astro-ph.SR - 太阳和天体物理学恒星
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.GT - 计算机科学与博弈论 cs.HC - 人机接口 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.NI - 网络和互联网体系结构 cs.RO - 机器人学 cs.SY - 系统与控制 eess.AS - 语音处理 eess.IV - 图像与视频处理 eess.SP - 信号处理 math.ST - 统计理论 nlin.AO - 适应和自组织系统 q-bio.PE - 人口与发展 stat.AP - 应用统计 stat.ME - 统计方法论 stat.ML - (统计)机器学习
• [astro-ph.SR]Predicting Solar Flares Using a Long Short-Term Memory Network
• [cs.AI]A Correctness Result for Synthesizing Plans With Loops in Stochastic Domains
• [cs.AI]How Case Based Reasoning Explained Neural Networks: An XAI Survey of Post-Hoc Explanation-by-Example in ANN-CBR Twins
• [cs.AI]Knowledge-Based Sequential Decision-Making Under Uncertainty
• [cs.CL]Adaptation of Deep Bidirectional Multilingual Transformers for Russian Language
• [cs.CL]Availability-Based Production Predicts Speakers’ Real-time Choices of Mandarin Classifiers
• [cs.CL]CHiVE: Varying Prosody in Speech Synthesis with a Linguistically Driven Dynamic Hierarchical Conditional Variational Network
• [cs.CL]Conversion Prediction Using Multi-task Conditional Attention Networks to Support the Creation of Effective Ad Creative
• [cs.CL]Distant Learning for Entity Linking with Automatic Noise Detection
• [cs.CL]Don’t Blame Distributional Semantics if it can’t do Entailment
• [cs.CL]ERNIE: Enhanced Language Representation with Informative Entities
• [cs.CL]IMHO Fine-Tuning Improves Claim Detection
• [cs.CL]Improving Question Answering over Incomplete KBs with Knowledge-Aware Reader
• [cs.CL]Learning Cross-lingual Embeddings from Twitter via Distant Supervision
• [cs.CL]Multi-hop Reading Comprehension across Multiple Documents by Reasoning over Heterogeneous Graphs
• [cs.CL]Plotting Markson’s ‘Mistress’
• [cs.CL]Towards Automatic Generation of Shareable Synthetic Clinical Notes Using Neural Language Models
• [cs.CR]A critique of the DeepSec Platform for Security Analysis of Deep Learning Models
• [cs.CR]Finding Rats in Cats: Detecting Stealthy Attacks using Group Anomaly Detection
• [cs.CR]Learning from Context: Exploiting and Interpreting File Path Information for Better Malware Detection
• [cs.CV]A deep learning approach to detecting volcano deformation from satellite imagery using synthetic datasets
• [cs.CV]AM-LFS: AutoML for Loss Function Search
• [cs.CV]CNN-based Cost Volume Analysis as Confidence Measure for Dense Matching
• [cs.CV]Group Re-Identification with Multi-grained Matching and Integration
• [cs.CV]How do neural networks see depth in single images?
• [cs.CV]LiDAR Sensor modeling and Data augmentation with GANs for Autonomous driving
• [cs.CV]Neither Global Nor Local: A Hierarchical Robust Subspace Clustering For Image Data
• [cs.CV]Neural Message Passing on Hybrid Spatio-Temporal Visual and Symbolic Graphs for Video Understanding
• [cs.CV]Non-Parametric Priors For Generative Adversarial Networks
• [cs.CV]Online Hyper-parameter Learning for Auto-Augmentation Strategy
• [cs.CV]PoreNet: CNN-based Pore Descriptor for High-resolution Fingerprint Recognition
• [cs.CV]Semantic Analysis of Traffic Camera Data: Topic Signal Extraction and Anomalous Event Detection
• [cs.CV]Side Window Filtering
• [cs.CV]Texture Fields: Learning Texture Representations in Function Space
• [cs.CV]Transfer Learning based Detection of Diabetic Retinopathy from Small Dataset
• [cs.CY]Between Discord and Deadlock: Consensus Under a Deadline
• [cs.CY]Exploration methods for simulation models
• [cs.DB]Concurrency Protocol Aiming at High Performance of Execution and Replay for Smart Contracts
• [cs.DC]Auto-tuning of dynamic load balancing applied to 3D reverse time migration on multicore systems
• [cs.DC]On the complexity of fault-tolerant consensus
• [cs.DS]Parallel decompression of gzip-compressed files and random access to DNA sequences
• [cs.DS]Separating Structure from Noise in Large Graphs Using the Regularity Lemma
• [cs.GT]Fiduciary Bandits
• [cs.HC]Are Automated Vehicles Safer than Manually Driven Cars?
