of dependency: Does it ease or does it load?
Farida Enikeeva, Axel Munk, Markus Pohlmann, Frank Werner
http://arxiv.org/abs/1906.08017v1
• [math.ST]Central limit theorem for a partially observed interacting system of Hawkes processes
Chenguang Liu
http://arxiv.org/abs/1906.08080v1
• [math.ST]Improper vs finitely additive distributions as limits of countably additive probabilities
Erwan Saint Loubert Bié, Pierre Druilhet, Erwan Saint, Loubert Bié
http://arxiv.org/abs/1906.07530v2
• [math.ST]Knowledge Gradient for Selection with Covariates: Consistency and Computation
Xiaowei Zhang, Haihui Shen, L. Jeff Hong, Liang Ding
http://arxiv.org/abs/1906.05098v2
• [math.ST]Rate-optimal estimation of the Blumenthal-Getoor index of a Lévy process
Fabian Mies
http://arxiv.org/abs/1906.08062v1
• [math.ST]Recovering low-rank structure from multiple networks with unknown edge distributions
Keith Levin, Asad Lodhia, Elizaveta Levina
http://arxiv.org/abs/1906.07265v1
• [math.ST]Safe Testing
Peter Grünwald, Rianne de Heide, Wouter Koolen
http://arxiv.org/abs/1906.07801v1
• [math.ST]Variances of surface area estimators based on pixel configuration counts
Jürgen Kampf
http://arxiv.org/abs/1906.07972v1
• [q-bio.GN]Convolutional neural network models for cancer type prediction based on gene expression
Milad Mostavi, Yu-Chiao Chiu, Yufei Huang, Yidong Chen
http://arxiv.org/abs/1906.07794v1
• [q-bio.QM]Automatic estimation of heading date of paddy rice using deep learning
Sai Vikas Desai, Vineeth N Balasubramanian, Tokihiro Fukatsu, Seishi Ninomiya, Wei Guo
http://arxiv.org/abs/1906.07917v1
• [q-bio.TO]Automated Definition of Skeletal Disease Burden in Metastatic Prostate Carcinoma: a 3D analysis of SPECT/CT images
Francesco Fiz, Helmut Dittmann, Cristina Campi, Matthias Weissinger, Samine Sahbai, Matthias Reimold, Arnulf Stenzl, Michele Piana, Gianmario Sambuceti, Christian la Fougère
http://arxiv.org/abs/1906.08200v1
• [stat.AP]Killings of social leaders in the Colombian post-conflict: Data analysis for investigative journalism
Maria De-Arteaga, Benedikt Boecking
http://arxiv.org/abs/1906.08206v1
• [stat.AP]Learning data representation using modified autoencoder for the integrative analysis of multi-omics data
Tianwei Yu
http://arxiv.org/abs/1906.07800v1
• [stat.CO]Bayesian inverse regression for supervised dimension reduction with small datasets
Xin Cai, Guang Lin, Jinglai Li
http://arxiv.org/abs/1906.08018v1
• [stat.ME]A spatial dependence graph model for multivariate spatial hybrid processes
Matthias Eckardt, Jorge Mateu
http://arxiv.org/abs/1906.07798v1
• [stat.ME]Estimation of Markovian-regime-switching models with independent regimes
Nigel Bean, Angus Lewis, Giang Nguyen
http://arxiv.org/abs/1906.07957v1
• [stat.ME]Finite sample properties of the Buckland-Burnham-Augustin confidence interval centered on a model averaged estimator
Paul Kabaila, Alan H. Welsh, Christeen Wijethunga
http://arxiv.org/abs/1906.07933v1
• [stat.ME]Multiple Testing Embedded in an Aggregation Tree to Identify where Two Distributions Differ
John Pura, Cliburn Chan, Jichun Xie
http://arxiv.org/abs/1906.07757v1
• [stat.ME]Should Observations be Grouped for Effective Monitoring of Multivariate Process Variability?
Jimoh Olawale Ajadi, Inez Maria Zwetsloot
http://arxiv.org/abs/1906.08038v1
• [stat.ME]Vecchia-Laplace approximations of generalized Gaussian processes for big non-Gaussian spatial data
Daniel Zilber, Matthias Katzfuss
http://arxiv.org/abs/1906.07828v1
• [stat.ML]Explanations can be manipulated and geometry is to blame
Ann-Kathrin Dombrowski, Maximilian Alber, Christopher J. Anders, Marcel Ackermann, Klaus-Robert Müller, Pan Kessel
http://arxiv.org/abs/1906.07983v1
• [stat.ML]Identification and Estimation of Hierarchical Latent Attribute Models
Yuqi Gu, Gongjun Xu
http://arxiv.org/abs/1906.07869v1
• [stat.ML]Kernel quadrature with DPPs
Ayoub Belhadji, Rémi Bardenet, Pierre Chainais
http://arxiv.org/abs/1906.07832v1
• [stat.ML]Local Bures-Wasserstein Transport: A Practical and Fast Mapping Approximation
Andrés Hoyos-Idrobo
http://arxiv.org/abs/1906.08227v1
• [stat.ML]Semi-supervised Logistic Learning Based on Exponential Tilt Mixture Models
Xinwei Zhang, Zhiqiang Tan
http://arxiv.org/abs/1906.07882v1
• [stat.ML]Stochastic Runge-Kutta Accelerates Langevin Monte Carlo and Beyond
Xuechen Li, Denny Wu, Lester Mackey, Murat A. Erdogdu
http://arxiv.org/abs/1906.07868v1
• [stat.ML]Variational Gaussian Processes with Signature Covariances
Csaba Toth, Harald Oberhauser
http://arxiv.org/abs/1906.08215v1