cs.AI - 人工智能
cs.AR - 硬件体系结构 cs.CG - 计算几何学 cs.CL - 计算与语言 cs.CV - 机器视觉与模式识别 cs.DB - 数据库 cs.DC - 分布式、并行与集群计算 cs.DM - 离散数学 cs.DS - 数据结构与算法 cs.IR - 信息检索 cs.IT - 信息论 cs.LG - 自动学习 cs.LO - 计算逻辑 cs.OH - 其他CS cs.SI - 社交网络与信息网络 eess.AS - 语音处理 eess.IV - 图像与视频处理 eess.SP - 信号处理 math.CO - 组合数学 math.PR - 概率 math.ST - 统计理论 physics.comp-ph - 计算物理学 physics.data-an - 数据分析、 统计和概率 physics.geo-ph - 地球物理学 stat.AP - 应用统计 stat.CO - 统计计算 stat.ME - 统计方法论 stat.ML - (统计)机器学习
• [cs.AI]Effective problem solving using SAT solvers
• [cs.AI]Embedding Biomedical Ontologies by Jointly Encoding Network Structure and Textual Node Descriptors
• [cs.AR]Thread Batching for High-performance Energy-efficient GPU Memory Design
• [cs.CG]Topological Data Analysis with $ε$-net Induced Lazy Witness Complex
• [cs.CL]A Simple and Effective Approach to Automatic Post-Editing with Transfer Learning
• [cs.CL]Conceptor Debiasing of Word Representations Evaluated on WEAT
• [cs.CL]Cost-sensitive Regularization for Label Confusion-aware Event Detection
• [cs.CL]Cumulative Adaptation for BLSTM Acoustic Models
• [cs.CL]DocRED: A Large-Scale Document-Level Relation Extraction Dataset
• [cs.CL]Improving Visual Question Answering by Referring to Generated Paragraph Captions
• [cs.CL]Learning to Ask Unanswerable Questions for Machine Reading Comprehension
• [cs.CL]Meaning to Form: Measuring Systematicity as Information
• [cs.CL]Microsoft AI Challenge India 2018: Learning to Rank Passages for Web Question Answering with Deep Attention Networks
• [cs.CL]NLProlog: Reasoning with Weak Unification for Question Answering in Natural Language
• [cs.CL]Neural Response Generation with Meta-Words
• [cs.CV]A Partially Reversible U-Net for Memory-Efficient Volumetric Image Segmentation
• [cs.CV]A Survey on Deep Learning Architectures for Image-based Depth Reconstruction
• [cs.CV]Connecting Touch and Vision via Cross-Modal Prediction
• [cs.CV]Copy and Paste: A Simple But Effective Initialization Method for Black-Box Adversarial Attacks
• [cs.CV]Cross-View Policy Learning for Street Navigation
• [cs.CV]Direct Image to Point Cloud Descriptors Matching for 6-DOF Camera Localization in Dense 3D Point Cloud
• [cs.CV]Divide and Conquer the Embedding Space for Metric Learning
• [cs.CV]Fusion vectors: Embedding Graph Fusions for Efficient Unsupervised Rank Aggregation
• [cs.CV]Image Captioning: Transforming Objects into Words
• [cs.CV]Low-light Image Enhancement Algorithm Based on Retinex and Generative Adversarial Network
• [cs.CV]Modality Conversion of Handwritten Patterns by Cross Variational Autoencoders
• [cs.CV]MonoLoco: Monocular 3D Pedestrian Localization and Uncertainty Estimation
• [cs.CV]Pseudo-LiDAR++: Accurate Depth for 3D Object Detection in Autonomous Driving
• [cs.CV]R2D2: Reliable and Repeatable Detectors and Descriptors for Joint Sparse Keypoint Detection and Local Feature Extraction
• [cs.CV]Temporal Transformer Networks: Joint Learning of Invariant and Discriminative Time Warping
• [cs.CV]Towards End-to-End Text Spotting in Natural Scenes
• [cs.CV]Universal Barcode Detector via Semantic Segmentation
• [cs.CV]Utilizing the Instability in Weakly Supervised Object Detection
• [cs.DB]Scalable Community Detection over Geo-Social Network
• [cs.DC]A Performance Study of the 2D Ising Model on GPUs
• [cs.DC]Diffusing Your Mobile Apps: Extending In-Network Function Virtualization to Mobile Function Offloading
• [cs.DC]Distributed Optimization for Over-Parameterized Learning
• [cs.DC]Gathering with extremely restricted visibility
• [cs.DM]Link Dimension and Exact Construction of a Graph
• [cs.DS]Dynamic Path-Decomposed Tries
• [cs.IR]Scalable Knowledge Graph Construction from Twitter
• [cs.IR]Topic Modeling via Full Dependence Mixtures