• [cs.HC]MiSC: Mixed Strategies Crowdsourcing
• [cs.IR]Cleaned Similarity for Better Memory-Based Recommenders
• [cs.IR]Deep Unified Multimodal Embeddings for Understanding both Content and Users in Social Media Networks
• [cs.IR]Exact-K Recommendation via Maximal Clique Optimization
• [cs.IT]Buffer-aided Resource Allocation for a Price Based Opportunistic Cognitive Radio Network
• [cs.IT]Discussions on Signal Uncertainty Principle in Shannon Channel Capacity Equation and Research on Breaking Shannon Limit Method
• [cs.IT]Joint Frequency-and-Phase Modulation for Backscatter-Tag Assisted Vehicular Positioning
• [cs.IT]Optimal Status Updating with a Finite-Battery Energy Harvesting Source
• [cs.LG]An Essay on Optimization Mystery of Deep Learning
• [cs.LG]Biosignal Generation and Latent Variable Analysis with Recurrent Generative Adversarial Networks
• [cs.LG]Comparison-Based Framework for Psychophysics: Lab versus Crowdsourcing
• [cs.LG]Contrastive Fairness in Machine Learning
• [cs.LG]Decision-Oriented Communications: Application to Energy-Efficient Resource Allocation
• [cs.LG]DeepSwarm: Optimising Convolutional Neural Networks using Swarm Intelligence
• [cs.LG]EmBench: Quantifying Performance Variations of Deep Neural Networks across Modern Commodity Devices
• [cs.LG]Fairness in Machine Learning with Tractable Models
• [cs.LG]Hybrid-FL: Cooperative Learning Mechanism Using Non-IID Data in Wireless Networks
• [cs.LG]Integer Discrete Flows and Lossless Compression
• [cs.LG]MOBA: A multi-objective bounded-abstention model for two-class cost-sensitive problems
• [cs.LG]MaMiC: Macro and Micro Curriculum for Robotic Reinforcement Learning
• [cs.LG]Mastering the Game of Sungka from Random Play
• [cs.LG]Online Multivariate Anomaly Detection and Localization for High-dimensional Settings
• [cs.LG]POPQORN: Quantifying Robustness of Recurrent Neural Networks
• [cs.LG]Privacy Preserving Adjacency Spectral Embedding on Stochastic Blockmodels
• [cs.LG]Recurrent Kalman Networks: Factorized Inference in High-Dimensional Deep Feature Spaces
• [cs.LG]Reference-Based Sequence Classification
• [cs.LG]SSFN: Self Size-estimating Feed-forward Network and Low Complexity Design
• [cs.LG]Simple Black-box Adversarial Attacks
• [cs.LG]Sliced Score Matching: A Scalable Approach to Density and Score Estimation
• [cs.LG]Spectral Metric for Dataset Complexity Assessment
• [cs.LG]Stochastically Dominant Distributional Reinforcement Learning
• [cs.LG]Stratospheric Aerosol Injection as a Deep Reinforcement Learning Problem
• [cs.LG]TBQ($σ$): Improving Efficiency of Trace Utilization for Off-Policy Reinforcement Learning
• [cs.LG]Utilizing Deep Learning Towards Multi-modal Bio-sensing and Vision-based Affective Computing
• [cs.LG]Weakly-Supervised Temporal Localization via Occurrence Count Learning
• [cs.NE]Approximation of the objective insensitivity regions using Hierarchic Memetic Strategy coupled with Covariance Matrix Adaptation Evolutionary Strategy
• [cs.NI]Minimizing the Age of Information in Wireless Networks with Stochastic Arrivals
• [cs.RO]Challenges in Collaborative HRI for Remote Robot Teams
• [cs.RO]Training Object Detectors With Noisy Data
• [cs.RO]Understanding of Object Manipulation Actions Using Human Multi-Modal Sensory Data
• [cs.SY]Randomized Algorithms for Data-Driven Stabilization of Stochastic Linear Systems
