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.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.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.SD - 声音处理 cs.SI - 社交网络与信息网络 eess.IV - 图像与视频处理 eess.SP - 信号处理 math.NA - 数值分析 math.OC - 优化与控制 math.PR - 概率 math.ST - 统计理论 quant-ph - 量子物理 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
• [astro-ph.SR]Fast Solar Image Classification Using Deep Learning and its Importance for Automation in Solar Physics
• [cs.AI]A Value-based Trust Assessment Model for Multi-agent Systems
• [cs.AI]Foundations of Digital Archæoludology
• [cs.AI]Multiple Policy Value Monte Carlo Tree Search
• [cs.AI]Ordinal Bucketing for Game Trees using Dynamic Quantile Approximation
• [cs.CL]A Lightweight Recurrent Network for Sequence Modeling
• [cs.CL]Assessing The Factual Accuracy of Generated Text
• [cs.CL]Attention Is (not) All You Need for Commonsense Reasoning
• [cs.CL]Constructive Type-Logical Supertagging with Self-Attention Networks
• [cs.CL]Content Word-based Sentence Decoding and Evaluating for Open-domain Neural Response Generation
• [cs.CL]Crowdsourcing and Validating Event-focused Emotion Corpora for German and English
• [cs.CL]DiaBLa: A Corpus of Bilingual Spontaneous Written Dialogues for Machine Translation
• [cs.CL]Do Human Rationales Improve Machine Explanations?
• [cs.CL]Effective writing style imitation via combinatorial paraphrasing
• [cs.CL]Fine-Grained Spoiler Detection from Large-Scale Review Corpora
• [cs.CL]GSN: A Graph-Structured Network for Multi-Party Dialogues
• [cs.CL]Grammar-based Neural Text-to-SQL Generation
• [cs.CL]Improving Open Information Extraction via Iterative Rank-Aware Learning
• [cs.CL]Information Minimization In Emergent Languages
• [cs.CL]Investigating an Effective Character-level Embedding in Korean Sentence Classification
• [cs.CL]Leveraging Pretrained Word Embeddings for Part-of-Speech Tagging of Code Switching Data
• [cs.CL]MathQA: Towards Interpretable Math Word Problem Solving with Operation-Based Formalisms
• [cs.CL]Multi-modal Discriminative Model for Vision-and-Language Navigation
• [cs.CL]MultiQA: An Empirical Investigation of Generalization and Transfer in Reading Comprehension
• [cs.CL]Rewarding Smatch: Transition-Based AMR Parsing with Reinforcement Learning
• [cs.CL]Symbol Emergence as an Interpersonal Multimodal Categorization
• [cs.CL]Using Natural Language Processing to Develop an Automated Orthodontic Diagnostic System
• [cs.CR]Privacy-Preserving Detection of IoT Devices Connected Behind a NAT in a Smart Home Setup
• [cs.CR]Real-Time Adversarial Attacks
• [cs.CR]Using Metrics Suites to Improve the Measurement of Privacy in Graphs
• [cs.CV]3DPalsyNet: A Facial Palsy Grading and Motion Recognition Framework using Fully 3D Convolutional Neural Networks
• [cs.CV]A Riemanian Approach to Blob Detection in Manifold-Valued Images
• [cs.CV]A survey of advances in vision-based vehicle re-identification
• [cs.CV]All-In-One Underwater Image Enhancement using Domain-Adversarial Learning
• [cs.CV]Counting and Segmenting Sorghum Heads
• [cs.CV]Deep Dual Relation Modeling for Egocentric Interaction Recognition
• [cs.CV]Deep interpretable architecture for plant diseases classification
• [cs.CV]Design Light-weight 3D Convolutional Networks for Video Recognition Temporal Residual, Fully Separable Block, and Fast Algorithm
• [cs.CV]Joint Representation of Multiple Geometric Priors via a Shape Decomposition Model for Single Monocular 3D Pose Estimation
• [cs.CV]Large Scale Incremental Learning
• [cs.CV]Multi-Precision Quantized Neural Networks via Encoding Decomposition of -1 and +1
• [cs.CV]Multimodal Joint Emotion and Game Context Recognition in League of Legends Livestreams
• [cs.CV]Multitask Text-to-Visual Embedding with Titles and Clickthrough Data
• [cs.CV]Point Clouds Learning with Attention-based Graph Convolution Networks
• [cs.CV]Rethinking Table Parsing using Graph Neural Networks
• [cs.CV]Scene Text Visual Question Answering
• [cs.CV]Supervised Online Hashing via Similarity Distribution Learning
• [cs.CV]TACNet: Transition-Aware Context Network for Spatio-Temporal Action Detection
• [cs.DB]Efficient Multiway Hash Join on Reconfigurable Hardware
• [cs.DB]ParPaRaw: Massively Parallel Parsing of Delimiter-Separated Raw Data
• [cs.DC]From Global Choreographies to Provably Correct and Efficient Distributed Implementations
• [cs.DC]INFaaS: Managed & Model-less Inference Serving
• [cs.DC]Isolation-Aware Timing Analysis and Design Space Exploration for Predictable and Composable Many-Core Systems
• [cs.DC]The Bloom Clock
• [cs.DC]Tracking in Order to Recover: Recoverable Lock-Free Data Structures
• [cs.DS]Principal Fairness: \ Removing Bias via Projections
• [cs.IR]Threshold-Based Retrieval and Textual Entailment Detection on Legal Bar Exam Questions
• [cs.IT]Age of Information in G/G/1/1 Systems: Age Expressions, Bounds, Special Cases, and Optimization
• [cs.IT]Collaborative Decoding of Polynomial Codes for Distributed Computation
• [cs.IT]Deep Learning for Distributed Optimization: Applications to Wireless Resource Management
• [cs.IT]Log-logarithmic Time Pruned Polar Coding
• [cs.IT]Neural Entropic Estimation: A faster path to mutual information estimation
• [cs.IT]Performance Analysis of Clustered LoRa Networks
• [cs.IT]When Full Duplex Wireless Meets Non-Orthogonal Multiple Access: Opportunities and Challenges
• [cs.LG]A novel hybrid model based on multi-objective Harris hawks optimization algorithm for daily PM2.5 and PM10 forecasting
• [cs.LG]Are Labels Required for Improving Adversarial Robustness?
