cs.AI - 人工智能

    cs.CL - 计算与语言 cs.CR - 加密与安全 cs.CV - 机器视觉与模式识别 cs.CY - 计算与社会 cs.DB - 数据库 cs.DC - 分布式、并行与集群计算 cs.DS - 数据结构与算法 cs.ET - 新兴技术 cs.HC - 人机接口 cs.IT - 信息论 cs.LG - 自动学习 cs.NE - 神经与进化计算 cs.PF - 计算性能 cs.RO - 机器人学 cs.SE - 软件工程 cs.SI - 社交网络与信息网络 econ.EM - 计量经济学 econ.GN - 一般经济学 eess.AS - 语音处理 eess.IV - 图像与视频处理 eess.SP - 信号处理 math.NA - 数值分析 math.ST - 统计理论 physics.app-ph - 应用物理 physics.comp-ph - 计算物理学 physics.data-an - 数据分析、 统计和概率 stat.AP - 应用统计 stat.ME - 统计方法论 stat.ML - (统计)机器学习

    • [cs.AI]KCAT: A Knowledge-Constraint Typing Annotation Tool
    • [cs.CL]A Comparison of Word-based and Context-based Representations for Classification Problems in Health Informatics
    • [cs.CL]A Computational Analysis of Natural Languages to Build a Sentence Structure Aware Artificial Neural Network
    • [cs.CL]Analyzing the Limitations of Cross-lingual Word Embedding Mappings
    • [cs.CL]Anti dependency distance minimization in short sequences. A graph theoretic approach
    • [cs.CL]Antonym-Synonym Classification Based on New Sub-space Embeddings
    • [cs.CL]COMET: Commonsense Transformers for Automatic Knowledge Graph Construction
    • [cs.CL]Character n-gram Embeddings to Improve RNN Language Models
    • [cs.CL]Compositional generalization through meta sequence-to-sequence learning
    • [cs.CL]E3: Entailment-driven Extracting and Editing for Conversational Machine Reading
    • [cs.CL]Enriching Neural Models with Targeted Features for Dementia Detection
    • [cs.CL]Figurative Usage Detection of Symptom Words to Improve Personal Health Mention Detection
    • [cs.CL]Improved Sentiment Detection via Label Transfer from Monolingual to Synthetic Code-Switched Text
    • [cs.CL]Keeping Notes: Conditional Natural Language Generation with a Scratchpad Mechanism
    • [cs.CL]Know What You Don’t Know: Modeling a Pragmatic Speaker that Refers to Objects of Unknown Categories
    • [cs.CL]Lattice Transformer for Speech Translation
    • [cs.CL]Neural Arabic Question Answering
    • [cs.CL]Proactive Human-Machine Conversation with Explicit Conversation Goals
    • [cs.CL]Real-Time Open-Domain Question Answering with Dense-Sparse Phrase Index
    • [cs.CL]Representation Learning for Words and Entities
    • [cs.CL]Semantic Change and Semantic Stability: Variation is Key
    • [cs.CL]Synthetic QA Corpora Generation with Roundtrip Consistency
    • [cs.CL]Transfer Learning in Biomedical Natural Language Processing: An Evaluation of BERT and ELMo on Ten Benchmarking Datasets
    • [cs.CL]UCAM Biomedical translation at WMT19: Transfer learning multi-domain ensembles
    • [cs.CL]Unsupervised Neural Single-Document Summarization of Reviews via Learning Latent Discourse Structure and its Ranking
    • [cs.CR]Deep Reinforcement Learning for Cyber Security
    • [cs.CV]$c^+$GAN: Complementary Fashion Item Recommendation
    • [cs.CV]2D Attentional Irregular Scene Text Recognizer
    • [cs.CV]Amur Tiger Re-identification in the Wild
    • [cs.CV]Assisted Excitation of Activations: A Learning Technique to Improve Object Detectors
    • [cs.CV]Contrastive Multiview Coding
    • [cs.CV]CoopSubNet: Cooperating Subnetwork for Data-Driven Regularization of Deep Networks under Limited Training Budgets
    • [cs.CV]Detecting Photoshopped Faces by Scripting Photoshop
    • [cs.CV]Egocentric affordance detection with the one-shot geometry-driven Interaction Tensor
    • [cs.CV]Eye Contact Correction using Deep Neural Networks
    • [cs.CV]Generating and Exploiting Probabilistic Monocular Depth Estimates
    • [cs.CV]Grid R-CNN Plus: Faster and Better
    • [cs.CV]HPLFlowNet: Hierarchical Permutohedral Lattice FlowNet for Scene Flow Estimation on Large-scale Point Clouds
    • [cs.CV]Illuminant Chromaticity Estimation from Interreflections
    • [cs.CV]Learning Spatio-Temporal Representation with Local and Global Diffusion
    • [cs.CV]MIMA: MAPPER-Induced Manifold Alignment for Semi-Supervised Fusion of Optical Image and Polarimetric SAR Data
    • [cs.CV]Privacy-Preserving Deep Visual Recognition: An Adversarial Learning Framework and A New Dataset
    • [cs.CV]S3: A Spectral-Spatial Structure Loss for Pan-Sharpening Networks
    • [cs.CV]Show, Match and Segment: Joint Learning of Semantic Matching and Object Co-segmentation
    • [cs.CV]Slim DensePose: Thrifty Learning from Sparse Annotations and Motion Cues
    • [cs.CV]The Herbarium Challenge 2019 Dataset
    • [cs.CV]The Replica Dataset: A Digital Replica of Indoor Spaces
    • [cs.CV]The iMaterialist Fashion Attribute Dataset
    • [cs.CV]Topology-Preserving Deep Image Segmentation
    • [cs.CV]Training Image Estimators without Image Ground-Truth
    • [cs.CV]Uncovering Dominant Social Class in Neighborhoods through Building Footprints: A Case Study of Residential Zones in Massachusetts using Computer Vision
    • [cs.CV]Understanding Human Context in 3D Scenes by Learning Spatial Affordances with Virtual Skeleton Models
    • [cs.CV]Unsupervised Image Noise Modeling with Self-Consistent GAN
    • [cs.CV]Unsupervised Monocular Depth and Ego-motion Learning with Structure and Semantics
    • [cs.CV]Visual Wake Words Dataset
    • [cs.CY]Advance gender prediction tool of first names and its use in analysing gender disparity in Computer Science in the UK, Malaysia and China
    • [cs.CY]Support Vector Machine-Based Fire Outbreak Detection System
    • [cs.CY]Tackling Climate Change with Machine Learning
    • [cs.CY]Understanding artificial intelligence ethics and safety
    • [cs.CY]Work Design and Job Rotation in Software Engineering: Results from an Industrial Study
    • [cs.DB]A Countrywide Traffic Accident Dataset
    • [cs.DB]Temporally-Biased Sampling Schemes for Online Model Management
    • [cs.DC]Blockchain Games: A Survey
    • [cs.DC]Mir-BFT: High-Throughput BFT for Blockchains
    • [cs.DC]Tensor Processing Units for Financial Monte Carlo
    • [cs.DC]The Consensus Number of a Cryptocurrency (Extended Version)
    • [cs.DS]The Communication Complexity of Optimization
    • [cs.ET]A Low-Power Domino Logic Architecture for Memristor-Based Neuromorphic Computing
    • [cs.HC]A Multiscale Visualization of Attention in the Transformer Model
    • [cs.IT]A Joint Graph Based Coding Scheme for the Unsourced Random Access Gaussian Channel
    • [cs.IT]Factorized Mutual Information Maximization
