cs.AI - 人工智能

    cs.CG - 计算几何学 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.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.NI - 网络和互联网体系结构 cs.OH - 其他CS cs.RO - 机器人学 cs.SD - 声音处理 cs.SE - 软件工程 cs.SI - 社交网络与信息网络 cs.SY - 系统与控制 eess.IV - 图像与视频处理 eess.SP - 信号处理 math.NA - 数值分析 math.OC - 优化与控制 math.ST - 统计理论 physics.geo-ph - 地球物理学 physics.soc-ph - 物理学与社会 stat.CO - 统计计算 stat.ME - 统计方法论 stat.ML - (统计)机器学习

    • [cs.AI]Better Future through AI: Avoiding Pitfalls and Guiding AI Towards its Full Potential
    • [cs.AI]Constructing High Precision Knowledge Bases with Subjective and Factual Attributes
    • [cs.AI]Learning Compositional Neural Programs with Recursive Tree Search and Planning
    • [cs.AI]Neural Consciousness Flow
    • [cs.AI]Quantifying consensus of rankings based on q-support patterns
    • [cs.AI]Unpredictability of AI
    • [cs.AI]Using Restart Heuristics to Improve Agent Performance in Angry Birds
    • [cs.CG]Persistent homology detects curvature
    • [cs.CL]A Compare-Aggregate Model with Latent Clustering for Answer Selection
    • [cs.CL]A Simple but Effective Method to Incorporate Multi-turn Context with BERT for Conversational Machine Comprehension
    • [cs.CL]Choosing Transfer Languages for Cross-Lingual Learning
    • [cs.CL]Controllable Unsupervised Text Attribute Transfer via Editing Entangled Latent Representation
    • [cs.CL]Geolocating Political Events in Text
    • [cs.CL]Hierarchical Transformers for Multi-Document Summarization
    • [cs.CL]Large Scale Question Paraphrase Retrieval with Smoothed Deep Metric Learning
    • [cs.CL]Lattice-based lightly-supervised acoustic model training
    • [cs.CL]M-GWAP: An Online and Multimodal Game With A Purpose in WordPress for Mental States Annotation
    • [cs.CL]Reducing Gender Bias in Word-Level Language Models with a Gender-Equalizing Loss Function
    • [cs.CL]Semantically Conditioned Dialog Response Generation via Hierarchical Disentangled Self-Attention
    • [cs.CL]The (Non-)Utility of Structural Features in BiLSTM-based Dependency Parsers
    • [cs.CL]Unbabel’s Submission to the WMT2019 APE Shared Task: BERT-based Encoder-Decoder for Automatic Post-Editing
    • [cs.CR]Automatically Dismantling Online Dating Fraud
    • [cs.CV]$d$-SNE: Domain Adaptation using Stochastic Neighborhood Embedding
    • [cs.CV]A Trainable Multiplication Layer for Auto-correlation and Co-occurrence Extraction
    • [cs.CV]A survey of Object Classification and Detection based on 2D/3D data
    • [cs.CV]AssembleNet: Searching for Multi-Stream Neural Connectivity in Video Architectures
    • [cs.CV]Attention: A Big Surprise for Cross-Domain Person Re-Identification
    • [cs.CV]Deep Learning Approach for Receipt Recognition
    • [cs.CV]Distant Pedestrian Detection in the Wild using Single Shot Detector with Deep Convolutional Generative Adversarial Networks
    • [cs.CV]Does computer vision matter for action?
    • [cs.CV]Dynamic Traffic Scene Classification with Space-Time Coherence
    • [cs.CV]Emergence of Object Segmentation in Perturbed Generative Models
    • [cs.CV]Entropic Regularisation of Robust Optimal Transport
    • [cs.CV]Extending Monocular Visual Odometry to Stereo Camera System by Scale Optimization
    • [cs.CV]GlyphGAN: Style-Consistent Font Generation Based on Generative Adversarial Networks
    • [cs.CV]Hierarchical Structure and Joint Training for Large Scale Semi-supervised Object Detection
    • [cs.CV]Interactive-predictive neural multimodal systems
    • [cs.CV]Learning Semantics-aware Distance Map with Semantics Layering Network for Amodal Instance Segmentation
    • [cs.CV]On Network Design Spaces for Visual Recognition
    • [cs.CV]P3SGD: Patient Privacy Preserving SGD for Regularizing Deep CNNs in Pathological Image Classification
    • [cs.CV]RoNIN: Robust Neural Inertial Navigation in the Wild: Benchmark, Evaluations, and New Methods
    • [cs.CV]The Fashion IQ Dataset: Retrieving Images by Combining Side Information and Relative Natural Language Feedback
    • [cs.CV]The General Pair-based Weighting Loss for Deep Metric Learning
    • [cs.CV]Towards Photo-Realistic Visible Watermark Removal with Conditional Generative Adversarial Networks
    • [cs.CV]Unsupervised Classification of Street Architectures Based on InfoGAN
    • [cs.CV]What Makes Training Multi-Modal Networks Hard?