• [eess.AS]End-to-end Adaptation with Backpropagation through WFST for On-device Speech Recognition System
• [eess.IV]GlidarCo: gait recognition by 3D skeleton estimation and biometric feature correction of flash lidar data
• [eess.IV]Mechanically Powered Motion Imaging Phantoms: Proof of Concept
• [eess.SP]Deep Reinforcement Learning-Based Channel Allocation for Wireless LANs with Graph Convolutional Networks
• [math.ST]Estimation of foreseeable and unforeseeable risks in motor insurance
• [math.ST]Maximum Likelihood Estimation of Toric Fano Varieties
• [math.ST]The Empirical Saddlepoint Estimator
• [nlin.AO]When the goal is to generate a series of activities: A self-organized simulated robot arm
• [q-bio.PE]NANUQ: A method for inferring species networks from gene trees under the coalescent model
• [stat.AP]Colombian Women’s Life Patterns: A Multivariate Density Regression Approach
• [stat.ME]A Bayesian hierarchical meta-analytic method for modelling surrogate relationships that vary across treatment classes
• [stat.ME]Analytic Basis Expansions for Functional Snippets
• [stat.ME]Functional Lagged Regression with Sparse Noisy Observations
• [stat.ME]Model interpretation through lower-dimensional posterior summarization
• [stat.ML]Comparison of Machine Learning Models in Food Authentication Studies
• [stat.ML]Dream Distillation: A Data-Independent Model Compression Framework
• [stat.ML]Lexicographic and Depth-Sensitive Margins in Homogeneous and Non-Homogeneous Deep Models
• [stat.ML]Merging versus Ensembling in Multi-Study Machine Learning: Theoretical Insight from Random Effects
• [stat.ML]Non-negative matrix factorization based on generalized dual divergence
• [stat.ML]Online Distributed Estimation of Principal Eigenspaces
• [stat.ML]Pair Matching: When bandits meet stochastic block model
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• [astro-ph.SR]Predicting Solar Flares Using a Long Short-Term Memory Network
Hao Liu, Chang Liu, Jason T. L. Wang, Haimin Wang
http://arxiv.org/abs/1905.07095v1
• [cs.AI]A Correctness Result for Synthesizing Plans With Loops in Stochastic Domains
Laszlo Treszkai, Vaishak Belle
http://arxiv.org/abs/1905.07028v1
• [cs.AI]How Case Based Reasoning Explained Neural Networks: An XAI Survey of Post-Hoc Explanation-by-Example in ANN-CBR Twins
Mark T Keane, Eoin M Kenny
http://arxiv.org/abs/1905.07186v1
• [cs.AI]Knowledge-Based Sequential Decision-Making Under Uncertainty
Daoming Lyu
http://arxiv.org/abs/1905.07030v1
• [cs.CL]Adaptation of Deep Bidirectional Multilingual Transformers for Russian Language
Yuri Kuratov, Mikhail Arkhipov
http://arxiv.org/abs/1905.07213v1
• [cs.CL]Availability-Based Production Predicts Speakers’ Real-time Choices of Mandarin Classifiers
Meilin Zhan, Roger Levy
http://arxiv.org/abs/1905.07321v1
• [cs.CL]CHiVE: Varying Prosody in Speech Synthesis with a Linguistically Driven Dynamic Hierarchical Conditional Variational Network
Vincent Wan, Chun-an Chan, Tom Kenter, Jakub Vit, Rob Clark
http://arxiv.org/abs/1905.07195v1
• [cs.CL]Conversion Prediction Using Multi-task Conditional Attention Networks to Support the Creation of Effective Ad Creative
Shunsuke Kitada, Hitoshi Iyatomi, Yoshifumi Seki
http://arxiv.org/abs/1905.07289v1
• [cs.CL]Distant Learning for Entity Linking with Automatic Noise Detection
Phong Le, Ivan Titov
