cond-mat.dis-nn - 无序系统与神经网络

    cs.CL - 计算与语言 cs.CV - 机器视觉与模式识别 cs.CY - 计算与社会 cs.DC - 分布式、并行与集群计算 cs.GR - 计算机图形学 cs.IR - 信息检索 cs.IT - 信息论 cs.LG - 自动学习 cs.NA - 数值分析 cs.NE - 神经与进化计算 cs.RO - 机器人学 cs.SD - 声音处理 cs.SY - 系统与控制 math.FA - 泛函演算 math.OC - 优化与控制 math.PR - 概率 math.ST - 统计理论 physics.ao-ph - 大气和海洋物理 physics.soc-ph - 物理学与社会 q-fin.CP -计算金融学 quant-ph - 量子物理 stat.AP - 应用统计 stat.ME - 统计方法论 stat.ML - (统计)机器学习

    • [cond-mat.dis-nn]Variational approach to unsupervised learning
    • [cs.CL]Better Automatic Evaluation of Open-Domain Dialogue Systems with Contextualized Embeddings
    • [cs.CL]Condition-Transforming Variational AutoEncoder for Conversation Response Generation
    • [cs.CL]Detecting Machine-Translated Paragraphs by Matching Similar Words
    • [cs.CL]End-to-End Spoken Language Translation
    • [cs.CL]Listening between the Lines: Learning Personal Attributes from Conversations
    • [cs.CL]Natural Language Interactions in Autonomous Vehicles: Intent Detection and Slot Filling from Passenger Utterances
    • [cs.CL]Objective Assessment of Social Skills Using Automated Language Analysis for Identification of Schizophrenia and Bipolar Disorder
    • [cs.CL]On the Contributions of Visual and Textual Supervision in Low-resource Semantic Speech Retrieval
    • [cs.CL]Semantic Drift in Multilingual Representations
    • [cs.CL]Who Blames Whom in a Crisis? Detecting Blame Ties from News Articles Using Neural Networks
    • [cs.CV]A CNN-RNN Architecture for Multi-Label Weather Recognition
    • [cs.CV]A General Framework for Edited Video and Raw Video Summarization
    • [cs.CV]A Large-scale Varying-view RGB-D Action Dataset for Arbitrary-view Human Action Recognition
    • [cs.CV]A Novel Re-weighting Method for Connectionist Temporal Classification
    • [cs.CV]Automatic cephalometric landmarks detection on frontal faces: an approach based on supervised learning techniques
    • [cs.CV]Bidirectional Learning for Domain Adaptation of Semantic Segmentation
    • [cs.CV]CED: Color Event Camera Dataset
    • [cs.CV]Computer-aided diagnosis in histopathological images of the endometrium using a convolutional neural network and attention mechanisms
    • [cs.CV]Detailed Human Shape Estimation from a Single Image by Hierarchical Mesh Deformation
    • [cs.CV]Improving Few-Shot User-Specific Gaze Adaptation via Gaze Redirection Synthesis
    • [cs.CV]Informative sample generation using class aware generative adversarial networks for classification of chest Xrays
    • [cs.CV]LFFD: A Light and Fast Face Detector for Edge Devices
    • [cs.CV]Multi-scale deep neural networks for real image super-resolution
    • [cs.CV]Neural Collaborative Subspace Clustering
    • [cs.CV]Optical machine learning with incoherent light and a single-pixel detector
    • [cs.CV]Segmenting the Future
    • [cs.CV]Simultaneous regression and feature learning for facial landmarking
    • [cs.CV]Super-resolution based generative adversarial network using visual perceptual loss function
    • [cs.CV]The VGG Image Annotator (VIA)
    • [cs.CV]The iterative convolution-thresholding method (ICTM) for image segmentation
    • [cs.CV]Understanding Art through Multi-Modal Retrieval in Paintings
    • [cs.CV]Unfocused images removal of z-axis overlapping Mie scattering particles by using three-dimensional nonlinear diffusion based on digital holography
    • [cs.CV]ViDeNN: Deep Blind Video Denoising
    • [cs.CY]The Ex-Ante View of Recommender System Design
    • [cs.DC]Chunkflow: Distributed Hybrid Cloud Processing of Large 3D Images by Convolutional Nets
    • [cs.DC]Distributed Continuous Range-Skyline Query Monitoring over the Internet of Mobile Things
    • [cs.DC]Fast Distributed Algorithms for LP-Type Problems of Bounded Dimension
    • [cs.GR]OperatorNet: Recovering 3D Shapes From Difference Operators
    • [cs.IR]Fine-Grained Named Entity Recognition using ELMo and Wikidata
    • [cs.IR]Integrating Social Media into a Pan-European Flood Awareness System: A Multilingual Approach
    • [cs.IR]Latent Variable Session-Based Recommendation
    • [cs.IR]Three Methods for Training on Bandit Feedback
    • [cs.IT]Energy-Efficient Mobile-Edge Computation Offloading over Multiple Fading Blocks
    • [cs.IT]Fixed-Symbol Aided Random Access Scheme for Machine-to-Machine Communications
    • [cs.IT]Group codes over fields are asymptotically good
    • [cs.IT]Joint Long-Term Cache Allocation and Short-Term Content Delivery in Green Cloud Small Cell Networks
    • [cs.IT]Obtaining binary perfect codes out of tilings
    • [cs.IT]Secure Communication in Dynamic Wireless Ad hoc Networks
    • [cs.IT]Throughput Maximization in Two-hop DF Multiple-Relay Network with Simultaneous Wireless Information and Power Transfer
    • [cs.LG]A Comparison Study of Credit Card Fraud Detection: Supervised versus Unsupervised
    • [cs.LG]A bag-of-concepts model improves relation extraction in a narrow knowledge domain with limited data
    • [cs.LG]Baconian: A Unified Opensource Framework for Model-Based Reinforcement Learning
    • [cs.LG]Beyond Adaptive Submodularity: Approximation Guarantees of Greedy Policy with Adaptive Submodularity Ratio