• [cs.LG]Attentional Policies for Cross-Context Multi-Agent Reinforcement Learning
• [cs.LG]Augmenting Transfer Learning with Semantic Reasoning
• [cs.LG]Bayesian Tensor Factorisation for Bottom-up Hidden Tree Markov Models
• [cs.LG]Bypassing Backdoor Detection Algorithms in Deep Learning
• [cs.LG]Cascaded Algorithm-Selection and Hyper-Parameter Optimization with Extreme-Region Upper Confidence Bound Bandit
• [cs.LG]Combating the Compounding-Error Problem with a Multi-step Model
• [cs.LG]Data-driven Algorithm Selection and Parameter Tuning: Two Case studies in Optimization and Signal Processing
• [cs.LG]Deep Bayesian Optimization on Attributed Graphs
• [cs.LG]DeepShift: Towards Multiplication-Less Neural Networks
• [cs.LG]Deterministic PAC-Bayesian generalization bounds for deep networks via generalizing noise-resilience
• [cs.LG]Discriminative structural graph classification
• [cs.LG]End to end learning and optimization on graphs
• [cs.LG]Exact sampling of determinantal point processes with sublinear time preprocessing
• [cs.LG]Explainability Techniques for Graph Convolutional Networks
• [cs.LG]Extending Deep Model Predictive Control with Safety Augmented Value Estimation from Demonstrations
• [cs.LG]Factorized Inference in Deep Markov Models for Incomplete Multimodal Time Series
• [cs.LG]Fast Online “Next Best Offers” using Deep Learning
• [cs.LG]GENO — GENeric Optimization for Classical Machine Learning
• [cs.LG]Implicit Regularization in Deep Matrix Factorization
• [cs.LG]Interval timing in deep reinforcement learning agents
• [cs.LG]L0 Regularization Based Neural Network Design and Compression
• [cs.LG]Learning Sparse Networks Using Targeted Dropout
• [cs.LG]Leveraging Trust and Distrust in Recommender Systems via Deep Learning
• [cs.LG]Luck Matters: Understanding Training Dynamics of Deep ReLU Networks
• [cs.LG]Max-MIG: an Information Theoretic Approach for Joint Learning from Crowds
• [cs.LG]MolecularRNN: Generating realistic molecular graphs with optimized properties
• [cs.LG]Neural Markov Logic Networks
• [cs.LG]On Value Functions and the Agent-Environment Boundary
• [cs.LG]On the Fairness of Disentangled Representations
• [cs.LG]On the Necessity and Effectiveness of Learning the Prior of Variational Auto-Encoder
• [cs.LG]Ordinal Regression as Structured Classification
• [cs.LG]PowerSGD: Practical Low-Rank Gradient Compression for Distributed Optimization
• [cs.LG]Pre-Training Graph Neural Networks for Generic Structural Feature Extraction
• [cs.LG]Reinforcement Learning Experience Reuse with Policy Residual Representation
• [cs.LG]Reinforcement Learning for Mean Field Game
• [cs.LG]Reinforcement Learning for Slate-based Recommender Systems: A Tractable Decomposition and Practical Methodology
• [cs.LG]Residual Networks as Nonlinear Systems: Stability Analysis using Linearization
• [cs.LG]SELFIES: a robust representation of semantically constrained graphs with an example application in chemistry
• [cs.LG]Scaffold-based molecular design using graph generative model
• [cs.LG]Sequence Modeling of Temporal Credit Assignment for Episodic Reinforcement Learning
• [cs.LG]Subspace Networks for Few-shot Classification
• [cs.LG]Sum-of-squares meets square loss: Fast rates for agnostic tensor completion
• [cs.LG]Time Matters in Regularizing Deep Networks: Weight Decay and Data Augmentation Affect Early Learning Dynamics, Matter Little Near Convergence
• [cs.LG]Time Series Anomaly Detection Using Convolutional Neural Networks and Transfer Learning
• [cs.LG]Uncoupled Regression from Pairwise Comparison Data
• [cs.LG]Updates of Equilibrium Prop Match Gradients of Backprop Through Time in an RNN with Static Input
• [cs.NE]Epsilon-Lexicase Selection for Regression
• [cs.NE]Improved memory in recurrent neural networks with sequential non-normal dynamics
• [cs.NI]Maximizing Clearance Rate by Penalizing Redundant Task Assignment in Mobile Crowdsensing Auctions
• [cs.NI]Reducing Tail Latency via Safe and Simple Duplication
• [cs.RO]2.5D Image based Robotic Grasping
• [cs.RO]Fast and Agile Vision-Based Flight with Teleoperation and Collision Avoidance on a Multirotor
• [cs.RO]Graduated Fidelity Lattices for Motion Planning under Uncertainty
• [cs.RO]Inverting Learned Dynamics Models for Aggressive Multirotor Control
• [cs.RO]Recent Advances in Imitation Learning from Observation
• [cs.RO]Taming Combinatorial Challenges in Optimal Clutter Removal Tasks
• [cs.SD]What does a Car-ssette tape tell?
• [cs.SI]Can We Derive Explicit and Implicit Bias from Corpus?