    • [cs.IT]New constructions of asymptotically optimal codebooks via character sums over a local ring
    • [cs.LG]A Computationally Efficient Method for Defending Adversarial Deep Learning Attacks
    • [cs.LG]A JIT Compiler for Neural Network Inference
    • [cs.LG]A Meta Approach to Defend Noisy Labels by the Manifold Regularizer PSDR
    • [cs.LG]Cognitive Knowledge Graph Reasoning for One-shot Relational Learning
    • [cs.LG]Competing Bandits in Matching Markets
    • [cs.LG]Contrastive Bidirectional Transformer for Temporal Representation Learning
    • [cs.LG]Efficient Evaluation-Time Uncertainty Estimation by Improved Distillation
    • [cs.LG]Factors for the Generalisation of Identity Relations by Neural Networks
    • [cs.LG]Flexible Modeling of Diversity with Strongly Log-Concave Distributions
    • [cs.LG]Generalization Guarantees for Neural Networks via Harnessing the Low-rank Structure of the Jacobian
    • [cs.LG]Goal-conditioned Imitation Learning
    • [cs.LG]Improving Prediction Accuracy in Building Performance Models Using Generative Adversarial Networks (GANs)
    • [cs.LG]Jacobian Policy Optimizations
    • [cs.LG]Kernel and Deep Regimes in Overparametrized Models
    • [cs.LG]Learning Curves for Deep Neural Networks: A Gaussian Field Theory Perspective
    • [cs.LG]Linear Distillation Learning
    • [cs.LG]Lower Bounds for Adversarially Robust PAC Learning
    • [cs.LG]Manifold Graph with Learned Prototypes for Semi-Supervised Image Classification
    • [cs.LG]Meta-Learning via Learned Loss
    • [cs.LG]Modeling and Interpreting Real-world Human Risk Decision Making with Inverse Reinforcement Learning
    • [cs.LG]Near-Optimal Glimpse Sequences for Improved Hard Attention Neural Network Training
    • [cs.LG]Neural Graph Evolution: Towards Efficient Automatic Robot Design
    • [cs.LG]Nonlinear System Identification via Tensor Completion
    • [cs.LG]Pairwise Fairness for Ranking and Regression
    • [cs.LG]Reinforcement Learning of Spatio-Temporal Point Processes
    • [cs.LG]Robust Regression for Safe Exploration in Control
    • [cs.LG]Spaceland Embedding of Sparse Stochastic Graphs
    • [cs.LG]Sub-Goal Trees — a Framework for Goal-Directed Trajectory Prediction and Optimization
    • [cs.LG]Topological Data Analysis for Arrhythmia Detection through Modular Neural Networks
    • [cs.LG]Training Neural Networks for and by Interpolation
    • [cs.LG]Utilizing Edge Features in Graph Neural Networks via Variational Information Maximization
    • [cs.LG]Variance Estimation For Online Regression via Spectrum Thresholding
    • [cs.NE]Associated Learning: Decomposing End-to-end Backpropagation based on Auto-encoders and Target Propagation
    • [cs.NE]Decentralised Multi-Demic Evolutionary Approach to the Dynamic Multi-Agent Travelling Salesman Problem
    • [cs.NE]MOPED: Efficient priors for scalable variational inference in Bayesian deep neural networks
    • [cs.NE]Meta-heuristic for non-homogeneous peak density spaces and implementation on 2 real-world parameter learning/tuning applications
    • [cs.PF]Optimizing Redundancy Levels in Master-Worker Compute Clusters for Straggler Mitigation
    • [cs.RO]Deep Reinforcement Learning for Industrial Insertion Tasks with Visual Inputs and Natural Rewards
    • [cs.SE]Sionnx: Automatic Unit Test Generator for ONNX Conformance
    • [cs.SI]Identifying Illicit Accounts in Large Scale E-payment Networks — A Graph Representation Learning Approach
    • [econ.EM]Sparse Approximate Factor Estimation for High-Dimensional Covariance Matrices
    • [econ.GN]ProPublica’s COMPAS Data Revisited
    • [eess.AS]Telephonetic: Making Neural Language Models Robust to ASR and Semantic Noise
    • [eess.IV]Deep Variational Networks with Exponential Weighting for Learning Computed Tomography
    • [eess.IV]Detection and Correction of Cardiac MR Motion Artefacts during Reconstruction from K-space
    • [eess.IV]GANPOP: Generative Adversarial Network Prediction of Optical Properties from Single Snapshot Wide-field Images
    • [eess.IV]Image-Adaptive GAN based Reconstruction
    • [eess.IV]Mask2Lesion: Mask-Constrained Adversarial Skin Lesion Image Synthesis
    • [eess.IV]Robust and interpretable blind image denoising via bias-free convolutional neural networks
    • [eess.SP]Deep Unfolding for Communications Systems: A Survey and Some New Directions
    • [math.NA]Deep Network Approximation Characterized by Number of Neurons
    • [math.ST]Efficiency of maximum likelihood estimation for a multinomial distribution with known probability sums
    • [math.ST]Hypotheses testing and posterior concentration rates for semi-Markov processes
    • [math.ST]Matrix Mittag—Leffler distributions and modeling heavy-tailed risks
    • [physics.app-ph]An image-driven machine learning approach to kinetic modeling of a discontinuous precipitation reaction
    • [physics.comp-ph]Modeling the Dynamics of PDE Systems with Physics-Constrained Deep Auto-Regressive Networks
    • [physics.data-an]Iterative subtraction method for Feature Ranking
    • [stat.AP]Dynamic Time Scan Forecasting
    • [stat.AP]Identifying and Predicting Parkinson’s Disease Subtypes through Trajectory Clustering via Bipartite Networks
    • [stat.AP]Interpretable ICD Code Embeddings with Self- and Mutual-Attention Mechanisms
    • [stat.AP]Machine Learning Based Analysis and Quantification of Potential Power Gain from Passive Device Installation
    • [stat.AP]Use of Emergency Departments by Frail Elderly Patients: Temporal Patterns and Case Complexity
    • [stat.ME]Direct Sampling of Bayesian Thin-Plate Splines for Spatial Smoothing
    • [stat.ME]Distributed High-dimensional Regression Under a Quantile Loss Function
    • [stat.ME]Efficient calibration for high-dimensional computer model output using basis methods
    • [stat.ME]Individualized Group Learning
    • [stat.ME]Permutation-based uncertainty quantification about a mixing distribution
    • [stat.ML]Copulas as High-Dimensional Generative Models: Vine Copula Autoencoders
    • [stat.ML]Optimal low rank tensor recovery
    • [stat.ML]Overcoming Mean-Field Approximations in Recurrent Gaussian Process Models
    • [stat.ML]Random Tessellation Forests
    • [stat.ML]Reweighted Expectation Maximization
    • [stat.ML]Selective prediction-set models with coverage guarantees
    • [stat.ML]Tensor Canonical Correlation Analysis