    • [cs.LG]Block-distributed Gradient Boosted Trees
    • [cs.LG]CascadeML: An Automatic Neural Network Architecture Evolution and Training Algorithm for Multi-label Classification
    • [cs.LG]Concise Fuzzy System Modeling Integrating Soft Subspace Clustering and Sparse Learning
    • [cs.LG]Deep Learning for Classification of Hyperspectral Data: A Comparative Review
    • [cs.LG]Deep Q-Learning for Nash Equilibria: Nash-DQN
    • [cs.LG]Design Automation for Efficient Deep Learning Computing
    • [cs.LG]Differentiable Pruning Method for Neural Networks
    • [cs.LG]Facilitating Bayesian Continual Learning by Natural Gradients and Stein Gradients
    • [cs.LG]Generated Loss and Augmented Training of MNIST VAE
    • [cs.LG]Generating Long Sequences with Sparse Transformers
    • [cs.LG]Generating Token-Level Explanations for Natural Language Inference
    • [cs.LG]Integer Programming for Learning Directed Acyclic Graphs from Continuous Data
    • [cs.LG]KFHE-HOMER: Kalman Filter-based Heuristic Ensemble of HOMER for Multi-Label Classification
    • [cs.LG]Layer Dynamics of Linearised Neural Nets
    • [cs.LG]Learning Bodily and Temporal Attention in Protective Movement Behavior Detection
    • [cs.LG]Low-Memory Neural Network Training: A Technical Report
    • [cs.LG]Maximum Entropy Based Significance of Itemsets
    • [cs.LG]Neural Logic Reinforcement Learning
    • [cs.LG]Prediction of Progression to Alzheimer`s disease with Deep InfoMax
    • [cs.LG]Quantum-assisted associative adversarial network: Applying quantum annealing in deep learning
    • [cs.LG]Some Limit Properties of Markov Chains Induced by Stochastic Recursive Algorithms
    • [cs.LG]Stochastic Lipschitz Q-Learning
    • [cs.LG]Target-Based Temporal Difference Learning
    • [cs.LG]The Scientific Method in the Science of Machine Learning
    • [cs.LG]Towards Combining On-Off-Policy Methods for Real-World Applications
    • [cs.LG]Unsupervised Assignment Flow: Label Learning on Feature Manifolds by Spatially Regularized Geometric Assignment
    • [cs.LG]Wearable-based Parkinson’s Disease Severity Monitoring using Deep Learning
    • [cs.NA]Low-Rank Tucker Approximation of a Tensor From Streaming Data
    • [cs.NE]Balanced Crossover Operators in Genetic Algorithms
    • [cs.NE]Evolving Neural Networks in Reinforcement Learning by means of UMDAc
    • [cs.NE]Mapping Hearthstone Deck Spaces through MAP-Elites with Sliding Boundaries
    • [cs.RO]Bayesian Gaussian mixture model for robotic policy imitation
    • [cs.RO]Tactile Mapping and Localization from High-Resolution Tactile Imprints
    • [cs.SD]Realizing Petabyte Scale Acoustic Modeling
    • [cs.SD]Unsupervised Adversarial Domain Adaptation Based On The Wasserstein Distance For Acoustic Scene Classification
    • [cs.SY]Autonomous Voltage Control for Grid Operation Using Deep Reinforcement Learning
    • [math.FA]Native Banach spaces for splines and variational inverse problems
    • [math.OC]Efficient Simulation Budget Allocation for Subset Selection Using Regression Metamodels
    • [math.PR]Drift Estimation for Discretely Sampled SPDEs
    • [math.PR]On laws exhibiting universal ordering under stochastic restart
    • [math.ST]Prediction bounds for (higher order) total variationregularized least squares
    • [math.ST]Unbiased truncated quadratic variation for volatility estimation in jump diffusion processes
    • [physics.ao-ph]Applying machine learning to improve simulations of a chaotic dynamical system using empirical error correction
    • [physics.soc-ph]Containing misinformation spreading in temporal social networks
    • [physics.soc-ph]Mercator: uncovering faithful hyperbolic embeddings of complex networks
    • [physics.soc-ph]Optimization of the post-crisis recovery plans in scale-free networks
    • [physics.soc-ph]Pulse strategy for suppressing spreading on networks
    • [q-fin.CP]A neural network-based framework for financial model calibration
    • [quant-ph]Machine learning for long-distance quantum communication
    • [stat.AP]A Cross-validated Ensemble Approach to Robust Hypothesis Testing of Continuous Nonlinear Interactions: Application to Nutrition-Environment Studies
    • [stat.AP]Who Gets the Job and How are They Paid? Machine Learning Application on H-1B Case Data
    • [stat.ME]A penalized likelihood approach for efficiently estimating a partially linear additive transformation model with current status data
    • [stat.ME]Baseline Drift Estimation for Air Quality Data Using Quantile Trend Filtering
    • [stat.ME]Comparing Samples from the $\mathcal{G}^0$ Distribution using a Geodesic Distance
    • [stat.ME]Horseshoe Regularization for Machine Learning in Complex and Deep Models
    • [stat.ME]Trajectory Functional Boxplots
    • [stat.ML]$S^{2}$-LBI: Stochastic Split Linearized Bregman Iterations for Parsimonious Deep Learning
    • [stat.ML]An Exploratory Analysis of Biased Learners in Soft-Sensing Frames
    • [stat.ML]Bayesian leave-one-out cross-validation for large data
    • [stat.ML]Kernel Mean Embedding of Instance-wise Predictions in Multiple Instance Regression
    • [stat.ML]Learning big Gaussian Bayesian networks: partition, estimation, and fusion