http://arxiv.org/abs/1905.07189v1
• [cs.CL]Don’t Blame Distributional Semantics if it can’t do Entailment
Matthijs Westera, Gemma Boleda
http://arxiv.org/abs/1905.07356v1
• [cs.CL]ERNIE: Enhanced Language Representation with Informative Entities
Zhengyan Zhang, Xu Han, Zhiyuan Liu, Xin Jiang, Maosong Sun, Qun Liu
http://arxiv.org/abs/1905.07129v1
• [cs.CL]IMHO Fine-Tuning Improves Claim Detection
Tuhin Chakrabarty, Christopher Hidey, Kathleen McKeown
http://arxiv.org/abs/1905.07000v1
• [cs.CL]Improving Question Answering over Incomplete KBs with Knowledge-Aware Reader
Wenhan Xiong, Mo Yu, Shiyu Chang, Xiaoxiao Guo, William Yang Wang
http://arxiv.org/abs/1905.07098v1
• [cs.CL]Learning Cross-lingual Embeddings from Twitter via Distant Supervision
Jose Camacho-Collados, Yerai Doval, Eugenio Martínez-Cámara, Luis Espinosa-Anke, Francesco Barbieri, Steven Schockaert
http://arxiv.org/abs/1905.07358v1
• [cs.CL]Multi-hop Reading Comprehension across Multiple Documents by Reasoning over Heterogeneous Graphs
Ming Tu, Guangtao Wang, Jing Huang, Yun Tang, Xiaodong He, Bowen Zhou
http://arxiv.org/abs/1905.07374v1
• [cs.CL]Plotting Markson’s ‘Mistress’
Kelleher Conor, Mark T. Keane
http://arxiv.org/abs/1905.07185v1
• [cs.CL]Towards Automatic Generation of Shareable Synthetic Clinical Notes Using Neural Language Models
Oren Melamud, Chaitanya Shivade
http://arxiv.org/abs/1905.07002v1
• [cs.CR]A critique of the DeepSec Platform for Security Analysis of Deep Learning Models
Nicholas Carlini
http://arxiv.org/abs/1905.07112v1
• [cs.CR]Finding Rats in Cats: Detecting Stealthy Attacks using Group Anomaly Detection
Aditya Kuppa, Slawomir Grzonkowski, Muhammad Rizwan Asghar, Nhien-An Le-Khac
http://arxiv.org/abs/1905.07273v1
• [cs.CR]Learning from Context: Exploiting and Interpreting File Path Information for Better Malware Detection
Adarsh Kyadige, Ethan M. Rudd, Konstantin Berlin
http://arxiv.org/abs/1905.06987v1
• [cs.CV]A deep learning approach to detecting volcano deformation from satellite imagery using synthetic datasets
Nantheera Anantrasirichai, Juliet Biggs, Fabien Albino, David Bull
http://arxiv.org/abs/1905.07286v1
• [cs.CV]AM-LFS: AutoML for Loss Function Search
Chuming Li, Chen Lin, Minghao Guo, Wei Wu, Wanli Ouyang, Junjie Yan
http://arxiv.org/abs/1905.07375v1
• [cs.CV]CNN-based Cost Volume Analysis as Confidence Measure for Dense Matching
Max Mehltretter, Christian Heipke
http://arxiv.org/abs/1905.07287v1
• [cs.CV]Group Re-Identification with Multi-grained Matching and Integration
Weiyao Lin, Yuxi Li, Hao Xiao, John See, Junni Zou, Hongkai Xiong, Jingdong Wang, Tao Mei
http://arxiv.org/abs/1905.07108v1
• [cs.CV]How do neural networks see depth in single images?
Tom van Dijk, Guido C. H. E. de Croon
http://arxiv.org/abs/1905.07005v1
• [cs.CV]LiDAR Sensor modeling and Data augmentation with GANs for Autonomous driving
Ahmad El Sallab, Ibrahim Sobh, Mohamed Zahran, Nader Essam
http://arxiv.org/abs/1905.07290v1
• [cs.CV]Neither Global Nor Local: A Hierarchical Robust Subspace Clustering For Image Data
Maryam Abdolali, Mohammad Rahmati
http://arxiv.org/abs/1905.07220v1
• [cs.CV]Neural Message Passing on Hybrid Spatio-Temporal Visual and Symbolic Graphs for Video Understanding
Effrosyni Mavroudi, Benjamín Béjar Haro, René Vidal
http://arxiv.org/abs/1905.07385v1
• [cs.CV]Non-Parametric Priors For Generative Adversarial Networks