• [cs.IT]A Finite-Length Construction of Irregular Spatially-Coupled Codes
• [cs.IT]A Lattice Based Joint Encryption, Encoding and Modulation Scheme
• [cs.IT]Collaborative Broadcast in O(log log n) Rounds
• [cs.IT]Deep Learning-Based Decoding of Constrained Sequence Codes
• [cs.IT]Optimal $q$-Ary Error Correcting/All Unidirectional Error Detecting Codes
• [cs.LG]A Distribution Dependent and Independent Complexity Analysis of Manifold Regularization
• [cs.LG]A Signal Propagation Perspective for Pruning Neural Networks at Initialization
• [cs.LG]A Tight and Unified Analysis of Extragradient for a Whole Spectrum of Differentiable Games
• [cs.LG]A Variational Autoencoder for Probabilistic Non-Negative Matrix Factorisation
• [cs.LG]Adversarial Training Can Hurt Generalization
• [cs.LG]Anomaly Detection with HMM Gauge Likelihood Analysis
• [cs.LG]Augmenting Neural Networks with First-order Logic
• [cs.LG]Binary Classification using Pairs of Minimum Spanning Trees or N-ary Trees
• [cs.LG]Curriculum Learning for Cumulative Return Maximization
• [cs.LG]Deep Learning Development Environment in Virtual Reality
• [cs.LG]Direct Policy Gradients: Direct Optimization of Policies in Discrete Action Spaces
• [cs.LG]Divide and Conquer: Leveraging Intermediate Feature Representations for Quantized Training of Neural Networks
• [cs.LG]Efficient N-Dimensional Convolutions via Higher-Order Factorization
• [cs.LG]Empirical study of extreme overfitting points of neural networks
• [cs.LG]Epistemic Risk-Sensitive Reinforcement Learning
• [cs.LG]Explaining Landscape Connectivity of Low-cost Solutions for Multilayer Nets
• [cs.LG]Gradient Descent Maximizes the Margin of Homogeneous Neural Networks
• [cs.LG]InfoGAN-CR: Disentangling Generative Adversarial Networks with Contrastive Regularizers
• [cs.LG]Layered SGD: A Decentralized and Synchronous SGD Algorithm for Scalable Deep Neural Network Training
• [cs.LG]Learning to Forget for Meta-Learning
• [cs.LG]Model Agnostic Dual Quality Assessment for Adversarial Machine Learning and an Analysis of Current Neural Networks and Defenses
• [cs.LG]Multigrid Neural Memory
• [cs.LG]Online Active Learning of Reject Option Classifiers
• [cs.LG]Provably Efficient $Q$-learning with Function Approximation via Distribution Shift Error Checking Oracle
• [cs.LG]Recurrent Neural Processes
• [cs.LG]Scalable Neural Architecture Search for 3D Medical Image Segmentation
• [cs.LG]Stochastic Proximal AUC Maximization
• [cs.LG]Sub-policy Adaptation for Hierarchical Reinforcement Learning
• [cs.LG]Support vector machines on the D-Wave quantum annealer
• [cs.LG]Towards Compact and Robust Deep Neural Networks
• [cs.LG]Towards Stable and Efficient Training of Verifiably Robust Neural Networks
• [cs.LG]Variational Federated Multi-Task Learning
• [cs.LO]Dynamic Term-Modal Logics for Epistemic Planning
• [cs.LO]Extensions of Generic DOL for Generic Ontology Design Patterns
• [cs.OH]FPScreen: A Rapid Similarity Search Tool for Massive Molecular Library Based on Molecular Fingerprint Comparison
• [cs.SI]A model of anonymous influence with anti-conformist agents
• [cs.SI]An analysis of community structure in Brazilian political topic-based Twitter networks
• [cs.SI]Disentangling Mixtures of Epidemics on Graphs
• [eess.AS]Speaker-Targeted Audio-Visual Models for Speech Recognition in Cocktail-Party Environments
• [eess.AS]Video-Driven Speech Reconstruction using Generative Adversarial Networks
• [eess.IV]Deep neural network for fringe pattern filtering and normalisation
• [eess.IV]GAN-based Multiple Adjacent Brain MRI Slice Reconstruction for Unsupervised Alzheimer’s Disease Diagnosis
• [eess.IV]Global and Local Interpretability for Cardiac MRI Classification