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    • [cs.AI]KCAT: A Knowledge-Constraint Typing Annotation Tool
    Sheng Lin, Luye Zheng, Bo Chen, Siliang Tang, Yueting Zhuang, Fei Wu, Zhigang Chen, Guoping Hu, Xiang Ren
    http://arxiv.org/abs/1906.05670v1

    • [cs.CL]A Comparison of Word-based and Context-based Representations for Classification Problems in Health Informatics
    Aditya Joshi, Sarvnaz Karimi, Ross Sparks, Cecile Paris, C Raina MacIntyre
    http://arxiv.org/abs/1906.05468v1

    • [cs.CL]A Computational Analysis of Natural Languages to Build a Sentence Structure Aware Artificial Neural Network
    Alberto Calderone
    http://arxiv.org/abs/1906.05491v1

    • [cs.CL]Analyzing the Limitations of Cross-lingual Word Embedding Mappings
    Aitor Ormazabal, Mikel Artetxe, Gorka Labaka, Aitor Soroa, Eneko Agirre
    http://arxiv.org/abs/1906.05407v1

    • [cs.CL]Anti dependency distance minimization in short sequences. A graph theoretic approach
    Ramon Ferrer-i-Cancho, Carlos Gómez-Rodríguez
    http://arxiv.org/abs/1906.05765v1

    • [cs.CL]Antonym-Synonym Classification Based on New Sub-space Embeddings
    Muhammad Asif Ali, Yifang Sun, Xiaoling Zhou, Wei Wang, Xiang Zhao
    http://arxiv.org/abs/1906.05612v1

    • [cs.CL]COMET: Commonsense Transformers for Automatic Knowledge Graph Construction
    Antoine Bosselut, Hannah Rashkin, Maarten Sap, Chaitanya Malaviya, Asli Celikyilmaz, Yejin Choi
    http://arxiv.org/abs/1906.05317v1

    • [cs.CL]Character n-gram Embeddings to Improve RNN Language Models
    Sho Takase, Jun Suzuki, Masaaki Nagata
    http://arxiv.org/abs/1906.05506v1

    • [cs.CL]Compositional generalization through meta sequence-to-sequence learning
    Brenden M. Lake
    http://arxiv.org/abs/1906.05381v1

    • [cs.CL]E3: Entailment-driven Extracting and Editing for Conversational Machine Reading
    Victor Zhong, Luke Zettlemoyer
    http://arxiv.org/abs/1906.05373v1

    • [cs.CL]Enriching Neural Models with Targeted Features for Dementia Detection
    Flavio Di Palo, Natalie Parde
    http://arxiv.org/abs/1906.05483v1

    • [cs.CL]Figurative Usage Detection of Symptom Words to Improve Personal Health Mention Detection
    Adith Iyer, Aditya Joshi, Sarvnaz Karimi, Ross Sparks, Cecile Paris
    http://arxiv.org/abs/1906.05466v1

    • [cs.CL]Improved Sentiment Detection via Label Transfer from Monolingual to Synthetic Code-Switched Text
    Bidisha Samanta, Niloy Ganguly, Soumen Chakrabarti
    http://arxiv.org/abs/1906.05725v1

    • [cs.CL]Keeping Notes: Conditional Natural Language Generation with a Scratchpad Mechanism
    Ryan Y. Benmalek, Madian Khabsa, Suma Desu, Claire Cardie, Michele Banko
    http://arxiv.org/abs/1906.05275v2

    • [cs.CL]Know What You Don’t Know: Modeling a Pragmatic Speaker that Refers to Objects of Unknown Categories
    Sina Zarrieß, David Schlangen
    http://arxiv.org/abs/1906.05518v1

    • [cs.CL]Lattice Transformer for Speech Translation
    Pei Zhang, Boxing Chen, Niyu Ge, Kai Fan
    http://arxiv.org/abs/1906.05551v1

    • [cs.CL]Neural Arabic Question Answering
    Hussein Mozannar, Karl El Hajal, Elie Maamary, Hazem Hajj
    http://arxiv.org/abs/1906.05394v1