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

    • [cond-mat.dis-nn]Variational approach to unsupervised learning
    Swapnil Nitin Shah
    http://arxiv.org/abs/1904.10869v1

    • [cs.CL]Better Automatic Evaluation of Open-Domain Dialogue Systems with Contextualized Embeddings
    Sarik Ghazarian, Johnny Tian-Zheng Wei, Aram Galstyan, Nanyun Peng
    http://arxiv.org/abs/1904.10635v1

    • [cs.CL]Condition-Transforming Variational AutoEncoder for Conversation Response Generation
    Yu-Ping Ruan, Zhen-Hua Ling, Quan Liu, Zhigang Chen, Nitin Indurkhya
    http://arxiv.org/abs/1904.10610v1

    • [cs.CL]Detecting Machine-Translated Paragraphs by Matching Similar Words
    Hoang-Quoc Nguyen-Son, Tran Phuong Thao, Seira Hidano, Shinsaku Kiyomoto
    http://arxiv.org/abs/1904.10641v1

    • [cs.CL]End-to-End Spoken Language Translation
    Michelle Guo, Albert Haque, Prateek Verma
    http://arxiv.org/abs/1904.10760v1

    • [cs.CL]Listening between the Lines: Learning Personal Attributes from Conversations
    Anna Tigunova, Andrew Yates, Paramita Mirza, Gerhard Weikum
    http://arxiv.org/abs/1904.10887v1

    • [cs.CL]Natural Language Interactions in Autonomous Vehicles: Intent Detection and Slot Filling from Passenger Utterances
    Eda Okur, Shachi H Kumar, Saurav Sahay, Asli Arslan Esme, Lama Nachman
    http://arxiv.org/abs/1904.10500v1

    • [cs.CL]Objective Assessment of Social Skills Using Automated Language Analysis for Identification of Schizophrenia and Bipolar Disorder
    Rohit Voleti, Stephanie Woolridge, Julie M. Liss, Melissa Milanovic, Christopher R. Bowie, Visar Berisha
    http://arxiv.org/abs/1904.10622v1

    • [cs.CL]On the Contributions of Visual and Textual Supervision in Low-resource Semantic Speech Retrieval
    Ankita Pasad, Bowen Shi, Herman Kamper, Karen Livescu
    http://arxiv.org/abs/1904.10947v1

    • [cs.CL]Semantic Drift in Multilingual Representations
    Lisa Beinborn, Rochelle Choenni
    http://arxiv.org/abs/1904.10820v1

    • [cs.CL]Who Blames Whom in a Crisis? Detecting Blame Ties from News Articles Using Neural Networks
    Shuailong Liang, Olivia Nicol, Yue Zhang
    http://arxiv.org/abs/1904.10637v1

    • [cs.CV]A CNN-RNN Architecture for Multi-Label Weather Recognition
    Bin Zhao, Xuelong Li, Xiaoqiang Lu, Zhigang Wang
    http://arxiv.org/abs/1904.10709v1

