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.DC - 分布式、并行与集群计算 cs.DL - 数字图书馆 cs.DS - 数据结构与算法 cs.ET - 新兴技术 cs.GT - 计算机科学与博弈论 cs.HC - 人机接口 cs.IR - 信息检索 cs.IT - 信息论 cs.LG - 自动学习 cs.LO - 计算逻辑 cs.MA - 多代理系统 cs.NE - 神经与进化计算 cs.PF - 计算性能 cs.RO - 机器人学 cs.SD - 声音处理 cs.SI - 社交网络与信息网络 eess.AS - 语音处理 eess.IV - 图像与视频处理 eess.SP - 信号处理 math.OC - 优化与控制 math.ST - 统计理论 physics.comp-ph - 计算物理学 physics.ins-det - 仪器和探测器 quant-ph - 量子物理 stat.AP - 应用统计 stat.ME - 统计方法论 stat.ML - (统计)机器学习

    • [cs.AI]Infusing domain knowledge in AI-based “black box” models for better explainability with application in bankruptcy prediction
    • [cs.AI]Triple2Vec: Learning Triple Embeddings from Knowledge Graphs
    • [cs.CL]A Cross-Domain Transferable Neural Coherence Model
    • [cs.CL]A Self-Attention Joint Model for Spoken Language Understanding in Situational Dialog Applications
    • [cs.CL]An Incremental Turn-Taking Model For Task-Oriented Dialog Systems
    • [cs.CL]Compositional pre-training for neural semantic parsing
    • [cs.CL]DSReg: Using Distant Supervision as a Regularizer
    • [cs.CL]Extracting adverse drug reactions and their context using sequence labelling ensembles in TAC2017
    • [cs.CL]Interpreting and improving natural-language processing (in machines) with natural language-processing (in the brain)
    • [cs.CL]Leap-LSTM: Enhancing Long Short-Term Memory for Text Categorization
    • [cs.CL]Miss Tools and Mr Fruit: Emergent communication in agents learning about object affordances
    • [cs.CL]On Measuring Gender Bias in Translation of Gender-neutral Pronouns
    • [cs.CL]Revisiting Low-Resource Neural Machine Translation: A Case Study
    • [cs.CL]Specific polysemy of the brief sapiential units
    • [cs.CL]Target-Guided Open-Domain Conversation
    • [cs.CL]Unsupervised Controllable Text Generation with Global Variation Discovery and Disentanglement
    • [cs.CL]Unsupervised End-to-End Learning of Discrete Linguistic Units for Voice Conversion
    • [cs.CL]Using Neural Networks for Relation Extraction from Biomedical Literature
    • [cs.CL]VQVAE Unsupervised Unit Discovery and Multi-scale Code2Spec Inverter for Zerospeech Challenge 2019
    • [cs.CL]XLDA: Cross-Lingual Data Augmentation for Natural Language Inference and Question Answering
    • [cs.CR]Evaluation of Machine Learning-based Anomaly Detection Algorithms on an Industrial Modbus/TCP Data Set
    • [cs.CR]Label Universal Targeted Attack
    • [cs.CV]A Cost Efficient Approach to Correct OCR Errors in Large Document Collections
    • [cs.CV]A Symmetric Encoder-Decoder with Residual Block for Infrared and Visible Image Fusion
    • [cs.CV]An Analysis of Object Embeddings for Image Retrieval
    • [cs.CV]CGaP: Continuous Growth and Pruning for Efficient Deep Learning
    • [cs.CV]Case-Based Histopathological Malignancy Diagnosis using Convolutional Neural Networks
    • [cs.CV]Cerberus: A Multi-headed Derenderer
    • [cs.CV]Compositional Convolutional Networks For Robust Object Classification under Occlusion
    • [cs.CV]Cross-Domain Transferability of Adversarial Perturbations
    • [cs.CV]End-to-End Pore Extraction and Matching in Latent Fingerprints: Going Beyond Minutiae
    • [cs.CV]Enhancing Salient Object Segmentation Through Attention
    • [cs.CV]FaceSwapNet: Landmark Guided Many-to-Many Face Reenactment
    • [cs.CV]FireNet: A Specialized Lightweight Fire & Smoke Detection Model for Real-Time IoT Applications
    • [cs.CV]Hallucinating Optical Flow Features for Video Classification
    • [cs.CV]Image Deformation Meta-Networks for One-Shot Learning
    • [cs.CV]Improving Action Localization by Progressive Cross-stream Cooperation
    • [cs.CV]Integrated Neural Network and Machine Vision Approach For Leather Defect Classification
    • [cs.CV]Invertible generative models for inverse problems: mitigating representation error and dataset bias
    • [cs.CV]JGAN: A Joint Formulation of GAN for Synthesizing Images and Labels
    • [cs.CV]Jointly Learning Structured Analysis Discriminative Dictionary and Analysis Multiclass Classifier
    • [cs.CV]LatentGNN: Learning Efficient Non-local Relations for Visual Recognition
    • [cs.CV]Local Label Propagation for Large-Scale Semi-Supervised Learning
    • [cs.CV]OICSR: Out-In-Channel Sparsity Regularization for Compact Deep Neural Networks
    • [cs.CV]Online Filter Clustering and Pruning for Efficient Convnets
    • [cs.CV]PHT-bot: Deep-Learning based system for automatic risk stratification of COPD patients based upon signs of Pulmonary Hypertension
    • [cs.CV]Progressive Learning of Low-Precision Networks
    • [cs.CV]Semantic Fisher Scores for Task Transfer: Using Objects to Classify Scenes
    • [cs.CV]Shape Evasion: Preventing Body Shape Inference of Multi-Stage Approaches
    • [cs.CV]SizeNet: Weakly Supervised Learning of Visual Size and Fit in Fashion Images
    • [cs.CV]The Nipple-Areola Complex for Criminal Identification
    • [cs.CV]Union Visual Translation Embedding for Visual Relationship Detection and Scene Graph Generation
    • [cs.CV]Unsupervised Learning from Video with Deep Neural Embeddings
    • [cs.CY]A Knowledge Graph-based Approach for Exploring the U.S. Opioid Epidemic
    • [cs.CY]Open Platforms for Artificial Intelligence for Social Good: Common Patterns as a Pathway to True Impact
    • [cs.DC]Clairvoyant State Machine Replication
    • [cs.DC]On Counting the Population Size
    • [cs.DC]On mixing eventual and strong consistency: Bayou revisited
    • [cs.DC]On the Complexity of Distributed Splitting Problems
    • [cs.DL]Social Cards Probably Provide For Better Understanding Of Web Archive Collections
    • [cs.DS]Adaptive Reduced Rank Regression
    • [cs.DS]Private Identity Testing for High-Dimensional Distributions
    • [cs.ET]Supervised Learning in Spiking Neural Networks with Phase-Change Memory Synapses
    • [cs.GT]Manipulating a Learning Defender and Ways to Counteract
    • [cs.HC]Crowdsourced Peer Learning Activity for Internet of Things Education: A Case Study
    • [cs.HC]Effect of context in swipe gesture-based continuous authentication on smartphones
    • [cs.HC]Towards a Wearable Interface for Food Quality Grading through ERP Analysis
    • [cs.IR]A Framework for App Store Optimization
    • [cs.IR]Beyond Personalization: Social Content Recommendation for Creator Equality and Consumer Satisfaction
    • [cs.IR]On a scalable problem transformation method for multi-label learning
    • [cs.IR]Video-based Person Re-identification with Two-stream Convolutional Network and Co-attentive Snippet Embedding
    • [cs.IT]A closed-form formula for the Kullback-Leibler divergence between Cauchy distributions
    • [cs.IT]Communication Complexity in Locally Private Distribution Estimation and Heavy Hitters
    • [cs.IT]Maximal correlation and the rate of Fisher information convergence in the Central Limit Theorem
    • [cs.IT]Statistical Learning Aided List Decoding of Semi-Random Block Oriented Convolutional Codes
    • [cs.LG]A Gram-Gauss-Newton Method Learning Overparameterized Deep Neural Networks for Regression Problems
    • [cs.LG]A Graph Theoretic Additive Approximation of Optimal Transport
    • [cs.LG]A Graph Theoretic Framework of Recomputation Algorithms for Memory-Efficient Backpropagation
    • [cs.LG]A Hessian Based Complexity Measure for Deep Networks
    • [cs.LG]A Review of Semi Supervised Learning Theories and Recent Advances
    • [cs.LG]Accelerating Extreme Classification via Adaptive Feature Agglomeration
    • [cs.LG]Adversarial Attacks on Remote User Authentication Using Behavioural Mouse Dynamics
    • [cs.LG]Adversarial Domain Adaptation Being Aware of Class Relationships
    • [cs.LG]Adversarially Robust Learning Could Leverage Computational Hardness
    • [cs.LG]Amalgamating Filtered Knowledge: Learning Task-customized Student from Multi-task Teachers
    • [cs.LG]An Empirical Study on Post-processing Methods for Word Embeddings
    • [cs.LG]Attacker Behaviour Profiling using Stochastic Ensemble of Hidden Markov Models
    • [cs.LG]Better Long-Range Dependency By Bootstrapping A Mutual Information Regularizer
    • [cs.LG]Beyond Exponentially Discounted Sum: Automatic Learning of Return Function
    • [cs.LG]Brain Signal Classification via Learning Connectivity Structure
    • [cs.LG]BreizhCrops: A Satellite Time Series Dataset for Crop Type Identification
    • [cs.LG]COSET: A Benchmark for Evaluating Neural Program Embeddings
    • [cs.LG]Capsule Routing via Variational Bayes
    • [cs.LG]Causal Confusion in Imitation Learning
    • [cs.LG]CompactNet: Platform-Aware Automatic Optimization for Convolutional Neural Networks
    • [cs.LG]Conditions on Features for Temporal Difference-Like Methods to Converge
    • [cs.LG]Connections Between Mirror Descent, Thompson Sampling and the Information Ratio
    • [cs.LG]Controlling Neural Level Sets
    • [cs.LG]Correlation Clustering with Adaptive Similarity Queries
    • [cs.LG]Cross-lingual Knowledge Graph Alignment via Graph Matching Neural Network
    • [cs.LG]Deep Neural Networks Abstract Like Humans
    • [cs.LG]Deep Scale-spaces: Equivariance Over Scale
    • [cs.LG]Differentiable Algorithm Networks for Composable Robot Learning
    • [cs.LG]Differentiable Quantization of Deep Neural Networks
    • [cs.LG]Differentiable Sorting using Optimal Transport:The Sinkhorn CDF and Quantile Operator
    • [cs.LG]Distributed estimation of the inverse Hessian by determinantal averaging
    • [cs.LG]Divide-and-Conquer Adversarial Detection
    • [cs.LG]Dynamic Nonparametric Edge-Clustering Model for Time-Evolving Sparse Networks