    • [cs.CL]Proactive Human-Machine Conversation with Explicit Conversation Goals
    Wenquan Wu, Zhen Guo, Xiangyang Zhou, Hua Wu, Xiyuan Zhang, Rongzhong Lian, Haifeng Wang
    http://arxiv.org/abs/1906.05572v1

    • [cs.CL]Real-Time Open-Domain Question Answering with Dense-Sparse Phrase Index
    Minjoon Seo, Jinhyuk Lee, Tom Kwiatkowski, Ankur P. Parikh, Ali Farhadi, Hannaneh Hajishirzi
    http://arxiv.org/abs/1906.05807v1

    • [cs.CL]Representation Learning for Words and Entities
    Pushpendre Rastogi
    http://arxiv.org/abs/1906.05651v1

    • [cs.CL]Semantic Change and Semantic Stability: Variation is Key
    Claire Bowern
    http://arxiv.org/abs/1906.05760v1

    • [cs.CL]Synthetic QA Corpora Generation with Roundtrip Consistency
    Chris Alberti, Daniel Andor, Emily Pitler, Jacob Devlin, Michael Collins
    http://arxiv.org/abs/1906.05416v1

    • [cs.CL]Transfer Learning in Biomedical Natural Language Processing: An Evaluation of BERT and ELMo on Ten Benchmarking Datasets
    Yifan Peng, Shankai Yan, Zhiyong Lu
    http://arxiv.org/abs/1906.05474v1

    • [cs.CL]UCAM Biomedical translation at WMT19: Transfer learning multi-domain ensembles
    Danielle Saunders, Felix Stahlberg, Bill Byrne
    http://arxiv.org/abs/1906.05786v1

    • [cs.CL]Unsupervised Neural Single-Document Summarization of Reviews via Learning Latent Discourse Structure and its Ranking
    Masaru Isonuma, Junichiro Mori, Ichiro Sakata
    http://arxiv.org/abs/1906.05691v1

    • [cs.CR]Deep Reinforcement Learning for Cyber Security
    Thanh Thi Nguyen, Vijay Janapa Reddi
    http://arxiv.org/abs/1906.05799v1

    • [cs.CV]$c^+$GAN: Complementary Fashion Item Recommendation
    Sudhir Kumar, Mithun Das Gupta
    http://arxiv.org/abs/1906.05596v1

    • [cs.CV]2D Attentional Irregular Scene Text Recognizer
    Pengyuan Lyu, Zhicheng Yang, Xinhang Leng, Xiaojun Wu, Ruiyu Li, Xiaoyong Shen
    http://arxiv.org/abs/1906.05708v1

    • [cs.CV]Amur Tiger Re-identification in the Wild
    Shuyuan Li, Jianguo Li, Weiyao Lin, Hanlin Tang
    http://arxiv.org/abs/1906.05586v1

    • [cs.CV]Assisted Excitation of Activations: A Learning Technique to Improve Object Detectors
    Mohammad Mahdi Derakhshani, Saeed Masoudnia, Amir Hossein Shaker, Omid Mersa, Mohammad Amin Sadeghi, Mohammad Rastegari, Babak N. Araabi
    http://arxiv.org/abs/1906.05388v1

    • [cs.CV]Contrastive Multiview Coding
    Yonglong Tian, Dilip Krishnan, Phillip Isola
    http://arxiv.org/abs/1906.05849v1

    • [cs.CV]CoopSubNet: Cooperating Subnetwork for Data-Driven Regularization of Deep Networks under Limited Training Budgets
    Riddhish Bhalodia, Shireen Elhabian, Ladislav Kavan, Ross Whitaker
    http://arxiv.org/abs/1906.05441v1

    • [cs.CV]Detecting Photoshopped Faces by Scripting Photoshop
    Sheng-Yu Wang, Oliver Wang, Andrew Owens, Richard Zhang, Alexei A. Efros
    http://arxiv.org/abs/1906.05856v1

    • [cs.CV]Egocentric affordance detection with the one-shot geometry-driven Interaction Tensor
    Eduardo Ruiz, Walterio Mayol-Cuevas
    http://arxiv.org/abs/1906.05794v1

    • [cs.CV]Eye Contact Correction using Deep Neural Networks
    Leo F. Isikdogan, Timo Gerasimow, Gilad Michael
    http://arxiv.org/abs/1906.05378v1

    • [cs.CV]Generating and Exploiting Probabilistic Monocular Depth Estimates
    Zhihao Xia, Patrick Sullivan, Ayan Chakrabarti
    http://arxiv.org/abs/1906.05739v1

    • [cs.CV]Grid R-CNN Plus: Faster and Better
    Xin Lu, Buyu Li, Yuxin Yue, Quanquan Li, Junjie Yan
    http://arxiv.org/abs/1906.05688v1

    • [cs.CV]HPLFlowNet: Hierarchical Permutohedral Lattice FlowNet for Scene Flow Estimation on Large-scale Point Clouds
    Xiuye Gu, Yijie Wang, Chongruo wu, Yong-Jae lee, Panqu Wang
    http://arxiv.org/abs/1906.05332v1

    • [cs.CV]Illuminant Chromaticity Estimation from Interreflections
    Eytan Lifshitz, Dani Lischinski
    http://arxiv.org/abs/1906.05526v1

    • [cs.CV]Learning Spatio-Temporal Representation with Local and Global Diffusion
    Zhaofan Qiu, Ting Yao, Chong-Wah Ngo, Xinmei Tian, Tao Mei
    http://arxiv.org/abs/1906.05571v1

    • [cs.CV]MIMA: MAPPER-Induced Manifold Alignment for Semi-Supervised Fusion of Optical Image and Polarimetric SAR Data
    Jingliang Hu, Danfeng Hong, Xiao Xiang Zhu
    http://arxiv.org/abs/1906.05512v1

    • [cs.CV]Privacy-Preserving Deep Visual Recognition: An Adversarial Learning Framework and A New Dataset
    Haotao Wang, Zhenyu Wu, Zhangyang Wang, Zhaowen Wang, Hailin Jin
    http://arxiv.org/abs/1906.05675v1

    • [cs.CV]S3: A Spectral-Spatial Structure Loss for Pan-Sharpening Networks
    Jae-Seok Choi, Yongwoo Kim, Munchurl Kim
    http://arxiv.org/abs/1906.05480v1