    • [cs.CV]A General Framework for Edited Video and Raw Video Summarization
    Xuelong Li, Bin Zhao, Xiaoqiang Lu
    http://arxiv.org/abs/1904.10669v1

    • [cs.CV]A Large-scale Varying-view RGB-D Action Dataset for Arbitrary-view Human Action Recognition
    Yanli Ji, Feixiang Xu, Yang Yang, Fumin Shen, Heng Tao Shen, Wei-Shi Zheng
    http://arxiv.org/abs/1904.10681v1

    • [cs.CV]A Novel Re-weighting Method for Connectionist Temporal Classification
    Hongzhu Li, Weiqiang Wang
    http://arxiv.org/abs/1904.10619v1

    • [cs.CV]Automatic cephalometric landmarks detection on frontal faces: an approach based on supervised learning techniques
    Lucas Faria Porto, Laise Nascimento Correia Lima, Marta Flores, Andrea Valsecchi, Oscar Ibanez, Carlos Eduardo Machado Palhares, Flavio de Barros Vidal
    http://arxiv.org/abs/1904.10816v1

    • [cs.CV]Bidirectional Learning for Domain Adaptation of Semantic Segmentation
    Yunsheng Li, Lu Yuan, Nuno Vasconcelos
    http://arxiv.org/abs/1904.10620v1

    • [cs.CV]CED: Color Event Camera Dataset
    Cedric Scheerlinck, Henri Rebecq, Timo Stoffregen, Nick Barnes, Robert Mahony, Davide Scaramuzza
    http://arxiv.org/abs/1904.10772v1

    • [cs.CV]Computer-aided diagnosis in histopathological images of the endometrium using a convolutional neural network and attention mechanisms
    Hao Sun, Xianxu Zeng, Tao Xu, Gang Peng, Yutao Ma
    http://arxiv.org/abs/1904.10626v1

    • [cs.CV]Detailed Human Shape Estimation from a Single Image by Hierarchical Mesh Deformation
    Hao Zhu, Xinxin Zuo, Sen Wang, Xun Cao, Ruigang Yang
    http://arxiv.org/abs/1904.10506v1

    • [cs.CV]Improving Few-Shot User-Specific Gaze Adaptation via Gaze Redirection Synthesis
    Yu Yu, Gang Liu, Jean-Marc Odobez
    http://arxiv.org/abs/1904.10638v1

    • [cs.CV]Informative sample generation using class aware generative adversarial networks for classification of chest Xrays
    Behzad Bozorgtabar, Dwarikanath Mahapatra
    http://arxiv.org/abs/1904.10781v1

    • [cs.CV]LFFD: A Light and Fast Face Detector for Edge Devices
    Yonghao He, Dezhong Xu, Lifang Wu, Meng Jian, Shiming Xiang, Chunhong Pan
    http://arxiv.org/abs/1904.10633v1

    • [cs.CV]Multi-scale deep neural networks for real image super-resolution
    Shangqi Gao, Xiahai Zhuang
    http://arxiv.org/abs/1904.10698v1

    • [cs.CV]Neural Collaborative Subspace Clustering
    Tong Zhang, Pan Ji, Mehrtash Harandi, Wenbing Huang, Hongdong Li
    http://arxiv.org/abs/1904.10596v1

    • [cs.CV]Optical machine learning with incoherent light and a single-pixel detector
    Shuming Jiao, Xiang Li, Zibang Zhang, Yang Gao, Ting Lei, Zhenwei Xie, Xiaocong Yuan
    http://arxiv.org/abs/1904.10851v1

    • [cs.CV]Segmenting the Future
    Hsu-kuang Chiu, Ehsan Adeli, Juan Carlos Niebles
    http://arxiv.org/abs/1904.10666v1

    • [cs.CV]Simultaneous regression and feature learning for facial landmarking
    Janez Križaj, Peter Peer, Vitomir Štruc, Simon Dobrišek
    http://arxiv.org/abs/1904.10787v1

    • [cs.CV]Super-resolution based generative adversarial network using visual perceptual loss function
    Xuan Zhu, Yue Cheng, Rongzhi Wang
    http://arxiv.org/abs/1904.10654v1

    • [cs.CV]The VGG Image Annotator (VIA)
    Abhishek Dutta, Andrew Zisserman
    http://arxiv.org/abs/1904.10699v1

    • [cs.CV]The iterative convolution-thresholding method (ICTM) for image segmentation
    Dong Wang, Xiao-Ping Wang
    http://arxiv.org/abs/1904.10917v1

    • [cs.CV]Understanding Art through Multi-Modal Retrieval in Paintings
    Noa Garcia, Benjamin Renoust, Yuta Nakashima
    http://arxiv.org/abs/1904.10615v1