    • [cs.LG]EDUCE: Explaining model Decisions through Unsupervised Concepts Extraction
    • [cs.LG]Efficient Wrapper Feature Selection using Autoencoder and Model Based Elimination
    • [cs.LG]EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks
    • [cs.LG]Equivalent and Approximate Transformations of Deep Neural Networks
    • [cs.LG]Evaluating and Calibrating Uncertainty Prediction in Regression Tasks
    • [cs.LG]Evaluating time series forecasting models: An empirical study on performance estimation methods
    • [cs.LG]FAN: Focused Attention Networks
    • [cs.LG]Forecasting Stock Market with Support Vector Regression and Butterfly Optimization Algorithm
    • [cs.LG]Generalization Bounds in the Predict-then-Optimize Framework
    • [cs.LG]Greedy InfoMax for Biologically Plausible Self-Supervised Representation Learning
    • [cs.LG]Importance of user inputs while using incremental learning to personalize human activity recognition models
    • [cs.LG]Improved Training Speed, Accuracy, and Data Utilization Through Loss Function Optimization
    • [cs.LG]Improving the Robustness of Deep Neural Networks via Adversarial Training with Triplet Loss
    • [cs.LG]Incidence Networks for Geometric Deep Learning
    • [cs.LG]Interactive Teaching Algorithms for Inverse Reinforcement Learning
    • [cs.LG]LambdaOpt: Learn to Regularize Recommender Models in Finer Levels
    • [cs.LG]Learning Efficient and Effective Exploration Policies with Counterfactual Meta Policy
    • [cs.LG]Learning In Practice: Reasoning About Quantization
    • [cs.LG]ME-Net: Towards Effective Adversarial Robustness with Matrix Estimation
    • [cs.LG]Machine Learning on data with sPlot background subtraction
    • [cs.LG]Model-Agnostic Counterfactual Explanations for Consequential Decisions
    • [cs.LG]Multi-Modal Graph Interaction for Multi-Graph Convolution Network in Urban Spatiotemporal Forecasting
    • [cs.LG]Network Deconvolution
    • [cs.LG]On Dropout and Nuclear Norm Regularization
    • [cs.LG]OrderNet: Ordering by Example
    • [cs.LG]Overlearning Reveals Sensitive Attributes
    • [cs.LG]Polynomial Tensor Sketch for Element-wise Function of Low-Rank Matrix
    • [cs.LG]Radar-based Road User Classification and Novelty Detection with Recurrent Neural Network Ensembles
    • [cs.LG]Rare Failure Prediction via Event Matching for Aerospace Applications
    • [cs.LG]RecNets: Channel-wise Recurrent Convolutional Neural Networks
    • [cs.LG]Regression via Kirszbraun Extension with Applications to Imitation Learning
    • [cs.LG]Relational Representation Learning for Dynamic (Knowledge) Graphs: A Survey
    • [cs.LG]Repeated A/B Testing
    • [cs.LG]SGD on Neural Networks Learns Functions of Increasing Complexity
    • [cs.LG]Single-Net Continual Learning with Progressive Segmented Training (PST)
    • [cs.LG]Sketch-based Randomized Algorithms for Dynamic Graph Regression
    • [cs.LG]Snooping Attacks on Deep Reinforcement Learning
    • [cs.LG]Solving NP-Hard Problems on Graphs by Reinforcement Learning without Domain Knowledge
    • [cs.LG]Structure Learning for Neural Module Networks
    • [cs.LG]Supervised Discriminative Sparse PCA for Com-Characteristic Gene Selection and Tumor Classification on Multiview Biological Data
    • [cs.LG]Tight Regret Bounds for Model-Based Reinforcement Learning with Greedy Policies
    • [cs.LG]Towards Interpretable Sparse Graph Representation Learning with Laplacian Pooling
    • [cs.LG]Uncertainty-based Continual Learning with Adaptive Regularization
    • [cs.LG]Universality Theorems for Generative Models
    • [cs.LG]Validating the Validation: Reanalyzing a large-scale comparison of Deep Learning and Machine Learning models for bioactivity prediction
    • [cs.LG]Variational Information Bottleneck for Unsupervised Clustering: Deep Gaussian Mixture Embedding
    • [cs.LG]When can unlabeled data improve the learning rate?
    • [cs.LO]NIL: Learning Nonlinear Interpolants
    • [cs.MA]A Parameterized Perspective on Protecting Elections
    • [cs.NE]Efficient Network Construction through Structural Plasticity
    • [cs.NE]Inference with Hybrid Bio-hardware Neural Networks
    • [cs.NE]Multi-Sample Dropout for Accelerated Training and Better Generalization
    • [cs.PF]Function-as-a-Service Benchmarking Framework
    • [cs.RO]Autonomous skill discovery with Quality-Diversity and Unsupervised Descriptors
    • [cs.RO]Fast human motion prediction for human-robot collaboration with wearable interfaces
    • [cs.RO]Mechanism Singularities Revisited from an Algebraic Viewpoint
    • [cs.RO]Next-Generation Inertial Navigation Computation Based on Functional Iteration
    • [cs.RO]Robotic bees: Algorithms for collision detection and prevention
    • [cs.SD]Texture Selection for Automatic Music Genre Classification
    • [cs.SD]Two-level Explanations in Music Emotion Recognition
    • [cs.SI]Adaptive Influence Maximization with Myopic Feedback
    • [cs.SI]The HyperBagGraph DataEdron: An Enriched Browsing Experience of Multimedia Datasets
    • [eess.AS]SignalTrain: Profiling Audio Compressors with Deep Neural Networks
    • [eess.IV]A Compact Representation of Histopathology Images using Digital Stain Separation & Frequency-Based Encoded Local Projections
    • [eess.IV]Adaptive Lighting for Data-Driven Non-Line-of-Sight 3D Localization and Object Identification
    • [eess.SP]Distributed Antenna Selection for Massive MIMO using Reversing Petri Nets
    • [eess.SP]Multiuser MISO UAV Communications in Uncertain Environments with No-fly Zones: Robust Trajectory and Resource Allocation Design
    • [eess.SP]Towards Practical Indoor Positioning Based on Massive MIMO Systems
    • [math.OC]An Accelerated Decentralized Stochastic Proximal Algorithm for Finite Sums
    • [math.OC]Analysis of Gradient Clipping and Adaptive Scaling with a Relaxed Smoothness Condition
    • [math.OC]Concavifiability and convergence: necessary and sufficient conditions for gradient descent analysis
    • [math.OC]Direct Nonlinear Acceleration
    • [math.OC]Finite-Time Analysis of Q-Learning with Linear Function Approximation
    • [math.OC]Sample Complexity of Sample Average Approximation for Conditional Stochastic Optimization
    • [math.ST]A Projected Non-Linear Conjugate Gradient Algorithm for Destructive Negative Binomial Cure Rate Model
    • [math.ST]Large Sample Properties of Matching for Balance
    • [math.ST]On the identification of individual principal stratum direct, natural direct and pleiotropic effects without cross-world independence assumptions
    • [math.ST]Probabilistic mappings and Bayesian nonparametrics
    • [math.ST]Statistical bounds for entropic optimal transport: sample complexity and the central limit theorem
    • [math.ST]The bias of the sample mean in multi-armed bandits can be positive or negative
    • [physics.comp-ph]AI Feynman: a Physics-Inspired Method for Symbolic Regression
    • [physics.ins-det]Fast Data-Driven Simulation of Cherenkov Detectors Using Generative Adversarial Networks
    • [quant-ph]Resource theory of asymmetric distinguishability
    • [stat.AP]Evaluation of mineralogy per geological layers by Approximate Bayesian Computation
    • [stat.ME]ADDIS: adaptive algorithms for online FDR control with conservative nulls
    • [stat.ME]Can we disregard the whole model? Omnibus non-inferiority testing for $R^{2}$ in multivariable linear regression and $\hatη^{2}$ in ANOVA
    • [stat.ME]Estimating Average Treatment Effects Utilizing Fractional Imputation when Confounders are Subject to Missingness
    • [stat.ME]Intervention in undirected Ising graphs and the partition function
    • [stat.ME]Kalman Filter, Sensor Fusion, and Constrained Regression: Equivalences and Insights
    • [stat.ME]Robust Nonparametric Difference-in-Differences Estimation
    • [stat.ME]Sparse Estimation of Historical Functional Linear Models with a Nested Group Bridge Approach
    • [stat.ME]Tuning Free Rank-Sparse Bayesian Matrix and Tensor Completion with Global-Local Priors
    • [stat.ME]Validation of Approximate Likelihood and Emulator Models for Computationally Intensive Simulations
    • [stat.ML]Ancestral causal learning in high dimensions with a human genome-wide application
    • [stat.ML]Anomaly scores for generative models
    • [stat.ML]Discrete Infomax Codes for Meta-Learning
    • [stat.ML]Distributed Linear Model Clustering over Networks: A Tree-Based Fused-Lasso ADMM Approach
    • [stat.ML]Estimating and Inferring the Maximum Degree of Stimulus-Locked Time-Varying Brain Connectivity Networks
    • [stat.ML]Global forensic geolocation with deep neural networks
    • [stat.ML]GraphNVP: An Invertible Flow Model for Generating Molecular Graphs
    • [stat.ML]Learning Bregman Divergences
    • [stat.ML]Learning Dynamics of Attention: Human Prior for Interpretable Machine Reasoning
    • [stat.ML]Learning distant cause and effect using only local and immediate credit assignment
    • [stat.ML]Quantifying the generalization error in deep learning in terms of data distribution and neural network smoothness
    • [stat.ML]Recursive Estimation for Sparse Gaussian Process Regression
    • [stat.ML]Robustness Quantification for Classification with Gaussian Processes
    • [stat.ML]Scaleable input gradient regularization for adversarial robustness
    • [stat.ML]Stochastic Proximal Langevin Algorithm: Potential Splitting and Nonasymptotic Rates
    • [stat.ML]The Hierarchy of Stable Distributions and Operators to Trade Off Stability and Performance
    • [stat.ML]Understanding the Behaviour of the Empirical Cross-Entropy Beyond the Training Distribution