• [cs.SI]Spotting Collusive Behaviour of Online Fraud Groups in Customer Reviews
• [eess.IV]Combining Noise-to-Image and Image-to-Image GANs: Brain MR Image Augmentation for Tumor Detection
• [eess.IV]Improving the resolution of CryoEM single particle analysis
• [eess.IV]Known-plaintext attack and ciphertext-only attack for encrypted single-pixel imaging
• [eess.IV]Partial Scan Electron Microscopy with Deep Learning
• [eess.SP]A Block Diagonal Markov Model for Indoor Software-Defined Power Line Communication
• [math.NA]Unified Analysis of Periodization-Based Sampling Methods for Matérn Covariances
• [math.OC]ADMM for Efficient Deep Learning with Global Convergence
• [math.OC]Distributed Submodular Minimization via Block-Wise Updates and Communications
• [math.PR]KMT coupling for random walk bridges
• [math.ST]Convergence of Smoothed Empirical Measures with Applications to Entropy Estimation
• [math.ST]State occupation probabilities in non-Markov models
• [quant-ph]Quantum Mean Embedding of Probability Distributions
• [stat.AP]Influences in Forecast Errors for Wind and Photovoltaic Power: A Study on Machine Learning Models
• [stat.CO]Component-wise approximate Bayesian computation via Gibbs-like steps
• [stat.ME]Accumulation Bias in Meta-Analysis: The Need to Consider Time in Error Control
• [stat.ME]Targeted Estimation of L2 Distance Between Densities and its Application to Geo-spatial Data
• [stat.ML]A multi-series framework for demand forecasts in E-commerce
• [stat.ML]Clustered Gaussian Graphical Model via Symmetric Convex Clustering
• [stat.ML]High Dimensional Classification via Empirical Risk Minimization: Improvements and Optimality
• [stat.ML]Neural Likelihoods for Multi-Output Gaussian Processes
• [stat.ML]PAC-Bayesian Transportation Bound
• [stat.ML]RKHSMetaMod : An R package to estimate the Hoeffding decomposition of an unknown function by solving RKHS Ridge Group Sparse optimization problem
• [stat.ML]Reverse KL-Divergence Training of Prior Networks: Improved Uncertainty and Adversarial Robustness
• [stat.ML]Seeing the Wind: Visual Wind Speed Prediction with a Coupled Convolutional and Recurrent Neural Network
• [stat.ML]Sparse Approximate Cross-Validation for High-Dimensional GLMs
• [stat.ML]Training Dynamics of Deep Networks using Stochastic Gradient Descent via Neural Tangent Kernel
• [stat.ML]Unlabeled Data Improves Adversarial Robustness
·····································
• [astro-ph.SR]Fast Solar Image Classification Using Deep Learning and its Importance for Automation in Solar Physics
John A. Armstrong, Lyndsay Fletcher
http://arxiv.org/abs/1905.13575v1
• [cs.AI]A Value-based Trust Assessment Model for Multi-agent Systems
Kinzang Chhogyal, Abhaya Nayak, Aditya Ghose, Hoa Khanh Dam
http://arxiv.org/abs/1905.13380v1
• [cs.AI]Foundations of Digital Archæoludology
Cameron Browne, Dennis J. N. J. Soemers, Éric Piette, Matthew Stephenson, Michael Conrad, Walter Crist, Thierry Depaulis, Eddie Duggan, Fred Horn, Steven Kelk, Simon M. Lucas, João Pedro Neto, David Parlett, Abdallah Saffidine, Ulrich Schädler, Jorge Nuno Silva, Alex de Voogt, Mark H. M. Winands
http://arxiv.org/abs/1905.13516v1
• [cs.AI]Multiple Policy Value Monte Carlo Tree Search
Li-Cheng Lan, Wei Li, Ting-Han Wei, I-Chen Wu
http://arxiv.org/abs/1905.13521v1
• [cs.AI]Ordinal Bucketing for Game Trees using Dynamic Quantile Approximation
Tobias Joppen, Tilman Strübig, Johannes Fürnkranz
http://arxiv.org/abs/1905.13449v1
• [cs.CL]A Lightweight Recurrent Network for Sequence Modeling
Biao Zhang, Rico Sennrich
http://arxiv.org/abs/1905.13324v1
• [cs.CL]Assessing The Factual Accuracy of Generated Text
Ben Goodrich, Vinay Rao, Mohammad Saleh, Peter J Liu
http://arxiv.org/abs/1905.13322v1
• [cs.CL]Attention Is (not) All You Need for Commonsense Reasoning
Tassilo Klein, Moin Nabi
http://arxiv.org/abs/1905.13497v1
• [cs.CL]Constructive Type-Logical Supertagging with Self-Attention Networks
Konstantinos Kogkalidis, Michael Moortgat, Tejaswini Deoskar
http://arxiv.org/abs/1905.13418v1
• [cs.CL]Content Word-based Sentence Decoding and Evaluating for Open-domain Neural Response Generation
Tianyu Zhao, Tatsuya Kawahara
http://arxiv.org/abs/1905.13438v1
• [cs.CL]Crowdsourcing and Validating Event-focused Emotion Corpora for German and English
Enrica Troiano, Sebastian Padó, Roman Klinger
http://arxiv.org/abs/1905.13618v1
• [cs.CL]DiaBLa: A Corpus of Bilingual Spontaneous Written Dialogues for Machine Translation
Rachel Bawden, Sophie Rosset, Thomas Lavergne, Eric Bilinski
http://arxiv.org/abs/1905.13354v1
• [cs.CL]Do Human Rationales Improve Machine Explanations?