• [eess.IV]Multi Scale Curriculum CNN for Context-Aware Breast MRI Malignancy Classification
• [eess.SP]Cache-Aided Non-Orthogonal Multiple Access for 5G-Enabled Vehicular Networks
• [eess.SP]Convolutional Neural Network based Multiple-Rate Compressive Sensing for Massive MIMO CSI Feedback: Design, Simulation, and Analysis
• [eess.SP]Massive MIMO Radar for Target Detection
• [eess.SP]Multi-Carrier Agile Phased Array Radar
• [eess.SP]Self-Tuning Sectorization: Deep Reinforcement Learning Meets Broadcast Beam Optimization
• [math.CO]Complexity of Dependencies in Bounded Domains, Armstrong Codes, and Generalizations
• [math.PR]Peskun-Tierney ordering for Markov chain and process Monte Carlo: beyond the reversible scenario
• [math.ST]A technical note on divergence of the Wald statistic
• [physics.comp-ph]Computing Committor Functions for the Study of Rare Events Using Deep Learning
• [physics.data-an]hepaccelerate: Fast Analysis of Columnar Collider Data
• [physics.geo-ph]Machine Learning Approach to Earthquake Rupture Dynamics
• [stat.AP]Comparison of Methods for the Assessment of Nonlinearity in Short-Term Heart Rate Variability under different Physiopathological States
• [stat.AP]Early Detection of Long Term Evaluation Criteria in Online Controlled Experiments
• [stat.AP]Exploiting Convexification for Bayesian Optimal Sensor Placement by Maximization of Mutual Information
• [stat.AP]Machine Learning on EPEX Order Books: Insights and Forecasts
• [stat.CO]Robustly estimating the marginal likelihood for cognitive models via importance sampling
• [stat.ME]A Latent Gaussian Process Model for Analyzing Intensive Longitudinal Data
• [stat.ME]Identify treatment effect patterns for personalised decisions
• [stat.ME]Statistical Inference for Generative Models with Maximum Mean Discrepancy
• [stat.ML]$(1 + \varepsilon)$-class Classification: an Anomaly Detection Method for Highly Imbalanced or Incomplete Data Sets
• [stat.ML]A stochastic alternating minimizing method for sparse phase retrieval
• [stat.ML]Distributionally Robust Counterfactual Risk Minimization
• [stat.ML]Empirical Bayes Method for Boltzmann Machines
• [stat.ML]Learning Landmark-Based Ensembles with Random Fourier Features and Gradient Boosting
• [stat.ML]Spectrally-truncated kernel ridge regression and its free lunch
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• [cs.AI]Effective problem solving using SAT solvers
Curtis Bright, Jürgen Gerhard, Ilias Kotsireas, Vijay Ganesh
http://arxiv.org/abs/1906.06251v1
• [cs.AI]Embedding Biomedical Ontologies by Jointly Encoding Network Structure and Textual Node Descriptors
Sotiris Kotitsas, Dimitris Pappas, Ion Androutsopoulos, Ryan McDonald, Marianna Apidianaki
http://arxiv.org/abs/1906.05939v1
• [cs.AR]Thread Batching for High-performance Energy-efficient GPU Memory Design
Bing Li, Mengjie Mao, Xiaoxiao Liu, Tao Liu, Zihao Liu, Wujie Wen, Yiran Chen, Hai, Li
http://arxiv.org/abs/1906.05922v1
• [cs.CG]Topological Data Analysis with $ε$-net Induced Lazy Witness Complex
Naheed Anjum Arafat, Debabrota Basu, Stéphane Bressan
http://arxiv.org/abs/1906.06122v1
• [cs.CL]A Simple and Effective Approach to Automatic Post-Editing with Transfer Learning
Gonçalo M. Correia, André F. T. Martins
http://arxiv.org/abs/1906.06253v1
• [cs.CL]Conceptor Debiasing of Word Representations Evaluated on WEAT
Saket Karve, Lyle Ungar, João Sedoc
http://arxiv.org/abs/1906.05993v1
• [cs.CL]Cost-sensitive Regularization for Label Confusion-aware Event Detection
Hongyu Lin, Yaojie Lu, Xianpei Han, Le Sun
http://arxiv.org/abs/1906.06003v1
• [cs.CL]Cumulative Adaptation for BLSTM Acoustic Models
Markus Kitza, Pavel Golik, Ralf Schlüter, Hermann Ney
http://arxiv.org/abs/1906.06207v1
• [cs.CL]DocRED: A Large-Scale Document-Level Relation Extraction Dataset