    ·····································

    • [cs.AI]Infusing domain knowledge in AI-based “black box” models for better explainability with application in bankruptcy prediction
    Sheikh Rabiul Islam, William Eberle, Sid Bundy, Sheikh Khaled Ghafoor
    http://arxiv.org/abs/1905.11474v1

    • [cs.AI]Triple2Vec: Learning Triple Embeddings from Knowledge Graphs
    Valeria Fionda, Giuseppe Pirró
    http://arxiv.org/abs/1905.11691v1

    • [cs.CL]A Cross-Domain Transferable Neural Coherence Model
    Peng Xu, Hamidreza Saghir, Jin Sung Kang, Teng Long, Avishek Joey Bose, Yanshuai Cao, Jackie Chi Kit Cheung
    http://arxiv.org/abs/1905.11912v1

    • [cs.CL]A Self-Attention Joint Model for Spoken Language Understanding in Situational Dialog Applications
    Mengyang Chen, Jin Zeng, Jie Lou
    http://arxiv.org/abs/1905.11393v1

    • [cs.CL]An Incremental Turn-Taking Model For Task-Oriented Dialog Systems
    Andrei C. Coman, Koichiro Yoshino, Yukitoshi Murase, Satoshi Nakamura, Giuseppe Riccardi
    http://arxiv.org/abs/1905.11806v1

    • [cs.CL]Compositional pre-training for neural semantic parsing
    Amir Ziai
    http://arxiv.org/abs/1905.11531v1

    • [cs.CL]DSReg: Using Distant Supervision as a Regularizer
    Yuxian Meng, Muyu Li, Wei Wu, Jiwei Li
    http://arxiv.org/abs/1905.11658v1

    • [cs.CL]Extracting adverse drug reactions and their context using sequence labelling ensembles in TAC2017
    Maksim Belousov, Nikola Milosevic, William Dixon, Goran Nenadic
    http://arxiv.org/abs/1905.11716v1

    • [cs.CL]Interpreting and improving natural-language processing (in machines) with natural language-processing (in the brain)
    Mariya Toneva, Leila Wehbe
    http://arxiv.org/abs/1905.11833v1

    • [cs.CL]Leap-LSTM: Enhancing Long Short-Term Memory for Text Categorization
    Ting Huang, Gehui Shen, Zhi-Hong Deng
    http://arxiv.org/abs/1905.11558v1

    • [cs.CL]Miss Tools and Mr Fruit: Emergent communication in agents learning about object affordances
    Diane Bouchacourt, Marco Baroni
    http://arxiv.org/abs/1905.11871v1

    • [cs.CL]On Measuring Gender Bias in Translation of Gender-neutral Pronouns
    Won Ik Cho, Ji Won Kim, Seok Min Kim, Nam Soo Kim
    http://arxiv.org/abs/1905.11684v1

    • [cs.CL]Revisiting Low-Resource Neural Machine Translation: A Case Study
    Rico Sennrich, Biao Zhang
    http://arxiv.org/abs/1905.11901v1

    • [cs.CL]Specific polysemy of the brief sapiential units
    Marie-Christine Bornes-Varol, Marie-Sol Ortola, Gronoff Jean-Daniel
    http://arxiv.org/abs/1905.11836v1

    • [cs.CL]Target-Guided Open-Domain Conversation
    Jianheng Tang, Tiancheng Zhao, Chengyan Xiong, Xiaodan Liang, Eric P. Xing, Zhiting Hu
    http://arxiv.org/abs/1905.11553v1

    • [cs.CL]Unsupervised Controllable Text Generation with Global Variation Discovery and Disentanglement
    Peng Xu, Yanshuai Cao, Jackie Chi Kit Cheung
    http://arxiv.org/abs/1905.11975v1

    • [cs.CL]Unsupervised End-to-End Learning of Discrete Linguistic Units for Voice Conversion
    Andy T. Liu, Po-chun Hsu, Hung-yi Lee
    http://arxiv.org/abs/1905.11563v1

    • [cs.CL]Using Neural Networks for Relation Extraction from Biomedical Literature
    Diana Sousa, Andre Lamurias, Francisco M. Couto
    http://arxiv.org/abs/1905.11391v1

    • [cs.CL]VQVAE Unsupervised Unit Discovery and Multi-scale Code2Spec Inverter for Zerospeech Challenge 2019
    Andros Tjandra, Berrak Sisman, Mingyang Zhang, Sakriani Sakti, Haizhou Li, Satoshi Nakamura
    http://arxiv.org/abs/1905.11449v1

    • [cs.CL]XLDA: Cross-Lingual Data Augmentation for Natural Language Inference and Question Answering
    Jasdeep Singh, Bryan McCann, Nitish Shirish Keskar, Caiming Xiong, Richard Socher
    http://arxiv.org/abs/1905.11471v1

    • [cs.CR]Evaluation of Machine Learning-based Anomaly Detection Algorithms on an Industrial Modbus/TCP Data Set
    Simon Duque Anton, Suneetha Kanoor, Daniel Fraunholz, Hans Dieter Schotten
    http://arxiv.org/abs/1905.11757v1

    • [cs.CR]Label Universal Targeted Attack
    Naveed Akhtar, Mohammad A. A. K. Jalwana, Mohammed Bennamoun, Ajmal Mian
    http://arxiv.org/abs/1905.11544v1

    • [cs.CV]A Cost Efficient Approach to Correct OCR Errors in Large Document Collections
    Deepayan Das, Jerin Philip, Minesh Mathew, C. V. Jawahar
    http://arxiv.org/abs/1905.11739v1

    • [cs.CV]A Symmetric Encoder-Decoder with Residual Block for Infrared and Visible Image Fusion
    Lihua Jian, Xiaomin Yang, Zheng Liu, Gwanggil Jeon, Mingliang Gao, David Chisholm
    http://arxiv.org/abs/1905.11447v1