    • [cs.CV]Show, Match and Segment: Joint Learning of Semantic Matching and Object Co-segmentation
    Yun-Chun Chen, Yen-Yu Lin, Ming-Hsuan Yang, Jia-Bin Huang
    http://arxiv.org/abs/1906.05857v1

    • [cs.CV]Slim DensePose: Thrifty Learning from Sparse Annotations and Motion Cues
    Natalia Neverova, James Thewlis, Rıza Alp Güler, Iasonas Kokkinos, Andrea Vedaldi
    http://arxiv.org/abs/1906.05706v1

    • [cs.CV]The Herbarium Challenge 2019 Dataset
    Kiat Chuan Tan, Yulong Liu, Barbara Ambrose, Melissa Tulig, Serge Belongie
    http://arxiv.org/abs/1906.05372v1

    • [cs.CV]The Replica Dataset: A Digital Replica of Indoor Spaces
    Julian Straub, Thomas Whelan, Lingni Ma, Yufan Chen, Erik Wijmans, Simon Green, Jakob J. Engel, Raul Mur-Artal, Carl Ren, Shobhit Verma, Anton Clarkson, Mingfei Yan, Brian Budge, Yajie Yan, Xiaqing Pan, June Yon, Yuyang Zou, Kimberly Leon, Nigel Carter, Jesus Briales, Tyler Gillingham, Elias Mueggler, Luis Pesqueira, Manolis Savva, Dhruv Batra, Hauke M. Strasdat, Renzo De Nardi, Michael Goesele, Steven Lovegrove, Richard Newcombe
    http://arxiv.org/abs/1906.05797v1

    • [cs.CV]The iMaterialist Fashion Attribute Dataset
    Sheng Guo, Weilin Huang, Xiao Zhang, Prasanna Srikhanta, Yin Cui, Yuan Li, Serge Belongie, Hartwig Adam, Matthew Scott
    http://arxiv.org/abs/1906.05750v1

    • [cs.CV]Topology-Preserving Deep Image Segmentation
    Xiaoling Hu, Li Fuxin, Dimitris Samaras, Chao Chen
    http://arxiv.org/abs/1906.05404v1

    • [cs.CV]Training Image Estimators without Image Ground-Truth
    Zhihao Xia, Ayan Chakrabarti
    http://arxiv.org/abs/1906.05775v1

    • [cs.CV]Uncovering Dominant Social Class in Neighborhoods through Building Footprints: A Case Study of Residential Zones in Massachusetts using Computer Vision
    Qianhui Liang, Zhoutong Wang
    http://arxiv.org/abs/1906.05352v1

    • [cs.CV]Understanding Human Context in 3D Scenes by Learning Spatial Affordances with Virtual Skeleton Models
    Lasitha Piyathilaka, Sarath Kodagoda
    http://arxiv.org/abs/1906.05498v1

    • [cs.CV]Unsupervised Image Noise Modeling with Self-Consistent GAN
    Hanshu Yan, Vincent Tan, Wenhan Yang, Jiashi Feng
    http://arxiv.org/abs/1906.05762v1

    • [cs.CV]Unsupervised Monocular Depth and Ego-motion Learning with Structure and Semantics
    Vincent Casser, Soeren Pirk, Reza Mahjourian, Anelia Angelova
    http://arxiv.org/abs/1906.05717v1

    • [cs.CV]Visual Wake Words Dataset
    Aakanksha Chowdhery, Pete Warden, Jonathon Shlens, Andrew Howard, Rocky Rhodes
    http://arxiv.org/abs/1906.05721v1

    • [cs.CY]Advance gender prediction tool of first names and its use in analysing gender disparity in Computer Science in the UK, Malaysia and China
    Hua Zhao, Fairouz Kamareddine
    http://arxiv.org/abs/1906.05769v1

    • [cs.CY]Support Vector Machine-Based Fire Outbreak Detection System
    Uduak Umoh, Edward Udo, Nyoho Emmanuel
    http://arxiv.org/abs/1906.05655v1

    • [cs.CY]Tackling Climate Change with Machine Learning
    David Rolnick, Priya L. Donti, Lynn H. Kaack, Kelly Kochanski, Alexandre Lacoste, Kris Sankaran, Andrew Slavin Ross, Nikola Milojevic-Dupont, Natasha Jaques, Anna Waldman-Brown, Alexandra Luccioni, Tegan Maharaj, Evan D. Sherwin, S. Karthik Mukkavilli, Konrad P. Kording, Carla Gomes, Andrew Y. Ng, Demis Hassabis, John C. Platt, Felix Creutzig, Jennifer Chayes, Yoshua Bengio
    http://arxiv.org/abs/1906.05433v1

    • [cs.CY]Understanding artificial intelligence ethics and safety
    David Leslie
    http://arxiv.org/abs/1906.05684v1

    • [cs.CY]Work Design and Job Rotation in Software Engineering: Results from an Industrial Study
    Ronnie Santos, Marian Teresa Baldassarre, Fabio Queda Bueno da Silva, Cleyton Magalhaes, Luiz Fernando Capretz, Jorge Correia-Neto
    http://arxiv.org/abs/1906.05365v1

    • [cs.DB]A Countrywide Traffic Accident Dataset
    Sobhan Moosavi, Mohammad Hossein Samavatian, Srinivasan Parthasarathy, Rajiv Ramnath
    http://arxiv.org/abs/1906.05409v1

    • [cs.DB]Temporally-Biased Sampling Schemes for Online Model Management
    Brian Hentschel, Peter J. Haas, Yuanyuan Tian
    http://arxiv.org/abs/1906.05677v1

    • [cs.DC]Blockchain Games: A Survey
    Tian Min, Hanyi Wang, Yaoze Guo, Wei Cai
    http://arxiv.org/abs/1906.05558v1

    • [cs.DC]Mir-BFT: High-Throughput BFT for Blockchains
    Chrysoula Stathakopoulou, Tudor David, Marko Vukolić
    http://arxiv.org/abs/1906.05552v1

    • [cs.DC]Tensor Processing Units for Financial Monte Carlo
    Francois Belletti, Davis King, Kun Yang, Roland Nelet, Yusef Shafi, Yi-Fan Chen, John Anderson
    http://arxiv.org/abs/1906.02818v2