    • [cs.CV]Unfocused images removal of z-axis overlapping Mie scattering particles by using three-dimensional nonlinear diffusion based on digital holography
    Wei-Na Li, Zhengyun Zhang, Jianshe Ma, Xiaohao Wang, Ping Su
    http://arxiv.org/abs/1904.10613v1

    • [cs.CV]ViDeNN: Deep Blind Video Denoising
    Michele Claus, Jan van Gemert
    http://arxiv.org/abs/1904.10898v1

    • [cs.CY]The Ex-Ante View of Recommender System Design
    Guy Aridor, Duarte Goncalves, Shan Sikdar
    http://arxiv.org/abs/1904.10527v1

    • [cs.DC]Chunkflow: Distributed Hybrid Cloud Processing of Large 3D Images by Convolutional Nets
    Jingpeng Wu, William M. Silversmith, H. Sebastian Seung
    http://arxiv.org/abs/1904.10489v1

    • [cs.DC]Distributed Continuous Range-Skyline Query Monitoring over the Internet of Mobile Things
    Chuan-Chi Lai, Zulhaydar Fairozal Akbar, Chuan-Ming Liu, Van-Dai Ta, Li-Chun Wang
    http://arxiv.org/abs/1904.10889v1

    • [cs.DC]Fast Distributed Algorithms for LP-Type Problems of Bounded Dimension
    Kristian Hinnenthal, Christian Scheideler, Martijn Struijs
    http://arxiv.org/abs/1904.10706v1

    • [cs.GR]OperatorNet: Recovering 3D Shapes From Difference Operators
    Ruqi Huang, Marie-Julie Rakotosaona, Panos Achlioptas, Leonidas Guibas, Maks Ovsjanikov
    http://arxiv.org/abs/1904.10754v1

    • [cs.IR]Fine-Grained Named Entity Recognition using ELMo and Wikidata
    Cihan Dogan, Aimore Dutra, Adam Gara, Alfredo Gemma, Lei Shi, Michael Sigamani, Ella Walters
    http://arxiv.org/abs/1904.10503v1

    • [cs.IR]Integrating Social Media into a Pan-European Flood Awareness System: A Multilingual Approach
    V. Lorini, C. Castillo, F. Dottori, M. Kalas, D. Nappo, P. Salamon
    http://arxiv.org/abs/1904.10876v1

    • [cs.IR]Latent Variable Session-Based Recommendation
    David Rohde, Stephen Bonner
    http://arxiv.org/abs/1904.10784v1

    • [cs.IR]Three Methods for Training on Bandit Feedback
    Dmytro Mykhaylov, David Rohde, Flavian Vasile
    http://arxiv.org/abs/1904.10799v1

    • [cs.IT]Energy-Efficient Mobile-Edge Computation Offloading over Multiple Fading Blocks
    Rongfei Fan, Fudong Li, Song Jin, Gongpu Wang, Hai Jiang, Shaohua Wu
    http://arxiv.org/abs/1904.10586v1

    • [cs.IT]Fixed-Symbol Aided Random Access Scheme for Machine-to-Machine Communications
    Zhaoji Zhang, Ying Li, Lei Liu, Wei Hou
    http://arxiv.org/abs/1904.10874v1

    • [cs.IT]Group codes over fields are asymptotically good
    Martino Borello, Wolfgang Willems
    http://arxiv.org/abs/1904.10885v1

    • [cs.IT]Joint Long-Term Cache Allocation and Short-Term Content Delivery in Green Cloud Small Cell Networks
    Xiongwei Wu, Qiang Li, Xiuhua Li, Victor C. M. Leung, P. C. Ching
    http://arxiv.org/abs/1904.10882v1

    • [cs.IT]Obtaining binary perfect codes out of tilings
    Gabriella Akemi Miyamoto, Marcelo Firer
    http://arxiv.org/abs/1904.10789v1

    • [cs.IT]Secure Communication in Dynamic Wireless Ad hoc Networks
    B. N. Bharath, K. G. Nagananda
    http://arxiv.org/abs/1904.10856v1

    • [cs.IT]Throughput Maximization in Two-hop DF Multiple-Relay Network with Simultaneous Wireless Information and Power Transfer
    Qi Gu, Gongpu Wang, Rongfei Fan, Ning Zhang, Zhangdui Zhong
    http://arxiv.org/abs/1904.10589v1

    • [cs.LG]A Comparison Study of Credit Card Fraud Detection: Supervised versus Unsupervised
    Xuetong Niu, Li Wang, Xulei Yang
    http://arxiv.org/abs/1904.10604v1

    • [cs.LG]A bag-of-concepts model improves relation extraction in a narrow knowledge domain with limited data
    Jiyu Chen, Karin Verspoor, Zenan Zhai
    http://arxiv.org/abs/1904.10743v1