Yuan Yao, Deming Ye, Peng Li, Xu Han, Yankai Lin, Zhenghao Liu, Zhiyuan Liu, Lixin Huang, Jie Zhou, Maosong Sun
http://arxiv.org/abs/1906.06127v1
• [cs.CL]Improving Visual Question Answering by Referring to Generated Paragraph Captions
Hyounghun Kim, Mohit Bansal
http://arxiv.org/abs/1906.06216v1
• [cs.CL]Learning to Ask Unanswerable Questions for Machine Reading Comprehension
Haichao Zhu, Li Dong, Furu Wei, Wenhui Wang, Bing Qin, Ting Liu
http://arxiv.org/abs/1906.06045v1
• [cs.CL]Meaning to Form: Measuring Systematicity as Information
Tiago Pimentel, Arya D. McCarthy, Damián E. Blasi, Brian Roark, Ryan Cotterell
http://arxiv.org/abs/1906.05906v1
• [cs.CL]Microsoft AI Challenge India 2018: Learning to Rank Passages for Web Question Answering with Deep Attention Networks
Chaitanya Sai Alaparthi
http://arxiv.org/abs/1906.06056v1
• [cs.CL]NLProlog: Reasoning with Weak Unification for Question Answering in Natural Language
Leon Weber, Pasquale Minervini, Jannes Münchmeyer, Ulf Leser, Tim Rocktäschel
http://arxiv.org/abs/1906.06187v1
• [cs.CL]Neural Response Generation with Meta-Words
Can Xu, Wei Wu, Chongyang Tao, Huang Hu, Matt Schuerman, Ying Wang
http://arxiv.org/abs/1906.06050v1
• [cs.CV]A Partially Reversible U-Net for Memory-Efficient Volumetric Image Segmentation
Robin Brügger, Christian F. Baumgartner, Ender Konukoglu
http://arxiv.org/abs/1906.06148v1
• [cs.CV]A Survey on Deep Learning Architectures for Image-based Depth Reconstruction
Hamid Laga
http://arxiv.org/abs/1906.06113v1
• [cs.CV]Connecting Touch and Vision via Cross-Modal Prediction
Yunzhu Li, Jun-Yan Zhu, Russ Tedrake, Antonio Torralba
http://arxiv.org/abs/1906.06322v1
• [cs.CV]Copy and Paste: A Simple But Effective Initialization Method for Black-Box Adversarial Attacks
Thomas Brunner, Frederik Diehl, Alois Knoll
http://arxiv.org/abs/1906.06086v1
• [cs.CV]Cross-View Policy Learning for Street Navigation
Ang Li, Huiyi Hu, Piotr Mirowski, Mehrdad Farajtabar
http://arxiv.org/abs/1906.05930v1
• [cs.CV]Direct Image to Point Cloud Descriptors Matching for 6-DOF Camera Localization in Dense 3D Point Cloud
Uzair Nadeem, Mohammad A. A. K. Jalwana, Mohammed Bennamoun, Roberto Togneri, Ferdous Sohel
http://arxiv.org/abs/1906.06064v1
• [cs.CV]Divide and Conquer the Embedding Space for Metric Learning
Artsiom Sanakoyeu, Vadim Tschernezki, Uta Büchler, Björn Ommer
http://arxiv.org/abs/1906.05990v1
• [cs.CV]Fusion vectors: Embedding Graph Fusions for Efficient Unsupervised Rank Aggregation
Icaro Cavalcante Dourado, Ricardo da Silva Torres
http://arxiv.org/abs/1906.06011v1
• [cs.CV]Image Captioning: Transforming Objects into Words
Simao Herdade, Armin Kappeler, Kofi Boakye, Joao Soares
http://arxiv.org/abs/1906.05963v1
• [cs.CV]Low-light Image Enhancement Algorithm Based on Retinex and Generative Adversarial Network
Yangming Shi, Xiaopo Wu, Ming Zhu
http://arxiv.org/abs/1906.06027v1
• [cs.CV]Modality Conversion of Handwritten Patterns by Cross Variational Autoencoders
Taichi Sumi, Brian Kenji Iwana, Hideaki Hayashi, Seiichi Uchida
http://arxiv.org/abs/1906.06142v1
• [cs.CV]MonoLoco: Monocular 3D Pedestrian Localization and Uncertainty Estimation
Lorenzo Bertoni, Sven Kreiss, Alexandre Alahi
http://arxiv.org/abs/1906.06059v1
• [cs.CV]Pseudo-LiDAR++: Accurate Depth for 3D Object Detection in Autonomous Driving
Yurong You, Yan Wang, Wei-Lun Chao, Divyansh Garg, Geoff Pleiss, Bharath Hariharan, Mark Campbell, Kilian Q. Weinberger
http://arxiv.org/abs/1906.06310v1
• [cs.CV]R2D2: Reliable and Repeatable Detectors and Descriptors for Joint Sparse Keypoint Detection and Local Feature Extraction
Jerome Revaud, Philippe Weinzaepfel, César De Souza, Noe Pion, Gabriela Csurka, Yohann Cabon, Martin Humenberger