Rajhans Singh, Pavan Turaga, Suren Jayasuriya, Ravi Garg, Martin W. Braun
http://arxiv.org/abs/1905.07061v1
• [cs.CV]Online Hyper-parameter Learning for Auto-Augmentation Strategy
Chen Lin, Minghao Guo, Chuming Li, Wei Wu, Dahua Lin, Wanli Ouyang, Junjie Yan
http://arxiv.org/abs/1905.07373v1
• [cs.CV]PoreNet: CNN-based Pore Descriptor for High-resolution Fingerprint Recognition
Vijay Anand, Vivek Kanhangad
http://arxiv.org/abs/1905.06981v1
• [cs.CV]Semantic Analysis of Traffic Camera Data: Topic Signal Extraction and Anomalous Event Detection
Jeffrey Liu, Andrew Weinert, Saurabh Amin
http://arxiv.org/abs/1905.07332v1
• [cs.CV]Side Window Filtering
Hui Yin, Yuanhao Gong, Guoping Qiu
http://arxiv.org/abs/1905.07177v1
• [cs.CV]Texture Fields: Learning Texture Representations in Function Space
Michael Oechsle, Lars Mescheder, Michael Niemeyer, Thilo Strauss, Andreas Geiger
http://arxiv.org/abs/1905.07259v1
• [cs.CV]Transfer Learning based Detection of Diabetic Retinopathy from Small Dataset
Misgina Tsighe Hagos, Shri Kant
http://arxiv.org/abs/1905.07203v1
• [cs.CY]Between Discord and Deadlock: Consensus Under a Deadline
Marina Bannikova, Lihi Dery, Svetlana Obraztsova, Zinovi Rabinovich, Jeffrey S. Rosenschein
http://arxiv.org/abs/1905.07173v1
• [cs.CY]Exploration methods for simulation models
J. Raimbault, D. Pumain
http://arxiv.org/abs/1905.07160v1
• [cs.DB]Concurrency Protocol Aiming at High Performance of Execution and Replay for Smart Contracts
Shuaifeng Pang, Xiaodong Qi, Zhao Zhang, Cheqing Jin, Aoying Zhou
http://arxiv.org/abs/1905.07169v1
• [cs.DC]Auto-tuning of dynamic load balancing applied to 3D reverse time migration on multicore systems
Ítalo A. S. Assis, João B. Fernandes, Tiago Barros, Samuel Xavier-de-Souza
http://arxiv.org/abs/1905.06975v1
• [cs.DC]On the complexity of fault-tolerant consensus
Dariusz R. Kowalski, Jaroslaw Mirek
http://arxiv.org/abs/1905.07063v1
• [cs.DS]Parallel decompression of gzip-compressed files and random access to DNA sequences
Maël Kerbiriou, Rayan Chikhi
http://arxiv.org/abs/1905.07224v1
• [cs.DS]Separating Structure from Noise in Large Graphs Using the Regularity Lemma
Marco Fiorucci, Francesco Pelosin, Marcello Pelillo
http://arxiv.org/abs/1905.06917v2
• [cs.GT]Fiduciary Bandits
Gal Bahar, Omer Ben-Porat, Kevin Leyton-Brown, Moshe Tennenholtz
http://arxiv.org/abs/1905.07043v1
• [cs.HC]Are Automated Vehicles Safer than Manually Driven Cars?
Lionel Peter Robert Jr
http://arxiv.org/abs/1905.07054v1
• [cs.HC]MiSC: Mixed Strategies Crowdsourcing
Ching-Yun Ko, Rui Lin, Shu Li, Ngai Wong
http://arxiv.org/abs/1905.07394v1
• [cs.IR]Cleaned Similarity for Better Memory-Based Recommenders
Farhan Khawar, Nevin L. Zhang
http://arxiv.org/abs/1905.07370v1
• [cs.IR]Deep Unified Multimodal Embeddings for Understanding both Content and Users in Social Media Networks
Karan Sikka, Lucas Van Bramer, Ajay Divakaran
http://arxiv.org/abs/1905.07075v1
• [cs.IR]Exact-K Recommendation via Maximal Clique Optimization
Yu Gong, Yu Zhu, Lu Duan, Qingwen Liu, Ziyu Guan, Fei Sun, Wenwu Ou, Kenny Q. Zhu
http://arxiv.org/abs/1905.07089v1
• [cs.IT]Buffer-aided Resource Allocation for a Price Based Opportunistic Cognitive Radio Network
Nilanjan Biswas, Goutam Das, Priyadip Ray
http://arxiv.org/abs/1905.07143v1