    • [cs.DC]The Consensus Number of a Cryptocurrency (Extended Version)
    Rachid Guerraoui, Petr Kuznetsov, Matteo Monti, Matej Pavlovic, Dragos-Adrian Seredinschi
    http://arxiv.org/abs/1906.05574v1

    • [cs.DS]The Communication Complexity of Optimization
    Santosh S. Vempala, Ruosong Wang, David P. Woodruff
    http://arxiv.org/abs/1906.05832v1

    • [cs.ET]A Low-Power Domino Logic Architecture for Memristor-Based Neuromorphic Computing
    Cory Merkel, Animesh Nikam
    http://arxiv.org/abs/1906.05781v1

    • [cs.HC]A Multiscale Visualization of Attention in the Transformer Model
    Jesse Vig
    http://arxiv.org/abs/1906.05714v1

    • [cs.IT]A Joint Graph Based Coding Scheme for the Unsourced Random Access Gaussian Channel
    Asit Pradhan, Vamsi Amalladinne, Avinash Vem, Krishna R. Narayanan, Jean-Francois Chamberland
    http://arxiv.org/abs/1906.05410v1

    • [cs.IT]Factorized Mutual Information Maximization
    Thomas Merkh, Guido Montúfar
    http://arxiv.org/abs/1906.05460v1

    • [cs.IT]New constructions of asymptotically optimal codebooks via character sums over a local ring
    Liqin Qian, Xiwang Cao, Wei Lu, Xia Wu
    http://arxiv.org/abs/1906.05523v1

    • [cs.LG]A Computationally Efficient Method for Defending Adversarial Deep Learning Attacks
    Rajeev Sahay, Rehana Mahfuz, Aly El Gamal
    http://arxiv.org/abs/1906.05599v1

    • [cs.LG]A JIT Compiler for Neural Network Inference
    Felix Thielke, Arne Hasselbring
    http://arxiv.org/abs/1906.05737v1

    • [cs.LG]A Meta Approach to Defend Noisy Labels by the Manifold Regularizer PSDR
    Pengfei Chen, Benben Liao, Guangyong Chen, Shengyu Zhang
    http://arxiv.org/abs/1906.05509v1

    • [cs.LG]Cognitive Knowledge Graph Reasoning for One-shot Relational Learning
    Zhengxiao Du, Chang Zhou, Ming Ding, Hongxia Yang, Jie Tang
    http://arxiv.org/abs/1906.05489v1

    • [cs.LG]Competing Bandits in Matching Markets
    Lydia T. Liu, Horia Mania, Michael I. Jordan
    http://arxiv.org/abs/1906.05363v1

    • [cs.LG]Contrastive Bidirectional Transformer for Temporal Representation Learning
    Chen Sun, Fabien Baradel, Kevin Murphy, Cordelia Schmid
    http://arxiv.org/abs/1906.05743v1

    • [cs.LG]Efficient Evaluation-Time Uncertainty Estimation by Improved Distillation
    Erik Englesson, Hossein Azizpour
    http://arxiv.org/abs/1906.05419v1

    • [cs.LG]Factors for the Generalisation of Identity Relations by Neural Networks
    Radha Kopparti, Tillman Weyde
    http://arxiv.org/abs/1906.05449v1

    • [cs.LG]Flexible Modeling of Diversity with Strongly Log-Concave Distributions
    Joshua Robinson, Suvrit Sra, Stefanie Jegelka
    http://arxiv.org/abs/1906.05413v1

    • [cs.LG]Generalization Guarantees for Neural Networks via Harnessing the Low-rank Structure of the Jacobian
    Samet Oymak, Zalan Fabian, Mingchen Li, Mahdi Soltanolkotabi
    http://arxiv.org/abs/1906.05392v1

    • [cs.LG]Goal-conditioned Imitation Learning
    Yiming Ding, Carlos Florensa, Mariano Phielipp, Pieter Abbeel
    http://arxiv.org/abs/1906.05838v1

    • [cs.LG]Improving Prediction Accuracy in Building Performance Models Using Generative Adversarial Networks (GANs)
    Chanachok Chokwitthaya, Edward Cillier, Yimin Zhu, Supratik Mukhopadhyay
    http://arxiv.org/abs/1906.05767v1

    • [cs.LG]Jacobian Policy Optimizations
    Arip Asadulaev, Gideon Stein, Igor Kuznetsov, Andrey Filchenkov
    http://arxiv.org/abs/1906.05437v1

    • [cs.LG]Kernel and Deep Regimes in Overparametrized Models
    Blake Woodworth, Suriya Gunasekar, Jason Lee, Daniel Soudry, Nathan Srebro
    http://arxiv.org/abs/1906.05827v1

    • [cs.LG]Learning Curves for Deep Neural Networks: A Gaussian Field Theory Perspective
    Omry Cohen, Or Malka, Zohar Ringel
    http://arxiv.org/abs/1906.05301v1

    • [cs.LG]Linear Distillation Learning
    Arip Asadulaev, Igor Kuznetsov, Andrey Filchenkov
    http://arxiv.org/abs/1906.05431v1

    • [cs.LG]Lower Bounds for Adversarially Robust PAC Learning
    Dimitrios I. Diochnos, Saeed Mahloujifar, Mohammad Mahmoody
    http://arxiv.org/abs/1906.05815v1

    • [cs.LG]Manifold Graph with Learned Prototypes for Semi-Supervised Image Classification
    Chia-Wen Kuo, Chih-Yao Ma, Jia-Bin Huang, Zsolt Kira
    http://arxiv.org/abs/1906.05202v2

    • [cs.LG]Meta-Learning via Learned Loss
    Yevgen Chebotar, Artem Molchanov, Sarah Bechtle, Ludovic Righetti, Franziska Meier, Gaurav Sukhatme
    http://arxiv.org/abs/1906.05374v1

    • [cs.LG]Modeling and Interpreting Real-world Human Risk Decision Making with Inverse Reinforcement Learning
    Quanying Liu, Haiyan Wu, Anqi Liu
    http://arxiv.org/abs/1906.05803v1

    • [cs.LG]Near-Optimal Glimpse Sequences for Improved Hard Attention Neural Network Training
    William Harvey, Michael Teng, Frank Wood
    http://arxiv.org/abs/1906.05462v1