http://arxiv.org/abs/1906.06195v1
• [cs.CV]Temporal Transformer Networks: Joint Learning of Invariant and Discriminative Time Warping
Suhas Lohit, Qiao Wang, Pavan Turaga
http://arxiv.org/abs/1906.05947v1
• [cs.CV]Towards End-to-End Text Spotting in Natural Scenes
Hui Li, Peng Wang, Chunhua Shen
http://arxiv.org/abs/1906.06013v1
• [cs.CV]Universal Barcode Detector via Semantic Segmentation
Andrey Zharkov, Ivan Zagaynov
http://arxiv.org/abs/1906.06281v1
• [cs.CV]Utilizing the Instability in Weakly Supervised Object Detection
Yan Gao, Boxiao Liu, Nan Guo, Xiaochun Ye, Fang Wan, Haihang You, Dongrui Fan
http://arxiv.org/abs/1906.06023v1
• [cs.DB]Scalable Community Detection over Geo-Social Network
Xiuwen Zheng, Qiyu Liu, Amarnath Gupta
http://arxiv.org/abs/1906.05505v2
• [cs.DC]A Performance Study of the 2D Ising Model on GPUs
Joshua Romero, Mauro Bisson, Massimiliano Fatica, Massimo Bernaschi
http://arxiv.org/abs/1906.06297v1
• [cs.DC]Diffusing Your Mobile Apps: Extending In-Network Function Virtualization to Mobile Function Offloading
Mario Almeida, Liang Wang, Jeremy Blackburn, Konstantina Papagiannaki, Jon Crowcroft
http://arxiv.org/abs/1906.06240v1
• [cs.DC]Distributed Optimization for Over-Parameterized Learning
Chi Zhang, Qianxiao Li
http://arxiv.org/abs/1906.06205v1
• [cs.DC]Gathering with extremely restricted visibility
Rachid Guerraoui, Alexandre Maurer
http://arxiv.org/abs/1906.06239v1
• [cs.DM]Link Dimension and Exact Construction of a Graph
Gunjan S. Mahindre, Anura P. Jayasumana
http://arxiv.org/abs/1906.05916v1
• [cs.DS]Dynamic Path-Decomposed Tries
Shunsuke Kanda, Dominik Köppl, Yasuo Tabei, Kazuhiro Morita, Masao Fuketa
http://arxiv.org/abs/1906.06015v1
• [cs.IR]Scalable Knowledge Graph Construction from Twitter
Omar Alonso, Vasileios Kandylas, Serge-Eric Tremblay
http://arxiv.org/abs/1906.05986v1
• [cs.IR]Topic Modeling via Full Dependence Mixtures
Dan Fisher, Mark Kozdoba, Shie Mannor
http://arxiv.org/abs/1906.06181v1
• [cs.IT]A Finite-Length Construction of Irregular Spatially-Coupled Codes
Homa Esfahanizadeh, Ruiyi Wu, Lara Dolecek
http://arxiv.org/abs/1906.05955v1
• [cs.IT]A Lattice Based Joint Encryption, Encoding and Modulation Scheme
Khadijeh Bagheri, Taraneh Eghlidos, Mohammad-Reza Sadeghi, Daniel Panario
http://arxiv.org/abs/1906.06280v1
• [cs.IT]Collaborative Broadcast in O(log log n) Rounds
Christian Schindelhauer, Aditya Oak, Thomas Janson
http://arxiv.org/abs/1906.05153v2
• [cs.IT]Deep Learning-Based Decoding of Constrained Sequence Codes
Congzhe Cao, Duanshun Li, Ivan Fair
http://arxiv.org/abs/1906.06172v1
• [cs.IT]Optimal $q$-Ary Error Correcting/All Unidirectional Error Detecting Codes
Yeow Meng Chee, Xiande Zhang
http://arxiv.org/abs/1906.06066v1
• [cs.LG]A Distribution Dependent and Independent Complexity Analysis of Manifold Regularization
Alexander Mey, Tom Viering, Marco Loog
http://arxiv.org/abs/1906.06100v1
• [cs.LG]A Signal Propagation Perspective for Pruning Neural Networks at Initialization
Namhoon Lee, Thalaiyasingam Ajanthan, Stephen Gould, Philip H. S. Torr
http://arxiv.org/abs/1906.06307v1
• [cs.LG]A Tight and Unified Analysis of Extragradient for a Whole Spectrum of Differentiable Games
Waïss Azizian, Ioannis Mitliagkas, Simon Lacoste-Julien, Gauthier Gidel
http://arxiv.org/abs/1906.05945v1
• [cs.LG]A Variational Autoencoder for Probabilistic Non-Negative Matrix Factorisation
Steven Squires, Adam Prügel Bennett, Mahesan Niranjan
http://arxiv.org/abs/1906.05912v1
• [cs.LG]Adversarial Training Can Hurt Generalization
Aditi Raghunathan, Sang Michael Xie, Fanny Yang, John C. Duchi, Percy Liang
http://arxiv.org/abs/1906.06032v1
• [cs.LG]Anomaly Detection with HMM Gauge Likelihood Analysis