• [cs.IT]Discussions on Signal Uncertainty Principle in Shannon Channel Capacity Equation and Research on Breaking Shannon Limit Method
Dequn Liang, Xinyu Dou
http://arxiv.org/abs/1905.07097v1
• [cs.IT]Joint Frequency-and-Phase Modulation for Backscatter-Tag Assisted Vehicular Positioning
Kaifeng Han, Seung-Woo Ko, Seungmin Lee, Woo-Suk Ko, Kaibin Huang
http://arxiv.org/abs/1905.07125v1
• [cs.IT]Optimal Status Updating with a Finite-Battery Energy Harvesting Source
Baran Tan Bacinoglu, Yin Sun, Elif Uysal, Volkan Mutlu
http://arxiv.org/abs/1905.06679v2
• [cs.LG]An Essay on Optimization Mystery of Deep Learning
Eugene Golikov
http://arxiv.org/abs/1905.07187v1
• [cs.LG]Biosignal Generation and Latent Variable Analysis with Recurrent Generative Adversarial Networks
Shota Harada, Hideaki Hayashi, Seiichi Uchida
http://arxiv.org/abs/1905.07136v1
• [cs.LG]Comparison-Based Framework for Psychophysics: Lab versus Crowdsourcing
Siavash Haghiri, Felix Wichmann, Ulrike von Luxburg
http://arxiv.org/abs/1905.07234v1
• [cs.LG]Contrastive Fairness in Machine Learning
Tapabrata Chakraborti, Arijit Patra, Alison Noble
http://arxiv.org/abs/1905.07360v1
• [cs.LG]Decision-Oriented Communications: Application to Energy-Efficient Resource Allocation
Hang Zou, Chao Zhang, Samson Lasaulce, Lucas Saludjian, Patrick Panciatici
http://arxiv.org/abs/1905.07339v1
• [cs.LG]DeepSwarm: Optimising Convolutional Neural Networks using Swarm Intelligence
Edvinas Byla, Wei Pang
http://arxiv.org/abs/1905.07350v1
• [cs.LG]EmBench: Quantifying Performance Variations of Deep Neural Networks across Modern Commodity Devices
Mario Almeida, Stefanos Laskaridis, Ilias Leontiadis, Stylianos I. Venieris, Nicholas D. Lane
http://arxiv.org/abs/1905.07346v1
• [cs.LG]Fairness in Machine Learning with Tractable Models
Michael Varley, Vaishak Belle
http://arxiv.org/abs/1905.07026v1
• [cs.LG]Hybrid-FL: Cooperative Learning Mechanism Using Non-IID Data in Wireless Networks
Naoya Yoshida, Takayuki Nishio, Masahiro Morikura, Koji Yamamoto, Ryo Yonetani
http://arxiv.org/abs/1905.07210v1
• [cs.LG]Integer Discrete Flows and Lossless Compression
Emiel Hoogeboom, Jorn W. T. Peters, Rianne van den Berg, Max Welling
http://arxiv.org/abs/1905.07376v1
• [cs.LG]MOBA: A multi-objective bounded-abstention model for two-class cost-sensitive problems
Hongjiao Guan
http://arxiv.org/abs/1905.07297v1
• [cs.LG]MaMiC: Macro and Micro Curriculum for Robotic Reinforcement Learning
Manan Tomar, Akhil Sathuluri, Balaraman Ravindran
http://arxiv.org/abs/1905.07193v1
• [cs.LG]Mastering the Game of Sungka from Random Play
Darwin Bautista, Raimarc Dionido
http://arxiv.org/abs/1905.07102v1
• [cs.LG]Online Multivariate Anomaly Detection and Localization for High-dimensional Settings
Mahsa Mozaffari, Yasin Yilmaz
http://arxiv.org/abs/1905.07107v1
• [cs.LG]POPQORN: Quantifying Robustness of Recurrent Neural Networks
Ching-Yun Ko, Zhaoyang Lyu, Tsui-Wei Weng, Luca Daniel, Ngai Wong, Dahua Lin
http://arxiv.org/abs/1905.07387v1
• [cs.LG]Privacy Preserving Adjacency Spectral Embedding on Stochastic Blockmodels
Li Chen
http://arxiv.org/abs/1905.07065v1
• [cs.LG]Recurrent Kalman Networks: Factorized Inference in High-Dimensional Deep Feature Spaces
Philipp Becker, Harit Pandya, Gregor Gebhardt, Cheng Zhao, James Taylor, Gerhard Neumann
http://arxiv.org/abs/1905.07357v1
• [cs.LG]Reference-Based Sequence Classification