    • [cs.CV]An Analysis of Object Embeddings for Image Retrieval
    Bor-Chun Chen, Larry S. Davis, Ser-Nam Lim
    http://arxiv.org/abs/1905.11903v1

    • [cs.CV]CGaP: Continuous Growth and Pruning for Efficient Deep Learning
    Xiaocong Du, Zheng Li, Yu Cao
    http://arxiv.org/abs/1905.11533v1

    • [cs.CV]Case-Based Histopathological Malignancy Diagnosis using Convolutional Neural Networks
    Qicheng Lao, Thomas Fevens
    http://arxiv.org/abs/1905.11567v1

    • [cs.CV]Cerberus: A Multi-headed Derenderer
    Boyang Deng, Simon Kornblith, Geoffrey Hinton
    http://arxiv.org/abs/1905.11940v1

    • [cs.CV]Compositional Convolutional Networks For Robust Object Classification under Occlusion
    Adam Kortylewski, Qing Liu, Huiyu Wang, Zhishuai Zhang, Alan Yuille
    http://arxiv.org/abs/1905.11826v1

    • [cs.CV]Cross-Domain Transferability of Adversarial Perturbations
    Muzammal Naseer, Salman H. Khan, Harris Khan, Fahad Shahbaz Khan, Fatih Porikli
    http://arxiv.org/abs/1905.11736v1

    • [cs.CV]End-to-End Pore Extraction and Matching in Latent Fingerprints: Going Beyond Minutiae
    Dinh-Luan Nguyen, Anil K. Jain
    http://arxiv.org/abs/1905.11472v1

    • [cs.CV]Enhancing Salient Object Segmentation Through Attention
    Anuj Pahuja, Avishek Majumder, Anirban Chakraborty, R. Venkatesh Babu
    http://arxiv.org/abs/1905.11522v1

    • [cs.CV]FaceSwapNet: Landmark Guided Many-to-Many Face Reenactment
    Jiangning Zhang, Xianfang Zeng, Yusu Pan, Yong Liu, Yu Ding, Changjie Fan
    http://arxiv.org/abs/1905.11805v1

    • [cs.CV]FireNet: A Specialized Lightweight Fire & Smoke Detection Model for Real-Time IoT Applications
    Arpit Jadon, Mohd. Omama, Akshay Varshney, Mohammad Samar Ansari, Rishabh Sharma
    http://arxiv.org/abs/1905.11922v1

    • [cs.CV]Hallucinating Optical Flow Features for Video Classification
    Yongyi Tang, Lin Ma, Lianqiang Zhou
    http://arxiv.org/abs/1905.11799v1

    • [cs.CV]Image Deformation Meta-Networks for One-Shot Learning
    Zitian Chen, Yanwei Fu, Yu-Xiong Wang, Lin Ma, Wei Liu, Martial Hebert
    http://arxiv.org/abs/1905.11641v1

    • [cs.CV]Improving Action Localization by Progressive Cross-stream Cooperation
    Rui Su, Wanli Ouyang, Luping Zhou, Dong Xu
    http://arxiv.org/abs/1905.11575v1

    • [cs.CV]Integrated Neural Network and Machine Vision Approach For Leather Defect Classification
    Sze-Teng Liong, Y. S. Gan, Yen-Chang Huang, Kun-Hong Liu, Wei-Chuen Yau
    http://arxiv.org/abs/1905.11731v1

    • [cs.CV]Invertible generative models for inverse problems: mitigating representation error and dataset bias
    Muhammad Asim, Ali Ahmed, Paul Hand
    http://arxiv.org/abs/1905.11672v1

    • [cs.CV]JGAN: A Joint Formulation of GAN for Synthesizing Images and Labels
    Minje Park
    http://arxiv.org/abs/1905.11574v1

    • [cs.CV]Jointly Learning Structured Analysis Discriminative Dictionary and Analysis Multiclass Classifier
    Zhao Zhang, Weiming Jiang, Jie Qin, Li Zhang, Fanzhang Li, Min Zhang, Shuicheng Yan
    http://arxiv.org/abs/1905.11543v1

    • [cs.CV]LatentGNN: Learning Efficient Non-local Relations for Visual Recognition
    Songyang Zhang, Shipeng Yan, Xuming He
    http://arxiv.org/abs/1905.11634v1

    • [cs.CV]Local Label Propagation for Large-Scale Semi-Supervised Learning
    Chengxu Zhuang, Xuehao Ding, Divyanshu Murli, Daniel Yamins
    http://arxiv.org/abs/1905.11581v1

    • [cs.CV]OICSR: Out-In-Channel Sparsity Regularization for Compact Deep Neural Networks
    Jiashi Li, Qi Qi, Jingyu Wang, Ce Ge, Yujian Li, Zhangzhang Yue, Haifeng Sun
    http://arxiv.org/abs/1905.11664v1

    • [cs.CV]Online Filter Clustering and Pruning for Efficient Convnets
    Zhengguang Zhou, Wengang Zhou, Richang Hong, Houqiang Li
    http://arxiv.org/abs/1905.11787v1

    • [cs.CV]PHT-bot: Deep-Learning based system for automatic risk stratification of COPD patients based upon signs of Pulmonary Hypertension
    David Chettrit, Orna Bregman Amitai, Itamar Tamir, Amir Bar, Eldad Elnekave
    http://arxiv.org/abs/1905.11773v1

    • [cs.CV]Progressive Learning of Low-Precision Networks
    Zhengguang Zhou, Wengang Zhou, Xutao Lv, Xuan Huang, Xiaoyu Wang, Houqiang Li
    http://arxiv.org/abs/1905.11781v1

    • [cs.CV]Semantic Fisher Scores for Task Transfer: Using Objects to Classify Scenes
    Mandar Dixit, Yunsheng Li, Nuno Vasconcelos
    http://arxiv.org/abs/1905.11539v1

    • [cs.CV]Shape Evasion: Preventing Body Shape Inference of Multi-Stage Approaches
    Hosnieh Sattar, Katharina Krombholz, Gerard Pons-Moll, Mario Fritz
    http://arxiv.org/abs/1905.11503v1

    • [cs.CV]SizeNet: Weakly Supervised Learning of Visual Size and Fit in Fashion Images
    Nour Karessli, Romain Guigourès, Reza Shirvany
    http://arxiv.org/abs/1905.11784v1

    • [cs.CV]The Nipple-Areola Complex for Criminal Identification
    Wojciech Michal Matkowski, Krzysztof Matkowski, Adams Wai-Kin Kong, Cory Lloyd Hall
    http://arxiv.org/abs/1905.11651v1

    • [cs.CV]Union Visual Translation Embedding for Visual Relationship Detection and Scene Graph Generation
    Zih-Siou Hung, Arun Mallya, Svetlana Lazebnik
    http://arxiv.org/abs/1905.11624v1

    • [cs.CV]Unsupervised Learning from Video with Deep Neural Embeddings
    Chengxu Zhuang, Alex Andonian, Daniel Yamins
    http://arxiv.org/abs/1905.11954v1

    • [cs.CY]A Knowledge Graph-based Approach for Exploring the U.S. Opioid Epidemic
    Maulik R. Kamdar, Tymor Hamamsy, Shea Shelton, Ayin Vala, Tome Eftimov, James Zou, Suzanne Tamang
    http://arxiv.org/abs/1905.11513v1

    • [cs.CY]Open Platforms for Artificial Intelligence for Social Good: Common Patterns as a Pathway to True Impact
    Kush R. Varshney, Aleksandra Mojsilovic
    http://arxiv.org/abs/1905.11519v1

    • [cs.DC]Clairvoyant State Machine Replication
    Rida Bazzi, Maurice Herlihy
    http://arxiv.org/abs/1905.11607v1

    • [cs.DC]On Counting the Population Size
    Petra Berenbrink, Dominik Kaaser, Tomasz Radzik
    http://arxiv.org/abs/1905.11962v1

    • [cs.DC]On mixing eventual and strong consistency: Bayou revisited
    Maciej Kokociński, Tadeusz Kobus, Paweł T. Wojciechowski
    http://arxiv.org/abs/1905.11762v1

    • [cs.DC]On the Complexity of Distributed Splitting Problems
    Philipp Bamberger, Mohsen Ghaffari, Fabian Kuhn, Yannic Maus, Jara Uitto
    http://arxiv.org/abs/1905.11573v1

    • [cs.DL]Social Cards Probably Provide For Better Understanding Of Web Archive Collections
    Shawn M. Jones, Michele C. Weigle, Michael L. Nelson
    http://arxiv.org/abs/1905.11342v2

    • [cs.DS]Adaptive Reduced Rank Regression
    Qiong Wu, Felix Ming Fai Wong, Zhenming Liu, Yanhua Li, Varun Kanade
    http://arxiv.org/abs/1905.11566v1

    • [cs.DS]Private Identity Testing for High-Dimensional Distributions
    Clément L. Canonne, Gautam Kamath, Audra McMillan, Jonathan Ullman, Lydia Zakynthinou
    http://arxiv.org/abs/1905.11947v1