    • [cs.LG]Baconian: A Unified Opensource Framework for Model-Based Reinforcement Learning
    Linsen Dong, Guanyu Gao, Yuanlong Li, Yonggang Wen
    http://arxiv.org/abs/1904.10762v1

    • [cs.LG]Beyond Adaptive Submodularity: Approximation Guarantees of Greedy Policy with Adaptive Submodularity Ratio
    Kaito Fujii, Shinsaku Sakaue
    http://arxiv.org/abs/1904.10748v1

    • [cs.LG]Block-distributed Gradient Boosted Trees
    Theodore Vasiloudis, Hyunsu Cho, Henrik Boström
    http://arxiv.org/abs/1904.10522v1

    • [cs.LG]CascadeML: An Automatic Neural Network Architecture Evolution and Training Algorithm for Multi-label Classification
    Arjun Pakrashi, Brian Mac Namee
    http://arxiv.org/abs/1904.10551v1

    • [cs.LG]Concise Fuzzy System Modeling Integrating Soft Subspace Clustering and Sparse Learning
    Peng Xu, Zhaohong Deng, Chen Cui, Te Zhang, Kup-Sze Choi, Gu Suhang, Jun Wang, ShiTong Wang
    http://arxiv.org/abs/1904.10683v1

    • [cs.LG]Deep Learning for Classification of Hyperspectral Data: A Comparative Review
    Nicolas Audebert, Bertrand Saux, Sébastien Lefèvre
    http://arxiv.org/abs/1904.10674v1

    • [cs.LG]Deep Q-Learning for Nash Equilibria: Nash-DQN
    Philippe Casgrain, Brian Ning, Sebastian Jaimungal
    http://arxiv.org/abs/1904.10554v1

    • [cs.LG]Design Automation for Efficient Deep Learning Computing
    Song Han, Han Cai, Ligeng Zhu, Ji Lin, Kuan Wang, Zhijian Liu, Yujun Lin
    http://arxiv.org/abs/1904.10616v1

    • [cs.LG]Differentiable Pruning Method for Neural Networks
    Jaedeok Kim, Chiyoun Park, Hyun-Joo Jung, Yoonsuck Choe
    http://arxiv.org/abs/1904.10921v1

    • [cs.LG]Facilitating Bayesian Continual Learning by Natural Gradients and Stein Gradients
    Yu Chen, Tom Diethe, Neil Lawrence
    http://arxiv.org/abs/1904.10644v1

    • [cs.LG]Generated Loss and Augmented Training of MNIST VAE
    Jason Chou
    http://arxiv.org/abs/1904.10937v1

    • [cs.LG]Generating Long Sequences with Sparse Transformers
    Rewon Child, Scott Gray, Alec Radford, Ilya Sutskever
    http://arxiv.org/abs/1904.10509v1

    • [cs.LG]Generating Token-Level Explanations for Natural Language Inference
    James Thorne, Andreas Vlachos, Christos Christodoulopoulos, Arpit Mittal
    http://arxiv.org/abs/1904.10717v1

    • [cs.LG]Integer Programming for Learning Directed Acyclic Graphs from Continuous Data
    Hasan Manzour, Simge Küçükyavuz, Ali Shojaie
    http://arxiv.org/abs/1904.10574v1

    • [cs.LG]KFHE-HOMER: Kalman Filter-based Heuristic Ensemble of HOMER for Multi-Label Classification
    Arjun Pakrashi, Brian Mac Namee
    http://arxiv.org/abs/1904.10552v1

    • [cs.LG]Layer Dynamics of Linearised Neural Nets
    Saurav Basu, Koyel Mukherjee, Shrihari Vasudevan
    http://arxiv.org/abs/1904.10689v1

    • [cs.LG]Learning Bodily and Temporal Attention in Protective Movement Behavior Detection
    Chongyang Wang, Min Peng, Temitayo A. Olugbade, Nicholas D. Lane, Amanda C. De C. Williams, Nadia Bianchi-Berthouze
    http://arxiv.org/abs/1904.10824v1

    • [cs.LG]Low-Memory Neural Network Training: A Technical Report
    Nimit Sharad Sohoni, Christopher Richard Aberger, Megan Leszczynski, Jian Zhang, Christopher Ré
    http://arxiv.org/abs/1904.10631v1

    • [cs.LG]Maximum Entropy Based Significance of Itemsets
    Nikolaj Tatti
    http://arxiv.org/abs/1904.10632v1

    • [cs.LG]Neural Logic Reinforcement Learning
    Zhengyao Jiang, Shan Luo
    http://arxiv.org/abs/1904.10729v1

    • [cs.LG]Prediction of Progression to Alzheimer`s disease with Deep InfoMax
    Alex Fedorov, R Devon Hjelm, Anees Abrol, Zening Fu, Yuhui Du, Sergey Plis, Vince D. Calhoun
    http://arxiv.org/abs/1904.10931v1