Boris Lorbeer, Tanja Deutsch, Peter Ruppel, Axel Küpper
http://arxiv.org/abs/1906.06134v1
• [cs.LG]Augmenting Neural Networks with First-order Logic
Tao Li, Vivek Srikumar
http://arxiv.org/abs/1906.06298v1
• [cs.LG]Binary Classification using Pairs of Minimum Spanning Trees or N-ary Trees
Riccardo La Grassa, Ignazio Gallo, Alessandro Calefati, Dimitri Ognibene
http://arxiv.org/abs/1906.06090v1
• [cs.LG]Curriculum Learning for Cumulative Return Maximization
Francesco Foglino, Christiano Coletto Christakou, Ricardo Luna Gutierrez, Matteo Leonetti
http://arxiv.org/abs/1906.06178v1
• [cs.LG]Deep Learning Development Environment in Virtual Reality
Kevin C. VanHorn, Meyer Zinn, Murat Can Cobanoglu
http://arxiv.org/abs/1906.05925v1
• [cs.LG]Direct Policy Gradients: Direct Optimization of Policies in Discrete Action Spaces
Guy Lorberbom, Chris J. Maddison, Nicolas Heess, Tamir Hazan, Daniel Tarlow
http://arxiv.org/abs/1906.06062v1
• [cs.LG]Divide and Conquer: Leveraging Intermediate Feature Representations for Quantized Training of Neural Networks
Ahmed T. Elthakeb, Prannoy Pilligundla, Hadi Esmaeilzadeh
http://arxiv.org/abs/1906.06033v1
• [cs.LG]Efficient N-Dimensional Convolutions via Higher-Order Factorization
Jean Kossaifi, Adrian Bulat, Yannis Panagakis, Maja Pantic
http://arxiv.org/abs/1906.06196v1
• [cs.LG]Empirical study of extreme overfitting points of neural networks
Daniil Merkulov, Ivan Oseledets
http://arxiv.org/abs/1906.06295v1
• [cs.LG]Epistemic Risk-Sensitive Reinforcement Learning
Hannes Eriksson, Christos Dimitrakakis
http://arxiv.org/abs/1906.06273v1
• [cs.LG]Explaining Landscape Connectivity of Low-cost Solutions for Multilayer Nets
Rohith Kuditipudi, Xiang Wang, Holden Lee, Yi Zhang, Zhiyuan Li, Wei Hu, Sanjeev Arora, Rong Ge
http://arxiv.org/abs/1906.06247v1
• [cs.LG]Gradient Descent Maximizes the Margin of Homogeneous Neural Networks
Kaifeng Lyu, Jian Li
http://arxiv.org/abs/1906.05890v1
• [cs.LG]InfoGAN-CR: Disentangling Generative Adversarial Networks with Contrastive Regularizers
Zinan Lin, Kiran Koshy Thekumparampil, Giulia Fanti, Sewoong Oh
http://arxiv.org/abs/1906.06034v1
• [cs.LG]Layered SGD: A Decentralized and Synchronous SGD Algorithm for Scalable Deep Neural Network Training
Kwangmin Yu, Thomas Flynn, Shinjae Yoo, Nicholas D’Imperio
http://arxiv.org/abs/1906.05936v1
• [cs.LG]Learning to Forget for Meta-Learning
Sungyong Baik, Seokil Hong, Kyoung Mu Lee
http://arxiv.org/abs/1906.05895v1
• [cs.LG]Model Agnostic Dual Quality Assessment for Adversarial Machine Learning and an Analysis of Current Neural Networks and Defenses
Danilo Vasconcellos Vargas, Shashank Kotyan
http://arxiv.org/abs/1906.06026v1
• [cs.LG]Multigrid Neural Memory
Tri Huynh, Michael Maire, Matthew R. Walter
http://arxiv.org/abs/1906.05948v1
• [cs.LG]Online Active Learning of Reject Option Classifiers
Kulin Shah, Naresh Manwani
http://arxiv.org/abs/1906.06166v1
• [cs.LG]Provably Efficient $Q$-learning with Function Approximation via Distribution Shift Error Checking Oracle
Simon S. Du, Yuping Luo, Ruosong Wang, Hanrui Zhang
http://arxiv.org/abs/1906.06321v1
• [cs.LG]Recurrent Neural Processes
Timon Willi, Jonathan Masci, Jürgen Schmidhuber, Christian Osendorfer
http://arxiv.org/abs/1906.05915v1
• [cs.LG]Scalable Neural Architecture Search for 3D Medical Image Segmentation
Sungwoong Kim, Ildoo Kim, Sungbin Lim, Woonhyuk Baek, Chiheon Kim, Hyungjoo Cho, Boogeon Yoon, Taesup Kim
http://arxiv.org/abs/1906.05956v1
• [cs.LG]Stochastic Proximal AUC Maximization
Yunwen Lei, Yiming Ying
http://arxiv.org/abs/1906.06053v1
• [cs.LG]Sub-policy Adaptation for Hierarchical Reinforcement Learning
Alexander C. Li, Carlos Florensa, Ignasi Clavera, Pieter Abbeel
http://arxiv.org/abs/1906.05862v1