    • [cs.ET]Supervised Learning in Spiking Neural Networks with Phase-Change Memory Synapses
    S. R. Nandakumar, Irem Boybat, Manuel Le Gallo, Evangelos Eleftheriou, Abu Sebastian, Bipin Rajendran
    http://arxiv.org/abs/1905.11929v1

    • [cs.GT]Manipulating a Learning Defender and Ways to Counteract
    Jiarui Gan, Qingyu Guo, Long Tran-Thanh, Bo An, Michael Wooldridge
    http://arxiv.org/abs/1905.11759v1

    • [cs.HC]Crowdsourced Peer Learning Activity for Internet of Things Education: A Case Study
    Ahmed Hussein, Mahmoud Barhamgi, Massimo Vecchio, Charith Perera
    http://arxiv.org/abs/1905.11652v1

    • [cs.HC]Effect of context in swipe gesture-based continuous authentication on smartphones
    Pekka Siirtola, Jukka Komulainen, Vili Kellokumpu
    http://arxiv.org/abs/1905.11780v1

    • [cs.HC]Towards a Wearable Interface for Food Quality Grading through ERP Analysis
    M. Guermandi, S. Benatti, D. Brunelli, V. Kartsch, L. Benini
    http://arxiv.org/abs/1905.11633v1

    • [cs.IR]A Framework for App Store Optimization
    Artur Strzelecki
    http://arxiv.org/abs/1905.11668v1

    • [cs.IR]Beyond Personalization: Social Content Recommendation for Creator Equality and Consumer Satisfaction
    Wenyi Xiao, Huan Zhao, Haojie Pan, Yangqiu Song, Vincent W. Zheng, Qiang Yang
    http://arxiv.org/abs/1905.11900v1

    • [cs.IR]On a scalable problem transformation method for multi-label learning
    Dora Jambor, Peng Yu
    http://arxiv.org/abs/1905.11518v1

    • [cs.IR]Video-based Person Re-identification with Two-stream Convolutional Network and Co-attentive Snippet Embedding
    Peixian Chen, Pingyang Dai, Qiong Wu, Yuyu Huang
    http://arxiv.org/abs/1905.11862v1

    • [cs.IT]A closed-form formula for the Kullback-Leibler divergence between Cauchy distributions
    Frédéric Chyzak, Frank Nielsen
    http://arxiv.org/abs/1905.10965v2

    • [cs.IT]Communication Complexity in Locally Private Distribution Estimation and Heavy Hitters
    Jayadev Acharya, Ziteng Sun
    http://arxiv.org/abs/1905.11888v1

    • [cs.IT]Maximal correlation and the rate of Fisher information convergence in the Central Limit Theorem
    Oliver Johnson
    http://arxiv.org/abs/1905.11913v1

    • [cs.IT]Statistical Learning Aided List Decoding of Semi-Random Block Oriented Convolutional Codes
    Wenchao Lin, Xiao Ma, Suihua Cai, Baodian Wei
    http://arxiv.org/abs/1905.11392v1

    • [cs.LG]A Gram-Gauss-Newton Method Learning Overparameterized Deep Neural Networks for Regression Problems
    Tianle Cai, Ruiqi Gao, Jikai Hou, Siyu Chen, Dong Wang, Di He, Zhihua Zhang, Liwei Wang
    http://arxiv.org/abs/1905.11675v1

    • [cs.LG]A Graph Theoretic Additive Approximation of Optimal Transport
    Nathaniel Lahn, Deepika Mulchandani, Sharath Raghvendra
    http://arxiv.org/abs/1905.11830v1

    • [cs.LG]A Graph Theoretic Framework of Recomputation Algorithms for Memory-Efficient Backpropagation
    Mitsuru Kusumoto, Takuya Inoue, Gentaro Watanabe, Takuya Akiba, Masanori Koyama
    http://arxiv.org/abs/1905.11722v1

    • [cs.LG]A Hessian Based Complexity Measure for Deep Networks
    Hamid Javadi, Randall Balestriero, Richard Baraniuk
    http://arxiv.org/abs/1905.11639v1

    • [cs.LG]A Review of Semi Supervised Learning Theories and Recent Advances
    Enmei Tu, Jie Yang
    http://arxiv.org/abs/1905.11590v1

    • [cs.LG]Accelerating Extreme Classification via Adaptive Feature Agglomeration
    Ankit Jalan, Purushottam Kar
    http://arxiv.org/abs/1905.11769v1

    • [cs.LG]Adversarial Attacks on Remote User Authentication Using Behavioural Mouse Dynamics
    Yi Xiang Marcus Tan, Alfonso Iacovazzi, Ivan Homoliak, Yuval Elovici, Alexander Binder
    http://arxiv.org/abs/1905.11831v1

    • [cs.LG]Adversarial Domain Adaptation Being Aware of Class Relationships
    Zeya Wang, Baoyu Jing, Yang Ni, Nanqing Dong, Pengtao Xie, Eric P. Xing
    http://arxiv.org/abs/1905.11931v1

    • [cs.LG]Adversarially Robust Learning Could Leverage Computational Hardness
    Sanjam Garg, Somesh Jha, Saeed Mahloujifar, Mohammad Mahmoody
    http://arxiv.org/abs/1905.11564v1

    • [cs.LG]Amalgamating Filtered Knowledge: Learning Task-customized Student from Multi-task Teachers
    Jingwen Ye, Xinchao Wang, Yixin Ji, Kairi Ou, Mingli Song
    http://arxiv.org/abs/1905.11569v1

    • [cs.LG]An Empirical Study on Post-processing Methods for Word Embeddings
    Shuai Tang, Mahta Mousavi, Virginia R. de Sa
    http://arxiv.org/abs/1905.10971v2

    • [cs.LG]Attacker Behaviour Profiling using Stochastic Ensemble of Hidden Markov Models
    Soham Deshmukh, Rahul Rade, Dr. Faruk Kazi
    http://arxiv.org/abs/1905.11824v1

    • [cs.LG]Better Long-Range Dependency By Bootstrapping A Mutual Information Regularizer
    Yanshuai Cao, Peng Xu
    http://arxiv.org/abs/1905.11978v1

    • [cs.LG]Beyond Exponentially Discounted Sum: Automatic Learning of Return Function
    Yufei Wang, Qiwei Ye, Tie-Yan Liu
    http://arxiv.org/abs/1905.11591v1

    • [cs.LG]Brain Signal Classification via Learning Connectivity Structure
    Soobeom Jang, Seong-Eun Moon, Jong-Seok Lee
    http://arxiv.org/abs/1905.11678v1

    • [cs.LG]BreizhCrops: A Satellite Time Series Dataset for Crop Type Identification
    Marc Rußwurm, Sébastien Lefèvre, Marco Körner
    http://arxiv.org/abs/1905.11893v1

    • [cs.LG]COSET: A Benchmark for Evaluating Neural Program Embeddings
    Ke Wang, Mihai Christodorescu
    http://arxiv.org/abs/1905.11445v1

    • [cs.LG]Capsule Routing via Variational Bayes
    Fabio De Sousa Ribeiro, Georgios Leontidis, Stefanos Kollias
    http://arxiv.org/abs/1905.11455v1

    • [cs.LG]Causal Confusion in Imitation Learning
    Pim de Haan, Dinesh Jayaraman, Sergey Levine
    http://arxiv.org/abs/1905.11979v1

    • [cs.LG]CompactNet: Platform-Aware Automatic Optimization for Convolutional Neural Networks
    Weicheng Li, Rui Wang, Zhongzhi Luan, Di Huang, Zidong Du, Yunji Chen, Depei Qian
    http://arxiv.org/abs/1905.11669v1

    • [cs.LG]Conditions on Features for Temporal Difference-Like Methods to Converge
    Marcus Hutter, Samuel Yang-Zhao, Sultan J. Majeed
    http://arxiv.org/abs/1905.11702v1

    • [cs.LG]Connections Between Mirror Descent, Thompson Sampling and the Information Ratio
    Julian Zimmert, Tor Lattimore
    http://arxiv.org/abs/1905.11817v1

    • [cs.LG]Controlling Neural Level Sets
    Matan Atzmon, Niv Haim, Lior Yariv, Ofer Israelov, Haggai Maron, Yaron Lipman
    http://arxiv.org/abs/1905.11911v1

    • [cs.LG]Correlation Clustering with Adaptive Similarity Queries
    Marco Bressan, Nicolò Cesa-Bianchi, Andrea Paudice, Fabio Vitale
    http://arxiv.org/abs/1905.11902v1

    • [cs.LG]Cross-lingual Knowledge Graph Alignment via Graph Matching Neural Network
    Kun Xu, Liwei Wang, Mo Yu, Yansong Feng, Yan Song, Zhiguo Wang, Dong Yu
    http://arxiv.org/abs/1905.11605v1