    • [cs.LG]Quantum-assisted associative adversarial network: Applying quantum annealing in deep learning
    Max Wilson, Thomas Vandal, Tad Hogg, Eleanor Rieffel
    http://arxiv.org/abs/1904.10573v1

    • [cs.LG]Some Limit Properties of Markov Chains Induced by Stochastic Recursive Algorithms
    Abhishek Gupta, Gaurav Tendolkar, Hao Chen, Jianzong Pi
    http://arxiv.org/abs/1904.10778v1

    • [cs.LG]Stochastic Lipschitz Q-Learning
    Xu Zhu, David Dunson
    http://arxiv.org/abs/1904.10653v1

    • [cs.LG]Target-Based Temporal Difference Learning
    Donghwan Lee, Niao He
    http://arxiv.org/abs/1904.10945v1

    • [cs.LG]The Scientific Method in the Science of Machine Learning
    Jessica Zosa Forde, Michela Paganini
    http://arxiv.org/abs/1904.10922v1

    • [cs.LG]Towards Combining On-Off-Policy Methods for Real-World Applications
    Kai-Chun Hu, Chen-Huan Pi, Ting Han Wei, I-Chen Wu, Stone Cheng, Yi-Wei Dai, Wei-Yuan Ye
    http://arxiv.org/abs/1904.10642v1

    • [cs.LG]Unsupervised Assignment Flow: Label Learning on Feature Manifolds by Spatially Regularized Geometric Assignment
    Artjom Zern, Matthias Zisler, Stefania Petra, Christoph Schnörr
    http://arxiv.org/abs/1904.10863v1

    • [cs.LG]Wearable-based Parkinson’s Disease Severity Monitoring using Deep Learning
    Jann Goschenhofer, Franz MJ Pfister, Kamer Ali Yuksel, Bernd Bischl, Urban Fietzek, Janek Thomas
    http://arxiv.org/abs/1904.10829v1

    • [cs.NA]Low-Rank Tucker Approximation of a Tensor From Streaming Data
    Yiming Sun, Yang Guo, Charlene Luo, Joel Tropp, Madeleine Udell
    http://arxiv.org/abs/1904.10951v1

    • [cs.NE]Balanced Crossover Operators in Genetic Algorithms
    Luca Manzoni, Luca Mariot, Eva Tuba
    http://arxiv.org/abs/1904.10494v1

    • [cs.NE]Evolving Neural Networks in Reinforcement Learning by means of UMDAc
    Mikel Malagon, Josu Ceberio
    http://arxiv.org/abs/1904.10932v1

    • [cs.NE]Mapping Hearthstone Deck Spaces through MAP-Elites with Sliding Boundaries
    Matthew C. Fontaine, Scott Lee, L. B. Soros, Fernando De Mesentier Silva, Julian Togelius, Amy K. Hoover
    http://arxiv.org/abs/1904.10656v1

    • [cs.RO]Bayesian Gaussian mixture model for robotic policy imitation
    Emmanuel Pignat, Sylvain Calinon
    http://arxiv.org/abs/1904.10716v1

    • [cs.RO]Tactile Mapping and Localization from High-Resolution Tactile Imprints
    Maria Bauza, Oleguer Canal, Alberto Rodriguez
    http://arxiv.org/abs/1904.10944v1

    • [cs.SD]Realizing Petabyte Scale Acoustic Modeling
    Sree Hari Krishnan Parthasarathi, Nitin Sivakrishnan, Pranav Ladkat, Nikko Strom
    http://arxiv.org/abs/1904.10584v1

    • [cs.SD]Unsupervised Adversarial Domain Adaptation Based On The Wasserstein Distance For Acoustic Scene Classification
    Konstantinos Drossos, Paul Magron, Tuomas Virtanen
    http://arxiv.org/abs/1904.10678v1

    • [cs.SY]Autonomous Voltage Control for Grid Operation Using Deep Reinforcement Learning
    Ruisheng Diao, Zhiwei Wang, Di Shi, Qianyun Chang, Jiajun Duan, Xiaohu Zhang
    http://arxiv.org/abs/1904.10597v1

    • [math.FA]Native Banach spaces for splines and variational inverse problems
    Michael Unser, Julien Fageot
    http://arxiv.org/abs/1904.10818v1

    • [math.OC]Efficient Simulation Budget Allocation for Subset Selection Using Regression Metamodels
    Fei Gao, Zhongshun Shi, Siyang Gao, Hui Xiao
    http://arxiv.org/abs/1904.10639v1

    • [math.PR]Drift Estimation for Discretely Sampled SPDEs
    Igor Cialenco, Francisco Delgado-Vences, Hyun-Jung Kim
    http://arxiv.org/abs/1904.10884v1

    • [math.PR]On laws exhibiting universal ordering under stochastic restart
    Matija Vidmar
    http://arxiv.org/abs/1904.10495v1