Julia Strout, Ye Zhang, Raymond J. Mooney
http://arxiv.org/abs/1905.13714v1
• [cs.CL]Effective writing style imitation via combinatorial paraphrasing
Tommi Gröndahl, N. Asokan
http://arxiv.org/abs/1905.13464v1
• [cs.CL]Fine-Grained Spoiler Detection from Large-Scale Review Corpora
Mengting Wan, Rishabh Misra, Ndapa Nakashole, Julian McAuley
http://arxiv.org/abs/1905.13416v1
• [cs.CL]GSN: A Graph-Structured Network for Multi-Party Dialogues
Wenpeng Hu, Zhangming Chan, Bing Liu, Dongyan Zhao, Jinwen Ma, Rui Yan
http://arxiv.org/abs/1905.13637v1
• [cs.CL]Grammar-based Neural Text-to-SQL Generation
Kevin Lin, Ben Bogin, Mark Neumann, Jonathan Berant, Matt Gardner
http://arxiv.org/abs/1905.13326v1
• [cs.CL]Improving Open Information Extraction via Iterative Rank-Aware Learning
Zhengbao Jiang, Pengcheng Yin, Graham Neubig
http://arxiv.org/abs/1905.13413v1
• [cs.CL]Information Minimization In Emergent Languages
Eugene Kharitonov, Rahma Chaabouni, Diane Bouchacourt, Marco Baroni
http://arxiv.org/abs/1905.13687v1
• [cs.CL]Investigating an Effective Character-level Embedding in Korean Sentence Classification
Won Ik Cho, Seok Min Kim, Nam Soo Kim
http://arxiv.org/abs/1905.13656v1
• [cs.CL]Leveraging Pretrained Word Embeddings for Part-of-Speech Tagging of Code Switching Data
Fahad AlGhamdi, Mona Diab
http://arxiv.org/abs/1905.13359v1
• [cs.CL]MathQA: Towards Interpretable Math Word Problem Solving with Operation-Based Formalisms
Aida Amini, Saadia Gabriel, Peter Lin, Rik Koncel-Kedziorski, Yejin Choi, Hannaneh Hajishirzi
http://arxiv.org/abs/1905.13319v1
• [cs.CL]Multi-modal Discriminative Model for Vision-and-Language Navigation
Haoshuo Huang, Vihan Jain, Harsh Mehta, Jason Baldridge, Eugene Ie
http://arxiv.org/abs/1905.13358v1
• [cs.CL]MultiQA: An Empirical Investigation of Generalization and Transfer in Reading Comprehension
Alon Talmor, Jonathan Berant
http://arxiv.org/abs/1905.13453v1
• [cs.CL]Rewarding Smatch: Transition-Based AMR Parsing with Reinforcement Learning
Tahira Naseem, Abhishek Shah, Hui Wan, Radu Florian, Salim Roukos, Miguel Ballesteros
http://arxiv.org/abs/1905.13370v1
• [cs.CL]Symbol Emergence as an Interpersonal Multimodal Categorization
Yoshinobu Hagiwara, Hiroyoshi Kobayashi, Akira Taniguchi, Tadahiro Taniguchi
http://arxiv.org/abs/1905.13443v1
• [cs.CL]Using Natural Language Processing to Develop an Automated Orthodontic Diagnostic System
Tomoyuki Kajiwara, Chihiro Tanikawa, Yuujin Shimizu, Chenhui Chu, Takashi Yamashiro, Hajime Nagahara
http://arxiv.org/abs/1905.13601v1
• [cs.CR]Privacy-Preserving Detection of IoT Devices Connected Behind a NAT in a Smart Home Setup
Yair Meidan, Vinay Sachidananda, Yuval Elovici, Asaf Shabtai
http://arxiv.org/abs/1905.13430v1
• [cs.CR]Real-Time Adversarial Attacks
Yuan Gong, Boyang Li, Christian Poellabauer, Yiyu Shi
http://arxiv.org/abs/1905.13399v1
• [cs.CR]Using Metrics Suites to Improve the Measurement of Privacy in Graphs
Isabel Wagner, Yuchen Zhao
http://arxiv.org/abs/1905.13264v1
• [cs.CV]3DPalsyNet: A Facial Palsy Grading and Motion Recognition Framework using Fully 3D Convolutional Neural Networks
Gary Storey, Richard Jiang, Shelagh Keogh, Ahmed Bouridane, Chang-Tsun Li
http://arxiv.org/abs/1905.13607v1
• [cs.CV]A Riemanian Approach to Blob Detection in Manifold-Valued Images
Aleksei Shestov, Mikhail Kumskov
http://arxiv.org/abs/1905.13653v1
• [cs.CV]A survey of advances in vision-based vehicle re-identification
Sultan Daud Khan, Habib Ullah
http://arxiv.org/abs/1905.13258v1
• [cs.CV]All-In-One Underwater Image Enhancement using Domain-Adversarial Learning
Pritish Uplavikar, Zhenyu Wu, Zhangyang Wang
http://arxiv.org/abs/1905.13342v1
• [cs.CV]Counting and Segmenting Sorghum Heads
Min-hwan Oh, Peder Olsen, Karthikeyan Natesan Ramamurthy
http://arxiv.org/abs/1905.13291v1
• [cs.CV]Deep Dual Relation Modeling for Egocentric Interaction Recognition
Haoxin Li, Yijun Cai, Wei-Shi Zheng
http://arxiv.org/abs/1905.13586v1
• [cs.CV]Deep interpretable architecture for plant diseases classification
Mohammed Brahimi, Said Mahmoudi, Kamel Boukhalfa, Abdelouhab Moussaoui
http://arxiv.org/abs/1905.13523v1
• [cs.CV]Design Light-weight 3D Convolutional Networks for Video Recognition Temporal Residual, Fully Separable Block, and Fast Algorithm
Haonan Wang, Jun Lin, Zhongfeng Wang
http://arxiv.org/abs/1905.13388v1
• [cs.CV]Joint Representation of Multiple Geometric Priors via a Shape Decomposition Model for Single Monocular 3D Pose Estimation
Mengxi Jiang, Zhuliang Yu, Cuihua Li, Yunqi Lei
http://arxiv.org/abs/1905.13466v1
• [cs.CV]Large Scale Incremental Learning