    • [cs.LG]Deep Neural Networks Abstract Like Humans
    Alex Gain, Hava Siegelmann
    http://arxiv.org/abs/1905.11515v1

    • [cs.LG]Deep Scale-spaces: Equivariance Over Scale
    Daniel E. Worrall, Max Welling
    http://arxiv.org/abs/1905.11697v1

    • [cs.LG]Differentiable Algorithm Networks for Composable Robot Learning
    Peter Karkus, Xiao Ma, David Hsu, Leslie Pack Kaelbling, Wee Sun Lee, Tomas Lozano-Perez
    http://arxiv.org/abs/1905.11602v1

    • [cs.LG]Differentiable Quantization of Deep Neural Networks
    Stefan Uhlich, Lukas Mauch, Kazuki Yoshiyama, Fabien Cardinaux, Javier Alonso Garcia, Stephen Tiedemann, Thomas Kemp, Akira Nakamura
    http://arxiv.org/abs/1905.11452v1

    • [cs.LG]Differentiable Sorting using Optimal Transport:The Sinkhorn CDF and Quantile Operator
    Marco Cuturi, Olivier Teboul, Jean-Philippe Vert
    http://arxiv.org/abs/1905.11885v1

    • [cs.LG]Distributed estimation of the inverse Hessian by determinantal averaging
    Michał Dereziński, Michael W. Mahoney
    http://arxiv.org/abs/1905.11546v1

    • [cs.LG]Divide-and-Conquer Adversarial Detection
    Xuwang Yin, Soheil Kolouri, Gustavo K. Rohde
    http://arxiv.org/abs/1905.11475v1

    • [cs.LG]Dynamic Nonparametric Edge-Clustering Model for Time-Evolving Sparse Networks
    Elahe Ghalebi, Hamidreza Mahyar, Radu Grosu, Sinead Williamson
    http://arxiv.org/abs/1905.11724v1

    • [cs.LG]EDUCE: Explaining model Decisions through Unsupervised Concepts Extraction
    Diane Bouchacourt, Ludovic Denoyer
    http://arxiv.org/abs/1905.11852v1

    • [cs.LG]Efficient Wrapper Feature Selection using Autoencoder and Model Based Elimination
    Sharan Ramjee, Aly El Gamal
    http://arxiv.org/abs/1905.11592v1

    • [cs.LG]EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks
    Mingxing Tan, Quoc V. Le
    http://arxiv.org/abs/1905.11946v1

    • [cs.LG]Equivalent and Approximate Transformations of Deep Neural Networks
    Abhinav Kumar, Thiago Serra, Srikumar Ramalingam
    http://arxiv.org/abs/1905.11428v1

    • [cs.LG]Evaluating and Calibrating Uncertainty Prediction in Regression Tasks
    Dan Levi, Liran Gispan, Niv Giladi, Ethan Fetaya
    http://arxiv.org/abs/1905.11659v1

    • [cs.LG]Evaluating time series forecasting models: An empirical study on performance estimation methods
    Vitor Cerqueira, Luis Torgo, Igor Mozetic
    http://arxiv.org/abs/1905.11744v1

    • [cs.LG]FAN: Focused Attention Networks
    Chu Wang, Babak Samari, Vladimir Kim, Siddhartha Chaudhuri, Kaleem Siddiqi
    http://arxiv.org/abs/1905.11498v1

    • [cs.LG]Forecasting Stock Market with Support Vector Regression and Butterfly Optimization Algorithm
    Mohammadreza Ghanbari, Hamidreza Arian
    http://arxiv.org/abs/1905.11462v1

    • [cs.LG]Generalization Bounds in the Predict-then-Optimize Framework
    Othman El Balghiti, Adam N. Elmachtoub, Paul Grigas, Ambuj Tewari
    http://arxiv.org/abs/1905.11488v1

    • [cs.LG]Greedy InfoMax for Biologically Plausible Self-Supervised Representation Learning
    Sindy Löwe, Peter O’Connor, Bastiaan S. Veeling
    http://arxiv.org/abs/1905.11786v1

    • [cs.LG]Importance of user inputs while using incremental learning to personalize human activity recognition models
    Pekka Siirtola, Heli Koskimäki, Juha Röning
    http://arxiv.org/abs/1905.11775v1

    • [cs.LG]Improved Training Speed, Accuracy, and Data Utilization Through Loss Function Optimization
    Santiago Gonzalez, Risto Miikkulainen
    http://arxiv.org/abs/1905.11528v1

    • [cs.LG]Improving the Robustness of Deep Neural Networks via Adversarial Training with Triplet Loss
    Pengcheng Li, Jinfeng Yi, Bowen Zhou, Lijun Zhang
    http://arxiv.org/abs/1905.11713v1

    • [cs.LG]Incidence Networks for Geometric Deep Learning
    Marjan Albooyeh, Daniele Bertolini, Siamak Ravanbakhsh
    http://arxiv.org/abs/1905.11460v1

    • [cs.LG]Interactive Teaching Algorithms for Inverse Reinforcement Learning
    Parameswaran Kamalaruban, Rati Devidze, Volkan Cevher, Adish Singla
    http://arxiv.org/abs/1905.11867v1

    • [cs.LG]LambdaOpt: Learn to Regularize Recommender Models in Finer Levels
    Yihong Chen, Bei Chen, Xiangnan He, Chen Gao, Yong Li, Jian-Guang Lou, Yue Wang
    http://arxiv.org/abs/1905.11596v1

    • [cs.LG]Learning Efficient and Effective Exploration Policies with Counterfactual Meta Policy
    Ruihan Yang, Qiwei Ye, Tie-Yan Liu
    http://arxiv.org/abs/1905.11583v1

    • [cs.LG]Learning In Practice: Reasoning About Quantization
    Annie Cherkaev, Waiming Tai, Jeff Phillips, Vivek Srikumar
    http://arxiv.org/abs/1905.11478v1

    • [cs.LG]ME-Net: Towards Effective Adversarial Robustness with Matrix Estimation
    Yuzhe Yang, Guo Zhang, Dina Katabi, Zhi Xu
    http://arxiv.org/abs/1905.11971v1

    • [cs.LG]Machine Learning on data with sPlot background subtraction
    Maxim Borisyak, Nikita Kazeev
    http://arxiv.org/abs/1905.11719v1

    • [cs.LG]Model-Agnostic Counterfactual Explanations for Consequential Decisions
    Amir-Hossein Karimi, Gilles Barthe, Borja Balle, Isabel Valera
    http://arxiv.org/abs/1905.11190v2

    • [cs.LG]Multi-Modal Graph Interaction for Multi-Graph Convolution Network in Urban Spatiotemporal Forecasting
    Xu Geng, Xiyu Wu, Lingyu Zhang, Qiang Yang, Yan Liu, Jieping Ye
    http://arxiv.org/abs/1905.11395v1

    • [cs.LG]Network Deconvolution
    Chengxi Ye, Matthew Evanusa, Hua He, Anton Mitrokhin, Thomas Goldstein, James A. Yorke, Cornelia Fermüller, Yiannis Aloimonos
    http://arxiv.org/abs/1905.11926v1

    • [cs.LG]On Dropout and Nuclear Norm Regularization
    Poorya Mianjy, Raman Arora
    http://arxiv.org/abs/1905.11887v1

    • [cs.LG]OrderNet: Ordering by Example
    Robert Porter
    http://arxiv.org/abs/1905.11536v1

    • [cs.LG]Overlearning Reveals Sensitive Attributes
    Congzheng Song, Vitaly Shmatikov
    http://arxiv.org/abs/1905.11742v1

    • [cs.LG]Polynomial Tensor Sketch for Element-wise Function of Low-Rank Matrix
    Insu Han, Haim Avron, Jinwoo Shin
    http://arxiv.org/abs/1905.11616v1

    • [cs.LG]Radar-based Road User Classification and Novelty Detection with Recurrent Neural Network Ensembles
    Nicolas Scheiner, Nils Appenrodt, Jürgen Dickmann, Bernhard Sick
    http://arxiv.org/abs/1905.11703v1

    • [cs.LG]Rare Failure Prediction via Event Matching for Aerospace Applications
    Evgeny Burnaev
    http://arxiv.org/abs/1905.11586v1

    • [cs.LG]RecNets: Channel-wise Recurrent Convolutional Neural Networks
    George Retsinas, Athena Elafrou, Georgios Goumas, Petros Maragos
    http://arxiv.org/abs/1905.11910v1

    • [cs.LG]Regression via Kirszbraun Extension with Applications to Imitation Learning
    Armin Biess, Aryeh Kontorovich, Yury Makarychev, Hanan Zaichyk
    http://arxiv.org/abs/1905.11930v1

    • [cs.LG]Relational Representation Learning for Dynamic (Knowledge) Graphs: A Survey
    Seyed Mehran Kazemi, Rishab Goel, Kshitij Jain, Ivan Kobyzev, Akshay Sethi, Peter Forsyth, Pascal Poupart
    http://arxiv.org/abs/1905.11485v1