• [cs.LG]Support vector machines on the D-Wave quantum annealer
Dennis Willsch, Madita Willsch, Hans De Raedt, Kristel Michielsen
http://arxiv.org/abs/1906.06283v1
• [cs.LG]Towards Compact and Robust Deep Neural Networks
Vikash Sehwag, Shiqi Wang, Prateek Mittal, Suman Jana
http://arxiv.org/abs/1906.06110v1
• [cs.LG]Towards Stable and Efficient Training of Verifiably Robust Neural Networks
Huan Zhang, Hongge Chen, Chaowei Xiao, Bo Li, Duane Boning, Cho-Jui Hsieh
http://arxiv.org/abs/1906.06316v1
• [cs.LG]Variational Federated Multi-Task Learning
Luca Corinzia, Joachim M. Buhmann
http://arxiv.org/abs/1906.06268v1
• [cs.LO]Dynamic Term-Modal Logics for Epistemic Planning
Andreas Achen, Andrés Occhipinti Liberman, Rasmus K. Rendsvig
http://arxiv.org/abs/1906.06047v1
• [cs.LO]Extensions of Generic DOL for Generic Ontology Design Patterns
Mihai Codescu, Bernd Krieg-Brückner, Till Mossakowski
http://arxiv.org/abs/1906.06275v1
• [cs.OH]FPScreen: A Rapid Similarity Search Tool for Massive Molecular Library Based on Molecular Fingerprint Comparison
Lijun Wang, Jianbing Gong, Yingxia Zhang, Tianmou Liu, Junhui Gao
http://arxiv.org/abs/1906.06170v1
• [cs.SI]A model of anonymous influence with anti-conformist agents
Michel Grabisch, Alexis Poindron, Agnieszka Rusinowska
http://arxiv.org/abs/1906.06094v1
• [cs.SI]An analysis of community structure in Brazilian political topic-based Twitter networks
Camila P. S. Tautenhain, Rodrigo Francisquini, Mariá C. V. Nascimento
http://arxiv.org/abs/1906.06315v1
• [cs.SI]Disentangling Mixtures of Epidemics on Graphs
Jessica Hoffmann, Soumya Basu, Surbhi Goel, Constantine Caramanis
http://arxiv.org/abs/1906.06057v1
• [eess.AS]Speaker-Targeted Audio-Visual Models for Speech Recognition in Cocktail-Party Environments
Guan-Lin Chao, William Chan, Ian Lane
http://arxiv.org/abs/1906.05962v1
• [eess.AS]Video-Driven Speech Reconstruction using Generative Adversarial Networks
Konstantinos Vougioukas, Pingchuan Ma, Stavros Petridis, Maja Pantic
http://arxiv.org/abs/1906.06301v1
• [eess.IV]Deep neural network for fringe pattern filtering and normalisation
Alan Reyes-Figueroa, Mariano Rivera
http://arxiv.org/abs/1906.06224v1
• [eess.IV]GAN-based Multiple Adjacent Brain MRI Slice Reconstruction for Unsupervised Alzheimer’s Disease Diagnosis
Changhee Han, Leonardo Rundo, Kohei Murao, Zoltán Ádám Milacski, Kazuki Umemoto, Hideki Nakayama, Shin’ichi Satoh
http://arxiv.org/abs/1906.06114v1
• [eess.IV]Global and Local Interpretability for Cardiac MRI Classification
James R. Clough, Ilkay Oksuz, Esther Puyol-Anton, Bram Ruijsink, Andrew P. King, Julia A. Schnabel
http://arxiv.org/abs/1906.06188v1
• [eess.IV]Multi Scale Curriculum CNN for Context-Aware Breast MRI Malignancy Classification
Christoph Haarburger, Michael Baumgartner, Daniel Truhn, Mirjam Broeckmann, Hannah Schneider, Simone Schwabing, Christiane Kuhl, Dorit Merhof
http://arxiv.org/abs/1906.06058v1
• [eess.SP]Cache-Aided Non-Orthogonal Multiple Access for 5G-Enabled Vehicular Networks
Sanjeev Gurugopinath, Paschalis C. Sofotasios, Yousof Al-Hammadi, Sami Muhaidat
http://arxiv.org/abs/1906.06025v1
• [eess.SP]Convolutional Neural Network based Multiple-Rate Compressive Sensing for Massive MIMO CSI Feedback: Design, Simulation, and Analysis
Jiajia Guo, Chao-Kai Wen, Shi Jin, Geoffrey Ye Li
http://arxiv.org/abs/1906.06007v1
• [eess.SP]Massive MIMO Radar for Target Detection
Stefano Fortunati, Luca Sanguinetti, Fulvio Gini, Maria S. Greco
http://arxiv.org/abs/1906.06191v1
• [eess.SP]Multi-Carrier Agile Phased Array Radar
Tianyao Huang, Nir Shlezinger, Xingyu Xu, Dingyou Ma, Yimin Liu, Yonina C. Eldar
http://arxiv.org/abs/1906.06289v1
• [eess.SP]Self-Tuning Sectorization: Deep Reinforcement Learning Meets Broadcast Beam Optimization