Yue Wu, Yinpeng Chen, Lijuan Wang, Yuancheng Ye, Zicheng Liu, Yandong Guo, Yun Fu
http://arxiv.org/abs/1905.13260v1
• [cs.CV]Multi-Precision Quantized Neural Networks via Encoding Decomposition of -1 and +1
Qigong Sun, Fanhua Shang, Kang Yang, Xiufang Li, Yan Ren, Licheng Jiao
http://arxiv.org/abs/1905.13389v1
• [cs.CV]Multimodal Joint Emotion and Game Context Recognition in League of Legends Livestreams
Charles Ringer, James Alfred Walker, Mihalis A. Nicolaou
http://arxiv.org/abs/1905.13694v1
• [cs.CV]Multitask Text-to-Visual Embedding with Titles and Clickthrough Data
Pranav Aggarwal, Zhe Lin, Baldo Faieta, Saeid Motiian
http://arxiv.org/abs/1905.13339v1
• [cs.CV]Point Clouds Learning with Attention-based Graph Convolution Networks
Zhuyang Xie, Junzhou Chen, Bo Peng
http://arxiv.org/abs/1905.13445v1
• [cs.CV]Rethinking Table Parsing using Graph Neural Networks
Shah Rukh Qasim, Hassan Mahmood, Faisal Shafait
http://arxiv.org/abs/1905.13391v1
• [cs.CV]Scene Text Visual Question Answering
Ali Furkan Biten, Ruben Tito, Andres Mafla, Lluis Gomez, Marçal Rusiñol, Ernest Valveny, C. V. Jawahar, Dimosthenis Karatzas
http://arxiv.org/abs/1905.13648v1
• [cs.CV]Supervised Online Hashing via Similarity Distribution Learning
Mingbao Lin, Rongrong Ji, Shen Chen, Feng Zheng, Xiaoshuai Sun, Baochang Zhang, Liujuan Cao, Guodong Guo, Feiyue Huang
http://arxiv.org/abs/1905.13382v1
• [cs.CV]TACNet: Transition-Aware Context Network for Spatio-Temporal Action Detection
Lin Song, Shiwei Zhang, Gang Yu, Hongbin Sun
http://arxiv.org/abs/1905.13417v1
• [cs.DB]Efficient Multiway Hash Join on Reconfigurable Hardware
Kunle Olukotun, Raghu Prabhakar, Rekha Singhal, Jeffrey D. Ullman, Yaqi Zhang
http://arxiv.org/abs/1905.13376v1
• [cs.DB]ParPaRaw: Massively Parallel Parsing of Delimiter-Separated Raw Data
Elias Stehle, Hans-Arno Jacobsen
http://arxiv.org/abs/1905.13415v1
• [cs.DC]From Global Choreographies to Provably Correct and Efficient Distributed Implementations
Mohamad Jaber, Yliès Falcone, Paul Attie, Al-Abbass Khalil, Rayan Hallal
http://arxiv.org/abs/1905.13529v1
• [cs.DC]INFaaS: Managed & Model-less Inference Serving
Francisco Romero, Qian Li, Neeraja J. Yadwadkar, Christos Kozyrakis
http://arxiv.org/abs/1905.13348v1
• [cs.DC]Isolation-Aware Timing Analysis and Design Space Exploration for Predictable and Composable Many-Core Systems
Behnaz Pourmohseni, Fedor Smirnov, Stefan Wildermann, Jürgen Teich
http://arxiv.org/abs/1905.13503v1
• [cs.DC]The Bloom Clock
Lum Ramabaja
http://arxiv.org/abs/1905.13064v2
• [cs.DC]Tracking in Order to Recover: Recoverable Lock-Free Data Structures
Hagit Attiya, Ohad Ben-Baruch, Panagiota Fatourou, Danny Hendler, Eleftherios Kosmas
http://arxiv.org/abs/1905.13600v1
• [cs.DS]Principal Fairness: \ Removing Bias via Projections
Aris Anagnostopoulos, Luca Becchetti, Matteo Böhm, Adriano Fazzone, Stefano Leonardi, Cristina Menghini, Chris Schwiegelshohn
http://arxiv.org/abs/1905.13651v1
• [cs.IR]Threshold-Based Retrieval and Textual Entailment Detection on Legal Bar Exam Questions
Sabine Wehnert, Sayed Anisul Hoque, Wolfram Fenske, Gunter Saake
http://arxiv.org/abs/1905.13350v1
• [cs.IT]Age of Information in G/G/1/1 Systems: Age Expressions, Bounds, Special Cases, and Optimization
Alkan Soysal, Sennur Ulukus
http://arxiv.org/abs/1905.13743v1
• [cs.IT]Collaborative Decoding of Polynomial Codes for Distributed Computation
Adarsh M. Subramaniam, Anoosheh Heiderzadeh, Krishna R. Narayanan
http://arxiv.org/abs/1905.13685v1
• [cs.IT]Deep Learning for Distributed Optimization: Applications to Wireless Resource Management
Hoon Lee, Sang Hyun Lee, Tony Q. S. Quek
http://arxiv.org/abs/1905.13378v1
• [cs.IT]Log-logarithmic Time Pruned Polar Coding
Hsin-Po Wang, Iwan Duursma
http://arxiv.org/abs/1905.13340v1
• [cs.IT]Neural Entropic Estimation: A faster path to mutual information estimation
Chung Chan, Ali Al-Bashabsheh, Hing Pang Huang, Michael Lim, Da Sun Handason Tam, Chao Zhao
http://arxiv.org/abs/1905.12957v2
• [cs.IT]Performance Analysis of Clustered LoRa Networks
Zhijin Qin, Yuanwei Liu, Geoffrey Ye Li, Julie A. McCann
http://arxiv.org/abs/1905.13510v1
• [cs.IT]When Full Duplex Wireless Meets Non-Orthogonal Multiple Access: Opportunities and Challenges
Xianhao Chen, Gang Liu, Zheng Ma, Xi Zhang, Pingzhi Fan, Shanzhi Chen, F. Richard Yu
http://arxiv.org/abs/1905.13605v1
• [cs.LG]A novel hybrid model based on multi-objective Harris hawks optimization algorithm for daily PM2.5 and PM10 forecasting
Pei Du, Jianzhou Wang, Yan Hao, Tong Niu, Wendong Yang
http://arxiv.org/abs/1905.13550v1
• [cs.LG]Are Labels Required for Improving Adversarial Robustness?
