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
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• [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