Zengyou He, Guangyao Xu, Chaohua Sheng, Bo Xu, Quan Zou
http://arxiv.org/abs/1905.07188v1
• [cs.LG]SSFN: Self Size-estimating Feed-forward Network and Low Complexity Design
Saikat Chatterjee, Alireza M. Javid, Mostafa Sadeghi, Shumpei Kikuta, Partha P. Mitra, Mikael Skoglund
http://arxiv.org/abs/1905.07111v1
• [cs.LG]Simple Black-box Adversarial Attacks
Chuan Guo, Jacob R. Gardner, Yurong You, Andrew Gordon Wilson, Kilian Q. Weinberger
http://arxiv.org/abs/1905.07121v1
• [cs.LG]Sliced Score Matching: A Scalable Approach to Density and Score Estimation
Yang Song, Sahaj Garg, Jiaxin Shi, Stefano Ermon
http://arxiv.org/abs/1905.07088v1
• [cs.LG]Spectral Metric for Dataset Complexity Assessment
Frederic Branchaud-Charron, Andrew Achkar, Pierre-Marc Jodoin
http://arxiv.org/abs/1905.07299v1
• [cs.LG]Stochastically Dominant Distributional Reinforcement Learning
John D. Martin, Michal Lyskawinski, Xiaohu Li, Brendan Englot
http://arxiv.org/abs/1905.07318v1
• [cs.LG]Stratospheric Aerosol Injection as a Deep Reinforcement Learning Problem
Christian Schroeder de Witt, Thomas Hornigold
http://arxiv.org/abs/1905.07366v1
• [cs.LG]TBQ($σ$): Improving Efficiency of Trace Utilization for Off-Policy Reinforcement Learning
Longxiang Shi, Shijian Li, Longbing Cao, Long Yang, Gang Pan
http://arxiv.org/abs/1905.07237v1
• [cs.LG]Utilizing Deep Learning Towards Multi-modal Bio-sensing and Vision-based Affective Computing
Siddharth Siddharth, Tzyy-Ping Jung, Terrence J. Sejnowski
http://arxiv.org/abs/1905.07039v1
• [cs.LG]Weakly-Supervised Temporal Localization via Occurrence Count Learning
Julien Schroeter, Kirill Sidorov, David Marshall
http://arxiv.org/abs/1905.07293v1
• [cs.NE]Approximation of the objective insensitivity regions using Hierarchic Memetic Strategy coupled with Covariance Matrix Adaptation Evolutionary Strategy
Jakub Sawicki, Maciej Smołka, Marcin Łoś, Robert Schaefer
http://arxiv.org/abs/1905.07288v1
• [cs.NI]Minimizing the Age of Information in Wireless Networks with Stochastic Arrivals
Igor Kadota, Eytan Modiano
http://arxiv.org/abs/1905.07020v1
• [cs.RO]Challenges in Collaborative HRI for Remote Robot Teams
Helen Hastie, David A. Robb, José Lopes, Muneeb Ahmad, Pierre Le Bras, Xingkun Liu, Ronald P. A. Petrick, Katrin Lohan, Mike J. Chantler
http://arxiv.org/abs/1905.07379v1
• [cs.RO]Training Object Detectors With Noisy Data
Simon Chadwick, Paul Newman
http://arxiv.org/abs/1905.07202v1
• [cs.RO]Understanding of Object Manipulation Actions Using Human Multi-Modal Sensory Data
Bahareh Abbasi, Ehsan Noohi, Sina Parastegari, Milos Zefran
http://arxiv.org/abs/1905.07012v1
• [cs.SY]Randomized Algorithms for Data-Driven Stabilization of Stochastic Linear Systems
Mohamad Kazem Shirani Faradonbeh, Ambuj Tewari, George Michailidis
http://arxiv.org/abs/1905.06978v1
• [eess.AS]End-to-end Adaptation with Backpropagation through WFST for On-device Speech Recognition System
Emiru Tsunoo, Yosuke Kashiwagi, Satoshi Asakawa, Toshiyuki Kumakura
http://arxiv.org/abs/1905.07149v1
• [eess.IV]GlidarCo: gait recognition by 3D skeleton estimation and biometric feature correction of flash lidar data
Nasrin Sadeghzadehyazdi, Tamal Batabyal, Nibir K. Dhar, B. O. Familoni, K. M. Iftekharuddin, Scott T. Acton
http://arxiv.org/abs/1905.07058v1
• [eess.IV]Mechanically Powered Motion Imaging Phantoms: Proof of Concept