    • [math.ST]Prediction bounds for (higher order) total variationregularized least squares
    Sara van de Geer, Francesco Ortelli
    http://arxiv.org/abs/1904.10871v1

    • [math.ST]Unbiased truncated quadratic variation for volatility estimation in jump diffusion processes
    Chiara Amorino, Arnaud Gloter
    http://arxiv.org/abs/1904.10660v1

    • [physics.ao-ph]Applying machine learning to improve simulations of a chaotic dynamical system using empirical error correction
    Peter A. G. Watson
    http://arxiv.org/abs/1904.10904v1

    • [physics.soc-ph]Containing misinformation spreading in temporal social networks
    Wei Wang, Yuanhui Ma, Tao Wu, Yang Dai, Xingshu Chen, Lidia A. Braunstein
    http://arxiv.org/abs/1904.10801v1

    • [physics.soc-ph]Mercator: uncovering faithful hyperbolic embeddings of complex networks
    Guillermo García-Pérez, Antoine Allard, M. Ángeles Serrano, Marián Boguñá
    http://arxiv.org/abs/1904.10814v1

    • [physics.soc-ph]Optimization of the post-crisis recovery plans in scale-free networks
    Mohammad Bahrami, Narges Chinichian, Ali Hosseiny, Gholamreza Jafari, Marcel Ausloos
    http://arxiv.org/abs/1904.10625v1

    • [physics.soc-ph]Pulse strategy for suppressing spreading on networks
    Qiang Liu, Xiaoyu Zhou, Piet Van Mieghem
    http://arxiv.org/abs/1904.10883v1

    • [q-fin.CP]A neural network-based framework for financial model calibration
    Shuaiqiang Liu, Anastasia Borovykh, Lech A. Grzelak, Cornelis W. Oosterlee
    http://arxiv.org/abs/1904.10523v1

    • [quant-ph]Machine learning for long-distance quantum communication
    Julius Wallnöfer, Alexey A. Melnikov, Wolfgang Dür, Hans J. Briegel
    http://arxiv.org/abs/1904.10797v1

    • [stat.AP]A Cross-validated Ensemble Approach to Robust Hypothesis Testing of Continuous Nonlinear Interactions: Application to Nutrition-Environment Studies
    Jeremiah Zhe Liu, Jane Lee, Pi-i Debby Lin, Linda Valeri, David C. Christiani, David C. Bellinger, Robert O. Wright, Maitreyi M. Mazumdar, Brent A. Coull
    http://arxiv.org/abs/1904.10918v1

    • [stat.AP]Who Gets the Job and How are They Paid? Machine Learning Application on H-1B Case Data
    Barry Ke, Angela Qiao
    http://arxiv.org/abs/1904.10580v1

    • [stat.ME]A penalized likelihood approach for efficiently estimating a partially linear additive transformation model with current status data
    Yan Liu, Minggen Lu, Christopher S. McMahan
    http://arxiv.org/abs/1904.10575v1

    • [stat.ME]Baseline Drift Estimation for Air Quality Data Using Quantile Trend Filtering
    Halley L. Brantley, Joseph Guinness, Eric C. Chi
    http://arxiv.org/abs/1904.10582v1

    • [stat.ME]Comparing Samples from the $\mathcal{G}^0$ Distribution using a Geodesic Distance
    Alejandro C. Frery, Juliana Gambini
    http://arxiv.org/abs/1904.10499v1

    • [stat.ME]Horseshoe Regularization for Machine Learning in Complex and Deep Models
    Anindya Bhadra, Jyotishka Datta, Yunfan Li, Nicholas G. Polson
    http://arxiv.org/abs/1904.10939v1

    • [stat.ME]Trajectory Functional Boxplots
    Zonghui Yao, Wenlin Dai, Marc G. Genton
    http://arxiv.org/abs/1904.10792v1

    • [stat.ML]$S^{2}$-LBI: Stochastic Split Linearized Bregman Iterations for Parsimonious Deep Learning
    Yanwei Fu, Donghao Li, Xinwei Sun, Shun Zhang, Yizhou Wang, Yuan Yao
    http://arxiv.org/abs/1904.10873v1

    • [stat.ML]An Exploratory Analysis of Biased Learners in Soft-Sensing Frames
    Aysun Urhan, Burak Alakent
    http://arxiv.org/abs/1904.10753v1

    • [stat.ML]Bayesian leave-one-out cross-validation for large data
    Måns Magnusson, Michael Riis Andersen, Johan Jonasson, Aki Vehtari
    http://arxiv.org/abs/1904.10679v1

    • [stat.ML]Kernel Mean Embedding of Instance-wise Predictions in Multiple Instance Regression
    Thomas Uriot
    http://arxiv.org/abs/1904.10583v1

    • [stat.ML]Learning big Gaussian Bayesian networks: partition, estimation, and fusion
    Jiaying Gu, Qing Zhou
    http://arxiv.org/abs/1904.10900v1