    • [cs.LG]Repeated A/B Testing
    Nicolò Cesa-Bianchi, Tommaso R. Cesari, Yishay Mansour, Vianney Perchet
    http://arxiv.org/abs/1905.11797v1

    • [cs.LG]SGD on Neural Networks Learns Functions of Increasing Complexity
    Preetum Nakkiran, Gal Kaplun, Dimitris Kalimeris, Tristan Yang, Benjamin L. Edelman, Fred Zhang, Boaz Barak
    http://arxiv.org/abs/1905.11604v1

    • [cs.LG]Single-Net Continual Learning with Progressive Segmented Training (PST)
    Xiaocong Du, Gouranga Charan, Frank Liu, Yu Cao
    http://arxiv.org/abs/1905.11550v1

    • [cs.LG]Sketch-based Randomized Algorithms for Dynamic Graph Regression
    Mostafa Haghir Chehreghani
    http://arxiv.org/abs/1905.11963v1

    • [cs.LG]Snooping Attacks on Deep Reinforcement Learning
    Matthew Inkawhich, Yiran Chen, Hai Li
    http://arxiv.org/abs/1905.11832v1

    • [cs.LG]Solving NP-Hard Problems on Graphs by Reinforcement Learning without Domain Knowledge
    Kenshin Abe, Zijian Xu, Issei Sato, Masashi Sugiyama
    http://arxiv.org/abs/1905.11623v1

    • [cs.LG]Structure Learning for Neural Module Networks
    Vardaan Pahuja, Jie Fu, Sarath Chandar, Christopher J. Pal
    http://arxiv.org/abs/1905.11532v1

    • [cs.LG]Supervised Discriminative Sparse PCA for Com-Characteristic Gene Selection and Tumor Classification on Multiview Biological Data
    Chun-Mei Feng, Yong Xu, Jin-Xing Liu, Ying-Lian Gao, Chun-Hou Zheng
    http://arxiv.org/abs/1905.11837v1

    • [cs.LG]Tight Regret Bounds for Model-Based Reinforcement Learning with Greedy Policies
    Yonathan Efroni, Nadav Merlis, Mohammad Ghavamzadeh, Shie Mannor
    http://arxiv.org/abs/1905.11527v1

    • [cs.LG]Towards Interpretable Sparse Graph Representation Learning with Laplacian Pooling
    Emmanuel Noutahi, Dominique Beani, Julien Horwood, Prudencio Tossou
    http://arxiv.org/abs/1905.11577v1

    • [cs.LG]Uncertainty-based Continual Learning with Adaptive Regularization
    Hongjoon Ahn, Donggyu Lee, Sungmin Cha, Taesup Moon
    http://arxiv.org/abs/1905.11614v1

    • [cs.LG]Universality Theorems for Generative Models
    Valentin Khrulkov, Ivan Oseledets
    http://arxiv.org/abs/1905.11520v1

    • [cs.LG]Validating the Validation: Reanalyzing a large-scale comparison of Deep Learning and Machine Learning models for bioactivity prediction
    Matthew C. Robinson, Robert C. Glen, Alpha A. Lee
    http://arxiv.org/abs/1905.11681v1

    • [cs.LG]Variational Information Bottleneck for Unsupervised Clustering: Deep Gaussian Mixture Embedding
    Yigit Ugur, George Arvanitakis, Abdellatif Zaidi
    http://arxiv.org/abs/1905.11741v1

    • [cs.LG]When can unlabeled data improve the learning rate?
    Christina Göpfert, Shai Ben-David, Olivier Bousquet, Sylvain Gelly, Ilya Tolstikhin, Ruth Urner
    http://arxiv.org/abs/1905.11866v1

    • [cs.LO]NIL: Learning Nonlinear Interpolants
    Mingshuai Chen, Jian Wang, Jie An, Bohua Zhan, Deepak Kapur, Naijun Zhan
    http://arxiv.org/abs/1905.11625v1

    • [cs.MA]A Parameterized Perspective on Protecting Elections
    Palash Dey, Neeldhara Misra, Swaprava Nath, Garima Shakya
    http://arxiv.org/abs/1905.11838v1

    • [cs.NE]Efficient Network Construction through Structural Plasticity
    Xiaocong Du, Zheng Li, Yufei Ma, Yu Cao
    http://arxiv.org/abs/1905.11530v1

    • [cs.NE]Inference with Hybrid Bio-hardware Neural Networks
    Yuan Zeng, Zubayer Ibne Ferdous, Weixiang Zhang, Mufan Xu, Anlan Yu, Drew Patel, Xiaochen Guo, Yevgeny Berdichevsky, Zhiyuan Yan
    http://arxiv.org/abs/1905.11594v1

    • [cs.NE]Multi-Sample Dropout for Accelerated Training and Better Generalization
    Hiroshi Inoue
    http://arxiv.org/abs/1905.09788v2

    • [cs.PF]Function-as-a-Service Benchmarking Framework
    Roland Pellegrini, Igor Ivkic, Markus Tauber
    http://arxiv.org/abs/1905.11707v1

    • [cs.RO]Autonomous skill discovery with Quality-Diversity and Unsupervised Descriptors
    Antoine Cully
    http://arxiv.org/abs/1905.11874v1

    • [cs.RO]Fast human motion prediction for human-robot collaboration with wearable interfaces
    Stefano Tortora, Stefano Michieletto, Francesca Stival, Emanuele Menegatti
    http://arxiv.org/abs/1905.11734v1

    • [cs.RO]Mechanism Singularities Revisited from an Algebraic Viewpoint
    Zijia Li, Andreas Müller
    http://arxiv.org/abs/1905.11789v1

    • [cs.RO]Next-Generation Inertial Navigation Computation Based on Functional Iteration
    Yuanxin Wu
    http://arxiv.org/abs/1905.11615v1

    • [cs.RO]Robotic bees: Algorithms for collision detection and prevention
    Vincent Arcila, Isabel Piedrahita
    http://arxiv.org/abs/1905.11822v1

    • [cs.SD]Texture Selection for Automatic Music Genre Classification
    Juliano H. Foleiss, Tiago F. Tavares
    http://arxiv.org/abs/1905.11959v1

    • [cs.SD]Two-level Explanations in Music Emotion Recognition
    Verena Haunschmid, Shreyan Chowdhury, Gerhard Widmer
    http://arxiv.org/abs/1905.11760v1

    • [cs.SI]Adaptive Influence Maximization with Myopic Feedback
    Binghui Peng, Wei Chen
    http://arxiv.org/abs/1905.11663v1

    • [cs.SI]The HyperBagGraph DataEdron: An Enriched Browsing Experience of Multimedia Datasets
    Xavier Ouvrard, Jean-Marie Le Goff, Stéphane Marchand-Maillet
    http://arxiv.org/abs/1905.11695v1

    • [eess.AS]SignalTrain: Profiling Audio Compressors with Deep Neural Networks
    Scott H. Hawley, Benjamin Colburn, Stylianos I. Mimilakis
    http://arxiv.org/abs/1905.11928v1

    • [eess.IV]A Compact Representation of Histopathology Images using Digital Stain Separation & Frequency-Based Encoded Local Projections
    Alison K. Cheeseman, Hamid Tizhoosh, Edward R. Vrscay
    http://arxiv.org/abs/1905.11945v1

    • [eess.IV]Adaptive Lighting for Data-Driven Non-Line-of-Sight 3D Localization and Object Identification
    Sreenithy Chandran, Suren Jayasuriya
    http://arxiv.org/abs/1905.11595v1

    • [eess.SP]Distributed Antenna Selection for Massive MIMO using Reversing Petri Nets
    Harun Siljak, Kyriaki Psara, Anna Philippou
    http://arxiv.org/abs/1905.11932v1

    • [eess.SP]Multiuser MISO UAV Communications in Uncertain Environments with No-fly Zones: Robust Trajectory and Resource Allocation Design
    Dongfang Xu, Yan Sun, Derrick Wing Kwan Ng, Robert Schober
    http://arxiv.org/abs/1905.10731v2

    • [eess.SP]Towards Practical Indoor Positioning Based on Massive MIMO Systems
    Mark Widmaier, Maximilian Arnold, Sebastian Dörner, Sebastian Cammerer, Stephan ten Brink
    http://arxiv.org/abs/1905.11858v1

    • [math.OC]An Accelerated Decentralized Stochastic Proximal Algorithm for Finite Sums
    Hadrien Hendrikx, Francis Bach, Laurent Massoulie
    http://arxiv.org/abs/1905.11394v1

    • [math.OC]Analysis of Gradient Clipping and Adaptive Scaling with a Relaxed Smoothness Condition
    Jingzhao Zhang, Tianxing He, Suvrit Sra, Ali Jadbabaie
    http://arxiv.org/abs/1905.11881v1

    • [math.OC]Concavifiability and convergence: necessary and sufficient conditions for gradient descent analysis
    Thulasi Tholeti, Sheetal Kalyani
    http://arxiv.org/abs/1905.11620v1