Jonathan Uesato, Jean-Baptiste Alayrac, Po-Sen Huang, Robert Stanforth, Alhussein Fawzi, Pushmeet Kohli*
http://arxiv.org/abs/1905.13725v1
• [cs.LG]Attentional Policies for Cross-Context Multi-Agent Reinforcement Learning
Matthew A. Wright, Roberto Horowitz
http://arxiv.org/abs/1905.13428v1
• [cs.LG]Augmenting Transfer Learning with Semantic Reasoning
Freddy Lecue, Jiaoyan Chen, Jeff Z. Pan, Huajun Chen
http://arxiv.org/abs/1905.13672v1
• [cs.LG]Bayesian Tensor Factorisation for Bottom-up Hidden Tree Markov Models
Daniele Castellana, Davide Bacciu
http://arxiv.org/abs/1905.13528v1
• [cs.LG]Bypassing Backdoor Detection Algorithms in Deep Learning
Te Juin Lester Tan, Reza Shokri
http://arxiv.org/abs/1905.13409v1
• [cs.LG]Cascaded Algorithm-Selection and Hyper-Parameter Optimization with Extreme-Region Upper Confidence Bound Bandit
Yi-Qi Hu, Yang Yu, Jun-Da Liao
http://arxiv.org/abs/1905.13703v1
• [cs.LG]Combating the Compounding-Error Problem with a Multi-step Model
Kavosh Asadi, Dipendra Misra, Seungchan Kim, Michel L. Littman
http://arxiv.org/abs/1905.13320v1
• [cs.LG]Data-driven Algorithm Selection and Parameter Tuning: Two Case studies in Optimization and Signal Processing
Jesus A. De Loera, Jamie Haddock, Anna Ma, Deanna Needell
http://arxiv.org/abs/1905.13404v1
• [cs.LG]Deep Bayesian Optimization on Attributed Graphs
Jiaxu Cui, Bo Yang, Xia Hu
http://arxiv.org/abs/1905.13403v1
• [cs.LG]DeepShift: Towards Multiplication-Less Neural Networks
Mostafa Elhoushi, Farhan Shafiq, Ye Tian, Joey Yiwei Li, Zihao Chen
http://arxiv.org/abs/1905.13298v1
• [cs.LG]Deterministic PAC-Bayesian generalization bounds for deep networks via generalizing noise-resilience
Vaishnavh Nagarajan, J. Zico Kolter
http://arxiv.org/abs/1905.13344v1
• [cs.LG]Discriminative structural graph classification
Younjoo Seo, Andreas Loukas, Nathanael Peraudin
http://arxiv.org/abs/1905.13422v1
• [cs.LG]End to end learning and optimization on graphs
Bryan Wilder, Eric Ewing, Bistra Dilkina, Milind Tambe
http://arxiv.org/abs/1905.13732v1
• [cs.LG]Exact sampling of determinantal point processes with sublinear time preprocessing
Michał Dereziński, Daniele Calandriello, Michal Valko
http://arxiv.org/abs/1905.13476v1
• [cs.LG]Explainability Techniques for Graph Convolutional Networks
Federico Baldassarre, Hossein Azizpour
http://arxiv.org/abs/1905.13686v1
• [cs.LG]Extending Deep Model Predictive Control with Safety Augmented Value Estimation from Demonstrations
Brijen Thananjeyan, Ashwin Balakrishna, Ugo Rosolia, Felix Li, Rowan McAllister, Joseph E. Gonzalez, Sergey Levine, Francesco Borrelli, Ken Goldberg
http://arxiv.org/abs/1905.13402v1
• [cs.LG]Factorized Inference in Deep Markov Models for Incomplete Multimodal Time Series
Zhi-Xuan Tan, Harold Soh, Desmond C. Ong
http://arxiv.org/abs/1905.13570v1
• [cs.LG]Fast Online “Next Best Offers” using Deep Learning
Rekha Singhal, Gautam Shroff, Mukund Kumar, Sharod Roy, Sanket Kadarkar, Rupinder virk, Siddharth Verma, Vartika Tiwari
http://arxiv.org/abs/1905.13368v1
• [cs.LG]GENO — GENeric Optimization for Classical Machine Learning
Sören Laue, Matthias Mitterreiter, Joachim Giesen
http://arxiv.org/abs/1905.13587v1
• [cs.LG]Implicit Regularization in Deep Matrix Factorization
Sanjeev Arora, Nadav Cohen, Wei Hu, Yuping Luo
http://arxiv.org/abs/1905.13655v1
• [cs.LG]Interval timing in deep reinforcement learning agents
Ben Deverett, Ryan Faulkner, Meire Fortunato, Greg Wayne, Joel Z. Leibo
http://arxiv.org/abs/1905.13469v1
• [cs.LG]L0 Regularization Based Neural Network Design and Compression
S. Asim Ahmed
http://arxiv.org/abs/1905.13652v1
• [cs.LG]Learning Sparse Networks Using Targeted Dropout
Aidan N. Gomez, Ivan Zhang, Kevin Swersky, Yarin Gal, Geoffrey E. Hinton
http://arxiv.org/abs/1905.13678v1
• [cs.LG]Leveraging Trust and Distrust in Recommender Systems via Deep Learning
Dimitrios Rafailidis
http://arxiv.org/abs/1905.13612v1
• [cs.LG]Luck Matters: Understanding Training Dynamics of Deep ReLU Networks
Yuandong Tian, Tina Jiang, Qucheng Gong, Ari Morcos
http://arxiv.org/abs/1905.13405v1
• [cs.LG]Max-MIG: an Information Theoretic Approach for Joint Learning from Crowds
Peng Cao, Yilun Xu, Yuqing Kong, Yizhou Wang
http://arxiv.org/abs/1905.13436v1
• [cs.LG]MolecularRNN: Generating realistic molecular graphs with optimized properties
Mariya Popova, Mykhailo Shvets, Junier Oliva, Olexandr Isayev
http://arxiv.org/abs/1905.13372v1
• [cs.LG]Neural Markov Logic Networks
Giuseppe Marra, Ondřej Kuželka
http://arxiv.org/abs/1905.13462v1
• [cs.LG]On Value Functions and the Agent-Environment Boundary
Nan Jiang
http://arxiv.org/abs/1905.13341v1
• [cs.LG]On the Fairness of Disentangled Representations
Francesco Locatello, Gabriele Abbati, Tom Rainforth, Stefan Bauer, Bernhard Schölkopf, Olivier Bachem
http://arxiv.org/abs/1905.13662v1
• [cs.LG]On the Necessity and Effectiveness of Learning the Prior of Variational Auto-Encoder