    • [cs.LG]Neural Graph Evolution: Towards Efficient Automatic Robot Design
    Tingwu Wang, Yuhao Zhou, Sanja Fidler, Jimmy Ba
    http://arxiv.org/abs/1906.05370v1

    • [cs.LG]Nonlinear System Identification via Tensor Completion
    Nikolaos Kargas, Nicholas D. Sidiropoulos
    http://arxiv.org/abs/1906.05746v1

    • [cs.LG]Pairwise Fairness for Ranking and Regression
    Harikrishna Narasimhan, Andrew Cotter, Maya Gupta, Serena Wang
    http://arxiv.org/abs/1906.05330v1

    • [cs.LG]Reinforcement Learning of Spatio-Temporal Point Processes
    Shixiang Zhu, Shuang Li, Yao Xie
    http://arxiv.org/abs/1906.05467v1

    • [cs.LG]Robust Regression for Safe Exploration in Control
    Anqi Liu, Guanya Shi, Soon-Jo Chung, Anima Anandkumar, Yisong Yue
    http://arxiv.org/abs/1906.05819v1

    • [cs.LG]Spaceland Embedding of Sparse Stochastic Graphs
    Nikos Pitsianis, Alexandros-Stavros Iliopoulos, Dimitris Floros, Xiaobai Sun
    http://arxiv.org/abs/1906.05582v1

    • [cs.LG]Sub-Goal Trees — a Framework for Goal-Directed Trajectory Prediction and Optimization
    Tom Jurgenson, Edward Groshev, Aviv Tamar
    http://arxiv.org/abs/1906.05329v1

    • [cs.LG]Topological Data Analysis for Arrhythmia Detection through Modular Neural Networks
    Meryll Dindin, Yuhei Umeda, Frederic Chazal
    http://arxiv.org/abs/1906.05795v1

    • [cs.LG]Training Neural Networks for and by Interpolation
    Leonard Berrada, Andrew Zisserman, M. Pawan Kumar
    http://arxiv.org/abs/1906.05661v1

    • [cs.LG]Utilizing Edge Features in Graph Neural Networks via Variational Information Maximization
    Pengfei Chen, Weiwen Liu, Chang-Yu Hsieh, Guangyong Chen, Shengyu Zhang
    http://arxiv.org/abs/1906.05488v1

    • [cs.LG]Variance Estimation For Online Regression via Spectrum Thresholding
    Mark Kozdoba, Edward Moroshko, Shie Mannor, Koby Crammer
    http://arxiv.org/abs/1906.05591v1

    • [cs.NE]Associated Learning: Decomposing End-to-end Backpropagation based on Auto-encoders and Target Propagation
    Yu-Wei Kao, Hung-Hsuan Chen
    http://arxiv.org/abs/1906.05560v1

    • [cs.NE]Decentralised Multi-Demic Evolutionary Approach to the Dynamic Multi-Agent Travelling Salesman Problem
    Thomas E. Kent, Arthur G. Richards
    http://arxiv.org/abs/1906.05616v1

    • [cs.NE]MOPED: Efficient priors for scalable variational inference in Bayesian deep neural networks
    Ranganath Krishnan, Mahesh Subedar, Omesh Tickoo
    http://arxiv.org/abs/1906.05323v1

    • [cs.NE]Meta-heuristic for non-homogeneous peak density spaces and implementation on 2 real-world parameter learning/tuning applications
    Mojtaba Moattari, Emad Roshandel, Shima Kamyab, Zohreh Azimifar
    http://arxiv.org/abs/1906.05516v1

    • [cs.PF]Optimizing Redundancy Levels in Master-Worker Compute Clusters for Straggler Mitigation
    Mehmet Fatih Aktas, Emina Soljanin
    http://arxiv.org/abs/1906.05345v1

    • [cs.RO]Deep Reinforcement Learning for Industrial Insertion Tasks with Visual Inputs and Natural Rewards
    Gerrit Schoettler, Ashvin Nair, Jianlan Luo, Shikhar Bahl, Juan Aparicio Ojea, Eugen Solowjow, Sergey Levine
    http://arxiv.org/abs/1906.05841v1

    • [cs.SE]Sionnx: Automatic Unit Test Generator for ONNX Conformance
    Xinli Cai, Peng Zhou, Shuhan Ding, Guoyang Chen, Weifeng Zhang
    http://arxiv.org/abs/1906.05676v1

    • [cs.SI]Identifying Illicit Accounts in Large Scale E-payment Networks — A Graph Representation Learning Approach
    Da Sun Handason Tam, Wing Cheong Lau, Bin Hu, Qiu Fang Ying, Dah Ming Chiu, Hong Liu
    http://arxiv.org/abs/1906.05546v1

    • [econ.EM]Sparse Approximate Factor Estimation for High-Dimensional Covariance Matrices
    Maurizio Daniele, Winfried Pohlmeier, Aygul Zagidullina
    http://arxiv.org/abs/1906.05545v1

    • [econ.GN]ProPublica’s COMPAS Data Revisited
    Matias Barenstein
    http://arxiv.org/abs/1906.04711v2

    • [eess.AS]Telephonetic: Making Neural Language Models Robust to ASR and Semantic Noise
    Chris Larson, Tarek Lahlou, Diana Mingels, Zachary Kulis, Erik Mueller
    http://arxiv.org/abs/1906.05678v1

    • [eess.IV]Deep Variational Networks with Exponential Weighting for Learning Computed Tomography
    Valery Vishnevskiy, Richard Rau, Orcun Goksel
    http://arxiv.org/abs/1906.05528v1

    • [eess.IV]Detection and Correction of Cardiac MR Motion Artefacts during Reconstruction from K-space
    lkay Oksuz, James Clough, Bram Ruijsink, Esther Puyol-Anton, Aurelien Bustin, Gastao Cruz, Claudia Prieto, Daniel Rueckert, Andrew P. King, Julia A. Schnabel
    http://arxiv.org/abs/1906.05695v1

    • [eess.IV]GANPOP: Generative Adversarial Network Prediction of Optical Properties from Single Snapshot Wide-field Images
    Mason T. Chen, Faisal Mahmood, Jordan A. Sweer, Nicholas J. Durr
    http://arxiv.org/abs/1906.05360v1