    • [math.OC]Direct Nonlinear Acceleration
    Aritra Dutta, El Houcine Bergou, Yunming Xiao, Marco Canini, Peter Richtárik
    http://arxiv.org/abs/1905.11692v1

    • [math.OC]Finite-Time Analysis of Q-Learning with Linear Function Approximation
    Zaiwei Chen, Sheng Zhang, Thinh T. Doan, Siva Theja Maguluri, John-Paul Clarke
    http://arxiv.org/abs/1905.11425v1

    • [math.OC]Sample Complexity of Sample Average Approximation for Conditional Stochastic Optimization
    Yifan Hu, Xin Chen, Niao He
    http://arxiv.org/abs/1905.11957v1

    • [math.ST]A Projected Non-Linear Conjugate Gradient Algorithm for Destructive Negative Binomial Cure Rate Model
    Suvra Pal, Souvik Roy
    http://arxiv.org/abs/1905.11379v1

    • [math.ST]Large Sample Properties of Matching for Balance
    Yixin Wang, José R. Zubizarreta
    http://arxiv.org/abs/1905.11386v1

    • [math.ST]On the identification of individual principal stratum direct, natural direct and pleiotropic effects without cross-world independence assumptions
    Jaffer M. Zaidi, Tyler J. VanderWeele
    http://arxiv.org/abs/1905.11434v1

    • [math.ST]Probabilistic mappings and Bayesian nonparametrics
    Jürgen Jost, Hông Vân Lê, Duc Hoang Luu, Tat Dat Tran
    http://arxiv.org/abs/1905.11448v1

    • [math.ST]Statistical bounds for entropic optimal transport: sample complexity and the central limit theorem
    Gonzalo Mena, Jonathan Weed
    http://arxiv.org/abs/1905.11882v1

    • [math.ST]The bias of the sample mean in multi-armed bandits can be positive or negative
    Jaehyeok Shin, Aaditya Ramdas, Alessandro Rinaldo
    http://arxiv.org/abs/1905.11397v1

    • [physics.comp-ph]AI Feynman: a Physics-Inspired Method for Symbolic Regression
    Silviu-Marian Udrescu, Max Tegmark
    http://arxiv.org/abs/1905.11481v1

    • [physics.ins-det]Fast Data-Driven Simulation of Cherenkov Detectors Using Generative Adversarial Networks
    Artem Maevskiy, Denis Derkach, Nikita Kazeev, Andrey Ustyuzhanin, Maksim Artemev, Lucio Anderlini
    http://arxiv.org/abs/1905.11825v1

    • [quant-ph]Resource theory of asymmetric distinguishability
    Xin Wang, Mark M. Wilde
    http://arxiv.org/abs/1905.11629v1

    • [stat.AP]Evaluation of mineralogy per geological layers by Approximate Bayesian Computation
    Vianney Bruned, Alice Cleynen, André Mas, Sylvain Wlodarczyck
    http://arxiv.org/abs/1905.11779v1

    • [stat.ME]ADDIS: adaptive algorithms for online FDR control with conservative nulls
    Jinjin Tian, Aaditya Ramdas
    http://arxiv.org/abs/1905.11465v1

    • [stat.ME]Can we disregard the whole model? Omnibus non-inferiority testing for $R^{2}$ in multivariable linear regression and $\hatη^{2}$ in ANOVA
    Harlan Campbell, Daniël Lakens
    http://arxiv.org/abs/1905.11875v1

    • [stat.ME]Estimating Average Treatment Effects Utilizing Fractional Imputation when Confounders are Subject to Missingness
    Nathan Corder, Shu Yang
    http://arxiv.org/abs/1905.11497v1

    • [stat.ME]Intervention in undirected Ising graphs and the partition function
    Lourens Waldorp, Maarten Marsman
    http://arxiv.org/abs/1905.11502v1

    • [stat.ME]Kalman Filter, Sensor Fusion, and Constrained Regression: Equivalences and Insights
    David C. Farrow, Maria Jahja, Roni Rosenfeld, Ryan J. Tibshirani
    http://arxiv.org/abs/1905.11436v1

    • [stat.ME]Robust Nonparametric Difference-in-Differences Estimation
    Chen Lu, Xinkun Nie, Stefan Wager
    http://arxiv.org/abs/1905.11622v1

    • [stat.ME]Sparse Estimation of Historical Functional Linear Models with a Nested Group Bridge Approach
    Xiaolei Xun, Jiguo Cao
    http://arxiv.org/abs/1905.11676v1

    • [stat.ME]Tuning Free Rank-Sparse Bayesian Matrix and Tensor Completion with Global-Local Priors
    Daniel E. Gilbert, Martin T. Wells
    http://arxiv.org/abs/1905.11496v1

    • [stat.ME]Validation of Approximate Likelihood and Emulator Models for Computationally Intensive Simulations
    Niccolò Dalmasso, Ann B. Lee, Rafael Izbicki, Taylor Pospisil, Chieh-An Lin
    http://arxiv.org/abs/1905.11505v1

    • [stat.ML]Ancestral causal learning in high dimensions with a human genome-wide application
    Umberto Noè, Bernd Taschler, Joachim Täger, Peter Heutink, Sach Mukherjee
    http://arxiv.org/abs/1905.11506v1

    • [stat.ML]Anomaly scores for generative models
    Václav Šmídl, Jan Bím, Tomáš Pevný
    http://arxiv.org/abs/1905.11890v1

    • [stat.ML]Discrete Infomax Codes for Meta-Learning
    Yoonho Lee, Wonjae Kim, Seungjin Choi
    http://arxiv.org/abs/1905.11656v1

    • [stat.ML]Distributed Linear Model Clustering over Networks: A Tree-Based Fused-Lasso ADMM Approach
    Xin Zhang, Jia Liu, Zhengyuan Zhu
    http://arxiv.org/abs/1905.11549v1

    • [stat.ML]Estimating and Inferring the Maximum Degree of Stimulus-Locked Time-Varying Brain Connectivity Networks
    Kean Ming Tan, Junwei Lu, Tong Zhang, Han Liu
    http://arxiv.org/abs/1905.11588v1

    • [stat.ML]Global forensic geolocation with deep neural networks
    Neal S. Grantham, Brian J. Reich, Eric B. Laber, Krishna Pacifici, Robert R. Dunn, Noah Fierer, Matthew Gebert, Julia S. Allwood, Seth A. Faith
    http://arxiv.org/abs/1905.11765v1

    • [stat.ML]GraphNVP: An Invertible Flow Model for Generating Molecular Graphs
    Kaushalya Madhawa, Katushiko Ishiguro, Kosuke Nakago, Motoki Abe
    http://arxiv.org/abs/1905.11600v1

    • [stat.ML]Learning Bregman Divergences
    Ali Siahkamari, Venkatesh Saligrama, David Castanon, Brian Kulis
    http://arxiv.org/abs/1905.11545v1

    • [stat.ML]Learning Dynamics of Attention: Human Prior for Interpretable Machine Reasoning
    Wonjae Kim, Yoonho Lee
    http://arxiv.org/abs/1905.11666v1

    • [stat.ML]Learning distant cause and effect using only local and immediate credit assignment
    David Rawlinson, Abdelrahman Ahmed, Gideon Kowadlo
    http://arxiv.org/abs/1905.11589v1

    • [stat.ML]Quantifying the generalization error in deep learning in terms of data distribution and neural network smoothness
    Pengzhan Jin, Lu Lu, Yifa Tang, George Em Karniadakis
    http://arxiv.org/abs/1905.11427v1

    • [stat.ML]Recursive Estimation for Sparse Gaussian Process Regression
    Manuel Schürch, Dario Azzimonti, Alessio Benavoli, Marco Zaffalon
    http://arxiv.org/abs/1905.11711v1

    • [stat.ML]Robustness Quantification for Classification with Gaussian Processes
    Arno Blaas, Luca Laurenti, Andrea Patane, Luca Cardelli, Marta Kwiatkowska, Stephen Roberts
    http://arxiv.org/abs/1905.11876v1

    • [stat.ML]Scaleable input gradient regularization for adversarial robustness
    Chris Finlay, Adam M Oberman
    http://arxiv.org/abs/1905.11468v1

    • [stat.ML]Stochastic Proximal Langevin Algorithm: Potential Splitting and Nonasymptotic Rates
    Adil Salim, Dmitry Kovalev, Peter Richtárik
    http://arxiv.org/abs/1905.11768v1

    • [stat.ML]The Hierarchy of Stable Distributions and Operators to Trade Off Stability and Performance
    Adarsh Subbaswamy, Bryant Chen, Suchi Saria
    http://arxiv.org/abs/1905.11374v2

    • [stat.ML]Understanding the Behaviour of the Empirical Cross-Entropy Beyond the Training Distribution
    Matias Vera, Pablo Piantanida, Leonardo Rey Vega
    http://arxiv.org/abs/1905.11972v1