Haowen Xu, Wenxiao Chen, Jinlin Lai, Zhihan Li, Youjian Zhao, Dan Pei
http://arxiv.org/abs/1905.13452v1
• [cs.LG]Ordinal Regression as Structured Classification
Niall Twomey, Rafael Poyiadzi, Callum Mann, Raúl Santos-Rodríguez
http://arxiv.org/abs/1905.13658v1
• [cs.LG]PowerSGD: Practical Low-Rank Gradient Compression for Distributed Optimization
Thijs Vogels, Sai Praneeth Karimireddy, Martin Jaggi
http://arxiv.org/abs/1905.13727v1
• [cs.LG]Pre-Training Graph Neural Networks for Generic Structural Feature Extraction
Ziniu Hu, Changjun Fan, Ting Chen, Kai-Wei Chang, Yizhou Sun
http://arxiv.org/abs/1905.13728v1
• [cs.LG]Reinforcement Learning Experience Reuse with Policy Residual Representation
Wen-Ji Zhou, Yang Yu, Yingfeng Chen, Kai Guan, Tangjie Lv, Changjie Fan, Zhi-Hua Zhou
http://arxiv.org/abs/1905.13719v1
• [cs.LG]Reinforcement Learning for Mean Field Game
Nilay Tiwari, Arnob Ghosh, Vaneet Aggarwal
http://arxiv.org/abs/1905.13357v1
• [cs.LG]Reinforcement Learning for Slate-based Recommender Systems: A Tractable Decomposition and Practical Methodology
Eugene Ie, Vihan Jain, Jing Wang, Sanmit Narvekar, Ritesh Agarwal, Rui Wu, Heng-Tze Cheng, Morgane Lustman, Vince Gatto, Paul Covington, Jim McFadden, Tushar Chandra, Craig Boutilier
http://arxiv.org/abs/1905.12767v2
• [cs.LG]Residual Networks as Nonlinear Systems: Stability Analysis using Linearization
Kai Rothauge, Zhewei Yao, Zixi Hu, Michael W. Mahoney
http://arxiv.org/abs/1905.13386v1
• [cs.LG]SELFIES: a robust representation of semantically constrained graphs with an example application in chemistry
Mario Krenn, Florian Häse, AkshatKumar Nigam, Pascal Friederich, Alán Aspuru-Guzik
http://arxiv.org/abs/1905.13741v1
• [cs.LG]Scaffold-based molecular design using graph generative model
Jaechang Lim, Sang-Yeon Hwang, Seungsu Kim, Seokhyun Moon, Woo Youn Kim
http://arxiv.org/abs/1905.13639v1
• [cs.LG]Sequence Modeling of Temporal Credit Assignment for Episodic Reinforcement Learning
Yang Liu, Yunan Luo, Yuanyi Zhong, Xi Chen, Qiang Liu, Jian Peng
http://arxiv.org/abs/1905.13420v1
• [cs.LG]Subspace Networks for Few-shot Classification
Arnout Devos, Matthias Grossglauser
http://arxiv.org/abs/1905.13613v1
• [cs.LG]Sum-of-squares meets square loss: Fast rates for agnostic tensor completion
Dylan J. Foster, Andrej Risteski
http://arxiv.org/abs/1905.13283v1
• [cs.LG]Time Matters in Regularizing Deep Networks: Weight Decay and Data Augmentation Affect Early Learning Dynamics, Matter Little Near Convergence
Aditya Golatkar, Alessandro Achille, Stefano Soatto
http://arxiv.org/abs/1905.13277v1
• [cs.LG]Time Series Anomaly Detection Using Convolutional Neural Networks and Transfer Learning
Tailai Wen, Roy Keyes
http://arxiv.org/abs/1905.13628v1
• [cs.LG]Uncoupled Regression from Pairwise Comparison Data
Liyuan Xu, Junya Honda, Niu Gang, Masashi Sugiyama
http://arxiv.org/abs/1905.13659v1
• [cs.LG]Updates of Equilibrium Prop Match Gradients of Backprop Through Time in an RNN with Static Input
Maxence Ernoult, Julie Grollier, Damien Querlioz, Yoshua Bengio, Benjamin Scellier
http://arxiv.org/abs/1905.13633v1
• [cs.NE]Epsilon-Lexicase Selection for Regression
William La Cava, Lee Spector, Kourosh Danai
http://arxiv.org/abs/1905.13266v1
• [cs.NE]Improved memory in recurrent neural networks with sequential non-normal dynamics
A. Emin Orhan, Xaq Pitkow
http://arxiv.org/abs/1905.13715v1
• [cs.NI]Maximizing Clearance Rate by Penalizing Redundant Task Assignment in Mobile Crowdsensing Auctions
Maggie E. Gendy, Ahmad Al-Kabbany, Ehab F. Badran
http://arxiv.org/abs/1905.13563v1
• [cs.NI]Reducing Tail Latency via Safe and Simple Duplication
Hafiz Mohsin Bashir, Abdullah Bin Faisal, Muhammad Asim Jamshed, Peter Vondras, Ali Musa Iftikhar, Ihsan Ayyub Qazi, Fahad R. Dogar
http://arxiv.org/abs/1905.13352v1
• [cs.RO]2.5D Image based Robotic Grasping
Song Yaoxian, Cheng Chun, Fei Yuejiao, Li Xiangqing, Yu Changbin
http://arxiv.org/abs/1905.13675v1
• [cs.RO]Fast and Agile Vision-Based Flight with Teleoperation and Collision Avoidance on a Multirotor
Alex Spitzer, Xuning Yang, John Yao, Aditya Dhawale, Kshitij Goel, Mosam Dabhi, Matt Collins, Curtis Boirum, Nathan Michael
http://arxiv.org/abs/1905.13419v1
• [cs.RO]Graduated Fidelity Lattices for Motion Planning under Uncertainty
Adrián González-Sieira, Manuel Mucientes, Alberto Bugarín
http://arxiv.org/abs/1905.13531v1
• [cs.RO]Inverting Learned Dynamics Models for Aggressive Multirotor Control
Alexander Spitzer, Nathan Michael
http://arxiv.org/abs/1905.13441v1
• [cs.RO]Recent Advances in Imitation Learning from Observation
Faraz Torabi, Garrett Warnell, Peter Stone
http://arxiv.org/abs/1905.13566v1
• [cs.RO]Taming Combinatorial Challenges in Optimal Clutter Removal Tasks
Wei N. Tang, Jingjin Yu
http://arxiv.org/abs/1905.13530v1
• [cs.SD]What does a Car-ssette tape tell?
Xuenan Xu, Heinrich Dinkel, Mengyue Wu, Kai Yu
http://arxiv.org/abs/1905.13448v1
• [cs.SI]Can We Derive Explicit and Implicit Bias from Corpus?