    • [eess.IV]Image-Adaptive GAN based Reconstruction
    Shady Abu Hussein, Tom Tirer, Raja Giryes
    http://arxiv.org/abs/1906.05284v1

    • [eess.IV]Mask2Lesion: Mask-Constrained Adversarial Skin Lesion Image Synthesis
    Kumar Abhishek, Ghassan Hamarneh
    http://arxiv.org/abs/1906.05845v1

    • [eess.IV]Robust and interpretable blind image denoising via bias-free convolutional neural networks
    Sreyas Mohan, Zahra Kadkhodaie, Eero P. Simoncelli, Carlos Fernandez-Granda
    http://arxiv.org/abs/1906.05478v1

    • [eess.SP]Deep Unfolding for Communications Systems: A Survey and Some New Directions
    Alexios Balatsoukas-Stimming, Christoph Studer
    http://arxiv.org/abs/1906.05774v1

    • [math.NA]Deep Network Approximation Characterized by Number of Neurons
    Zuowei Shen, Haizhao Yang, Shijun Zhang
    http://arxiv.org/abs/1906.05497v1

    • [math.ST]Efficiency of maximum likelihood estimation for a multinomial distribution with known probability sums
    Yo Sheena
    http://arxiv.org/abs/1906.05461v1

    • [math.ST]Hypotheses testing and posterior concentration rates for semi-Markov processes
    V Barbu, Ghislaine Gayraud, N. Limnios, I. Votsi
    http://arxiv.org/abs/1906.05566v1

    • [math.ST]Matrix Mittag—Leffler distributions and modeling heavy-tailed risks
    Hansjoerg Albrecher, Martin Bladt, Mogens Bladt
    http://arxiv.org/abs/1906.05316v1

    • [physics.app-ph]An image-driven machine learning approach to kinetic modeling of a discontinuous precipitation reaction
    Elizabeth Kautz, Wufei Ma, Saumyadeep Jana, Arun Devaraj, Vineet Joshi, Bülent Yener, Daniel Lewis
    http://arxiv.org/abs/1906.05496v1

    • [physics.comp-ph]Modeling the Dynamics of PDE Systems with Physics-Constrained Deep Auto-Regressive Networks
    Nicholas Geneva, Nicholas Zabaras
    http://arxiv.org/abs/1906.05747v1

    • [physics.data-an]Iterative subtraction method for Feature Ranking
    Paul Glaysher, Judith M. Katzy, Sitong An
    http://arxiv.org/abs/1906.05718v1

    • [stat.AP]Dynamic Time Scan Forecasting
    Marcelo Azevedo Costa, Leandro Brioschi Mineti, Marcos Oliveira Prates, Ramiro Ruiz Cardenas
    http://arxiv.org/abs/1906.05399v1

    • [stat.AP]Identifying and Predicting Parkinson’s Disease Subtypes through Trajectory Clustering via Bipartite Networks
    Sanjukta Krishnagopal, Rainer Von Coelln, Lisa M. Shulman, Michelle Girvan
    http://arxiv.org/abs/1906.05338v1

    • [stat.AP]Interpretable ICD Code Embeddings with Self- and Mutual-Attention Mechanisms
    Dixin Luo, Hongteng Xu, Lawrence Carin
    http://arxiv.org/abs/1906.05492v1

    • [stat.AP]Machine Learning Based Analysis and Quantification of Potential Power Gain from Passive Device Installation
    Hoon Hwangbo, Yu Ding, Daniel Cabezon
    http://arxiv.org/abs/1906.05776v1

    • [stat.AP]Use of Emergency Departments by Frail Elderly Patients: Temporal Patterns and Case Complexity
    Jens Rauch, Mathias Denter, Ursula Hübner
    http://arxiv.org/abs/1906.05641v1

    • [stat.ME]Direct Sampling of Bayesian Thin-Plate Splines for Spatial Smoothing
    Gentry White, Dongchu Sun, Paul Speckman
    http://arxiv.org/abs/1906.05575v1

    • [stat.ME]Distributed High-dimensional Regression Under a Quantile Loss Function
    Xi Chen, Weidong Liu, Xiaojun Mao, Zhuoyi Yang
    http://arxiv.org/abs/1906.05741v1

    • [stat.ME]Efficient calibration for high-dimensional computer model output using basis methods
    James M Salter, Daniel B Williamson
    http://arxiv.org/abs/1906.05758v1

    • [stat.ME]Individualized Group Learning
    Chencheng Cai, Rong Chen, Min-ge Xie
    http://arxiv.org/abs/1906.05533v1

    • [stat.ME]Permutation-based uncertainty quantification about a mixing distribution
    Vaidehi Dixit, Ryan Martin
    http://arxiv.org/abs/1906.05349v1

    • [stat.ML]Copulas as High-Dimensional Generative Models: Vine Copula Autoencoders
    Natasa Tagasovska, Damien Ackerer, Thibault Vatter
    http://arxiv.org/abs/1906.05423v1

    • [stat.ML]Optimal low rank tensor recovery
    Jian-Feng Cai, Lizhang Miao, Yang Wang, Yin Xian
    http://arxiv.org/abs/1906.05346v1

    • [stat.ML]Overcoming Mean-Field Approximations in Recurrent Gaussian Process Models
    Alessandro Davide Ialongo, Mark van der Wilk, James Hensman, Carl Edward Rasmussen
    http://arxiv.org/abs/1906.05828v1

    • [stat.ML]Random Tessellation Forests
    Shufei Ge, Shijia Wang, Yee Whye Teh, Liangliang Wang, Lloyd T. Elliott
    http://arxiv.org/abs/1906.05440v1

    • [stat.ML]Reweighted Expectation Maximization
    Adji B. Dieng, John Paisley
    http://arxiv.org/abs/1906.05850v1

    • [stat.ML]Selective prediction-set models with coverage guarantees
    Jean Feng, Arjun Sondhi, Jessica Perry, Noah Simon
    http://arxiv.org/abs/1906.05473v1

    • [stat.ML]Tensor Canonical Correlation Analysis
    You-Lin Chen, Mladen Kolar, Ruey S. Tsay
    http://arxiv.org/abs/1906.05358v1