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
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• [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