Alberto Gomez, Cornelia Schmitz, Markus Henningsson, James Housden, Yohan Noh, Veronika A. Zimmer, James R. Clough, Ilkay Oksuz, Nicolas Toussaint, Andrew P. King, Julia A. Schnabel
http://arxiv.org/abs/1905.07198v1
• [eess.SP]Deep Reinforcement Learning-Based Channel Allocation for Wireless LANs with Graph Convolutional Networks
Kota Nakashima, Shotaro Kamiya, Kazuki Ohtsu, Koji Yamamoto, Takayuki Nishio, Masahiro Morikura
http://arxiv.org/abs/1905.07144v1
• [math.ST]Estimation of foreseeable and unforeseeable risks in motor insurance
Weihong Ni, Corina Constantinescu, Alfredo Egídio dos Reis, Véronique Maume-Deschamps
http://arxiv.org/abs/1905.07157v1
• [math.ST]Maximum Likelihood Estimation of Toric Fano Varieties
Carlos Améndola, Dimitra Kosta, Kaie Kubjas
http://arxiv.org/abs/1905.07396v1
• [math.ST]The Empirical Saddlepoint Estimator
Benjamin Holcblat, Fallaw Sowell
http://arxiv.org/abs/1905.06977v1
• [nlin.AO]When the goal is to generate a series of activities: A self-organized simulated robot arm
Tim Koglin, Bulcsú Sándor, Claudius Gros
http://arxiv.org/abs/1905.07235v1
• [q-bio.PE]NANUQ: A method for inferring species networks from gene trees under the coalescent model
Elizabeth Allman, Hector Banos, John Rhodes
http://arxiv.org/abs/1905.07050v1
• [stat.AP]Colombian Women’s Life Patterns: A Multivariate Density Regression Approach
Sara Wade, Raffaella Piccarreta, Andrea Cremaschi, Isadora Antoniano-Villalobos
http://arxiv.org/abs/1905.07172v1
• [stat.ME]A Bayesian hierarchical meta-analytic method for modelling surrogate relationships that vary across treatment classes
Tasos Papanikos, John Thompson, Keith Abrams, Nicolas Staedler, Oriana Ciani, Rod Taylor, Sylwia Bujkiewicz
http://arxiv.org/abs/1905.07194v1
• [stat.ME]Analytic Basis Expansions for Functional Snippets
Zhenhua Lin, Qixian Zhong, Jane-Ling Wang
http://arxiv.org/abs/1905.07067v1
• [stat.ME]Functional Lagged Regression with Sparse Noisy Observations
Tomáš Rubín, Victor M. Panaretos
http://arxiv.org/abs/1905.07218v1
• [stat.ME]Model interpretation through lower-dimensional posterior summarization
Spencer Woody, Carlos M. Carvalho, Jared S. Murray
http://arxiv.org/abs/1905.07103v1
• [stat.ML]Comparison of Machine Learning Models in Food Authentication Studies
Manokamna Singh, Katarina Domijan
http://arxiv.org/abs/1905.07302v1
• [stat.ML]Dream Distillation: A Data-Independent Model Compression Framework
Kartikeya Bhardwaj, Naveen Suda, Radu Marculescu
http://arxiv.org/abs/1905.07072v1
• [stat.ML]Lexicographic and Depth-Sensitive Margins in Homogeneous and Non-Homogeneous Deep Models
Mor Shpigel Nacson, Suriya Gunasekar, Jason D. Lee, Nathan Srebro, Daniel Soudry
http://arxiv.org/abs/1905.07325v1
• [stat.ML]Merging versus Ensembling in Multi-Study Machine Learning: Theoretical Insight from Random Effects
Zoe Guan, Giovanni Parmigiani, Prasad Patil
http://arxiv.org/abs/1905.07382v1
• [stat.ML]Non-negative matrix factorization based on generalized dual divergence
Karthik Devarajan
http://arxiv.org/abs/1905.07034v1
• [stat.ML]Online Distributed Estimation of Principal Eigenspaces
Davoud Ataee Tarzanagh, Mohamad Kazem Shirani Faradonbeh, George Michailidis
http://arxiv.org/abs/1905.07389v1
• [stat.ML]Pair Matching: When bandits meet stochastic block model
Christophe Giraud, Yann Issartel, Luc Lehéricy, Matthieu Lerasle
http://arxiv.org/abs/1905.07342v1