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