Bo Wang, Baixiang Xue, Anthony G. Greenwald
http://arxiv.org/abs/1905.13364v1
• [cs.SI]Spotting Collusive Behaviour of Online Fraud Groups in Customer Reviews
Sarthika Dhawan, Siva Charan Reddy Gangireddy, Shiv Kumar, Tanmoy Chakraborty
http://arxiv.org/abs/1905.13649v1
• [eess.IV]Combining Noise-to-Image and Image-to-Image GANs: Brain MR Image Augmentation for Tumor Detection
Changhee Han, Leonardo Rundo, Ryosuke Araki, Yudai Nagano, Yujiro Furukawa, Giancarlo Mauri, Hideki Nakayama, Hideaki Hayashi
http://arxiv.org/abs/1905.13456v1
• [eess.IV]Improving the resolution of CryoEM single particle analysis
Zhenwei Luo
http://arxiv.org/abs/1905.13408v1
• [eess.IV]Known-plaintext attack and ciphertext-only attack for encrypted single-pixel imaging
Shuming Jiao, Yang Gao, Ting Lei, Zhenwei Xie, Xiaocong Yuan
http://arxiv.org/abs/1905.13594v1
• [eess.IV]Partial Scan Electron Microscopy with Deep Learning
Jeffrey M. Ede, Richard Beanland
http://arxiv.org/abs/1905.13667v1
• [eess.SP]A Block Diagonal Markov Model for Indoor Software-Defined Power Line Communication
Ayokunle Damilola Familua
http://arxiv.org/abs/1905.13598v1
• [math.NA]Unified Analysis of Periodization-Based Sampling Methods for Matérn Covariances
Markus Bachmayr, Ivan G. Graham, Van Kien Nguyen, Robert Scheichl
http://arxiv.org/abs/1905.13522v1
• [math.OC]ADMM for Efficient Deep Learning with Global Convergence
Junxiang Wang, Fuxun Yu, Xiang Chen, Liang Zhao
http://arxiv.org/abs/1905.13611v1
• [math.OC]Distributed Submodular Minimization via Block-Wise Updates and Communications
Francesco Farina, Andrea Testa, Giuseppe Notarstefano
http://arxiv.org/abs/1905.13682v1
• [math.PR]KMT coupling for random walk bridges
Evgeni Dimitrov, Xuan Wu
http://arxiv.org/abs/1905.13691v1
• [math.ST]Convergence of Smoothed Empirical Measures with Applications to Entropy Estimation
Ziv Goldfeld, Kristjan Greenewald, Yury Polyanskiy, Jonathan Weed
http://arxiv.org/abs/1905.13576v1
• [math.ST]State occupation probabilities in non-Markov models
Morten Overgaard
http://arxiv.org/abs/1905.13499v1
• [quant-ph]Quantum Mean Embedding of Probability Distributions
Jonas M. Kübler, Krikamol Muandet, Bernhard Schölkopf
http://arxiv.org/abs/1905.13526v1
• [stat.AP]Influences in Forecast Errors for Wind and Photovoltaic Power: A Study on Machine Learning Models
Jens Schreiber, Artjom Buschin, Bernhard Sick
http://arxiv.org/abs/1905.13668v1
• [stat.CO]Component-wise approximate Bayesian computation via Gibbs-like steps
Grégoire Clarté, Christian P. Robert, Robin Ryder, Julien Stoehr
http://arxiv.org/abs/1905.13599v1
• [stat.ME]Accumulation Bias in Meta-Analysis: The Need to Consider Time in Error Control
Judith ter Schure, Peter D. Grünwald
http://arxiv.org/abs/1905.13494v1
• [stat.ME]Targeted Estimation of L2 Distance Between Densities and its Application to Geo-spatial Data
George Shan, Mark J. van der Laan
http://arxiv.org/abs/1905.13414v1
• [stat.ML]A multi-series framework for demand forecasts in E-commerce
Rémy Garnier, Arnaud Belletoile
http://arxiv.org/abs/1905.13614v1
• [stat.ML]Clustered Gaussian Graphical Model via Symmetric Convex Clustering
Tianyi Yao, Genevera I. Allen
http://arxiv.org/abs/1905.13251v1
• [stat.ML]High Dimensional Classification via Empirical Risk Minimization: Improvements and Optimality
Xiaoyi Mai, Zhenyu Liao
http://arxiv.org/abs/1905.13742v1
• [stat.ML]Neural Likelihoods for Multi-Output Gaussian Processes
Martin Jankowiak, Jacob Gardner
http://arxiv.org/abs/1905.13697v1
• [stat.ML]PAC-Bayesian Transportation Bound
Kohei Miyaguchi
http://arxiv.org/abs/1905.13435v1
• [stat.ML]RKHSMetaMod : An R package to estimate the Hoeffding decomposition of an unknown function by solving RKHS Ridge Group Sparse optimization problem
Halaleh Kamari, Sylvie Huet, Marie-Luce Taupin
http://arxiv.org/abs/1905.13695v1
• [stat.ML]Reverse KL-Divergence Training of Prior Networks: Improved Uncertainty and Adversarial Robustness
Andrey Malinin, Mark Gales
http://arxiv.org/abs/1905.13472v1
• [stat.ML]Seeing the Wind: Visual Wind Speed Prediction with a Coupled Convolutional and Recurrent Neural Network
Jennifer L Cardona, Michael F Howland, John O Dabiri
http://arxiv.org/abs/1905.13290v1
• [stat.ML]Sparse Approximate Cross-Validation for High-Dimensional GLMs
William Stephenson, Tamara Broderick
http://arxiv.org/abs/1905.13657v1
• [stat.ML]Training Dynamics of Deep Networks using Stochastic Gradient Descent via Neural Tangent Kernel
Soufiane Hayou, Arnaud Doucet, Judith Rousseau
http://arxiv.org/abs/1905.13654v1
• [stat.ML]Unlabeled Data Improves Adversarial Robustness
Yair Carmon, Aditi Raghunathan, Ludwig Schmidt, Percy Liang, John C. Duchi
http://arxiv.org/abs/1905.13736v1