Rubayet Shafin, Hao Chen, Young Han Nam, Sooyoung Hur, Jeongho Park, Jianzhong, Zhang, Jeffrey Reed, Lingjia Liu
http://arxiv.org/abs/1906.06021v1
• [math.CO]Complexity of Dependencies in Bounded Domains, Armstrong Codes, and Generalizations
Yeow Meng Chee, Hui Zhang, Xiande Zhang
http://arxiv.org/abs/1906.06070v1
• [math.PR]Peskun-Tierney ordering for Markov chain and process Monte Carlo: beyond the reversible scenario
Christophe Andrieu, Samuel Livingstone
http://arxiv.org/abs/1906.06197v1
• [math.ST]A technical note on divergence of the Wald statistic
Jean-Marie Dufour, Eric Renault, Victoria Zinde-Walsh
http://arxiv.org/abs/1906.05951v1
• [physics.comp-ph]Computing Committor Functions for the Study of Rare Events Using Deep Learning
Qianxiao Li, Bo Lin, Weiqing Ren
http://arxiv.org/abs/1906.06285v1
• [physics.data-an]hepaccelerate: Fast Analysis of Columnar Collider Data
Joosep Pata, Maria Spiropulu
http://arxiv.org/abs/1906.06242v1
• [physics.geo-ph]Machine Learning Approach to Earthquake Rupture Dynamics
Sabber Ahamed, Eric G. Daub
http://arxiv.org/abs/1906.06250v1
• [stat.AP]Comparison of Methods for the Assessment of Nonlinearity in Short-Term Heart Rate Variability under different Physiopathological States
Luca Faes, Manuel Gòmez-Extremera, Riccardo Pernice, Pedro Carpena, Giandomenico Nollo, Alberto Porta, Pedro Bernaola-Galvàn
http://arxiv.org/abs/1906.05918v1
• [stat.AP]Early Detection of Long Term Evaluation Criteria in Online Controlled Experiments
Yoni Schamroth, Liron Gat Kahlon, Boris Rabinovich, David Steinberg
http://arxiv.org/abs/1906.05959v1
• [stat.AP]Exploiting Convexification for Bayesian Optimal Sensor Placement by Maximization of Mutual Information
Pinaky Bhattacharyya, James L. Beck
http://arxiv.org/abs/1906.05953v1
• [stat.AP]Machine Learning on EPEX Order Books: Insights and Forecasts
Simon Schnürch, Andreas Wagner
http://arxiv.org/abs/1906.06248v1
• [stat.CO]Robustly estimating the marginal likelihood for cognitive models via importance sampling
Minh-Ngoc Tran, Marcel Scharth, David Gunawan, Robert Kohn, Scott D. Brown, Guy E. Hawkins
http://arxiv.org/abs/1906.06020v1
• [stat.ME]A Latent Gaussian Process Model for Analyzing Intensive Longitudinal Data
Yunxiao Chen, Siliang Zhang
http://arxiv.org/abs/1906.06095v1
• [stat.ME]Identify treatment effect patterns for personalised decisions
Jiuyong Li, Saisai Ma, Lin Liu, Thuc Duy Le, Jixue Liu, Yizhao Han
http://arxiv.org/abs/1906.06080v1
• [stat.ME]Statistical Inference for Generative Models with Maximum Mean Discrepancy
Francois-Xavier Briol, Alessandro Barp, Andrew B. Duncan, Mark Girolami
http://arxiv.org/abs/1906.05944v1
• [stat.ML]$(1 + \varepsilon)$-class Classification: an Anomaly Detection Method for Highly Imbalanced or Incomplete Data Sets
Maxim Borisyak, Artem Ryzhikov, Andrey Ustyuzhanin, Denis Derkach, Fedor Ratnikov, Olga Mineeva
http://arxiv.org/abs/1906.06096v1
• [stat.ML]A stochastic alternating minimizing method for sparse phase retrieval
Jianfeng Cai, Yuling Jiao, Xiliang Lu, Juntao You
http://arxiv.org/abs/1906.05967v1
• [stat.ML]Distributionally Robust Counterfactual Risk Minimization
Louis Faury, Ugo Tanielian, Flavian Vasile, Elena Smirnova, Elvis Dohmatob
http://arxiv.org/abs/1906.06211v1
• [stat.ML]Empirical Bayes Method for Boltzmann Machines
Muneki Yasuda, Tomoyuki Obuchi
http://arxiv.org/abs/1906.06002v1
• [stat.ML]Learning Landmark-Based Ensembles with Random Fourier Features and Gradient Boosting
Léo Gautheron, Pascal Germain, Amaury Habrard, Emilie Morvant, Marc Sebban, Valentina Zantedeschi
http://arxiv.org/abs/1906.06203v1
• [stat.ML]Spectrally-truncated kernel ridge regression and its free lunch
Arash A. Amini
http://arxiv.org/abs/1906.06276v1