    • [cs.CV]iSAID: A Large-scale Dataset for Instance Segmentation in Aerial Images
    • [cs.CY]The Security Implications of Data Subject Rights
    • [cs.DC]Evaluation of pilot jobs for Apache Spark applications on HPC clusters
    • [cs.DC]MCompiler: A Synergistic Compilation Framework
    • [cs.DC]The Bloom Clock
    • [cs.DC]Workflow Management on BFT Blockchains
    • [cs.DL]Exploring the Effects of Data Set Choice on Measuring International Research Collaboration: an Example Using the ACM Digital Library and Microsoft Academic Graph
    • [cs.DL]Social Cards Probably Provide For Better Understanding Of Web Archive Collections
    • [cs.ET]Nonvolatile Spintronic Memory Cells for Neural Networks
    • [cs.GT]Heuristics in Multi-Winner Approval Voting
    • [cs.HC]Visualizing a Moving Target: A Design Study on Task Parallel Programs in the Presence of Evolving Data and Concerns
    • [cs.IR]Collaborative Self-Attention for Recommender Systems
    • [cs.IR]Deep Adversarial Social Recommendation
    • [cs.IR]Deep Cross Networks with Aesthetic Preference for Cross-domain Recommendation
    • [cs.IR]Explainable Fashion Recommendation: A Semantic Attribute Region Guided Approach
    • [cs.IR]FairSearch: A Tool For Fairness in Ranked Search Results
    • [cs.IR]Job Recommendation through Progression of Job Selection
    • [cs.IR]On the Effectiveness of Low-rank Approximations for Collaborative Filtering compared to Neural Networks
    • [cs.IR]SAIN: Self-Attentive Integration Network for Recommendation
    • [cs.IR]STAR-GCN: Stacked and Reconstructed Graph Convolutional Networks for Recommender Systems
    • [cs.IT]A New Approach to Lossy Network Compression of a Tuple of Correlated Multivariate Gaussian RVs
    • [cs.IT]A Novel Time-Based Modulation Scheme in Time-Asynchronous Channels for Molecular Communications
    • [cs.IT]Deep Learning for Direct Hybrid Precoding in Millimeter Wave Massive MIMO Systems
    • [cs.IT]Neural Entropic Estimation: A faster path to mutual information estimation
    • [cs.IT]On Multiple-Access Systems with Queue-Length Dependent Service Quality
    • [cs.IT]Partially APN Boolean functions and classes of functions that are not APN infinitely often
    • [cs.IT]Skew constacyclic codes over a non-chain ring $\mathbb{F}_{q}[u,v]/\langle f(u),g(v), uv-vu\rangle$
    • [cs.LG]A General Optimization Framework for Dynamic Time Warping
    • [cs.LG]A Generalized Framework of Sequence Generation with Application to Undirected Sequence Models
    • [cs.LG]Active Learning in the Overparameterized and Interpolating Regime
    • [cs.LG]Adversarial Imitation Learning from Incomplete Demonstrations
    • [cs.LG]Adversarial Sub-sequence for Text Generation
    • [cs.LG]AlignFlow: Cycle Consistent Learning from Multiple Domains via Normalizing Flows
    • [cs.LG]An adaptive nearest neighbor rule for classification
    • [cs.LG]Bandlimiting Neural Networks Against Adversarial Attacks
    • [cs.LG]Batch weight for domain adaptation with mass shift
    • [cs.LG]Beyond Online Balanced Descent: An Optimal Algorithm for Smoothed Online Optimization
    • [cs.LG]Confirmatory Bayesian Online Change Point Detection in the Covariance Structure of Gaussian Processes
    • [cs.LG]Convergence of Distributed Stochastic Variance Reduced Methods without Sampling Extra Data
    • [cs.LG]Cross-modal Variational Auto-encoder with Distributed Latent Spaces and Associators
    • [cs.LG]Data-Dependent Differentially Private Parameter Learning for Directed Graphical Models
    • [cs.LG]Deep ensemble learning for Alzheimers disease classification
    • [cs.LG]Deep multi-class learning from label proportions
    • [cs.LG]Defining Admissible Rewards for High Confidence Policy Evaluation
    • [cs.LG]Diffusion Variational Autoencoders
    • [cs.LG]Distribution-dependent and Time-uniform Bounds for Piecewise i.i.d Bandits
    • [cs.LG]Effective Medical Test Suggestions Using Deep Reinforcement Learning
    • [cs.LG]Equipping Experts/Bandits with Long-term Memory
    • [cs.LG]Evaluating structure learning algorithms with a balanced scoring function
    • [cs.LG]Exploiting Uncertainty of Loss Landscape for Stochastic Optimization
    • [cs.LG]Fair Regression: Quantitative Definitions and Reduction-based Algorithms
    • [cs.LG]Fairness and Missing Values
    • [cs.LG]Finite-time Analysis of Approximate Policy Iteration for the Linear Quadratic Regulator
    • [cs.LG]Function approximation by deep networks
    • [cs.LG]Generalization Bounds of Stochastic Gradient Descent for Wide and Deep Neural Networks
    • [cs.LG]Generalized Separable Nonnegative Matrix Factorization
    • [cs.LG]Generating Contrastive Explanations with Monotonic Attribute Functions
    • [cs.LG]Graph Learning Network: A Structure Learning Algorithm
    • [cs.LG]Graph Neural Tangent Kernel: Fusing Graph Neural Networks with Graph Kernels
    • [cs.LG]Graph Normalizing Flows
    • [cs.LG]ImJoy: an open-source computational platform for the deep learning era
    • [cs.LG]Imitation Learning as $f$-Divergence Minimization
    • [cs.LG]Implicit Regularization of Accelerated Methods in Hilbert Spaces
    • [cs.LG]Information theoretic learning of robust deep representations
    • [cs.LG]Interpretable Adversarial Training for Text
    • [cs.LG]Intrinsic dimension of data representations in deep neural networks
    • [cs.LG]Latent Space Secrets of Denoising Text-Autoencoders
    • [cs.LG]Learning Nonsymmetric Determinantal Point Processes
    • [cs.LG]Learning Representations by Humans, for Humans
    • [cs.LG]Learning by Active Nonlinear Diffusion
    • [cs.LG]Learning to Balance: Bayesian Meta-Learning for Imbalanced and Out-of-distribution Tasks
    • [cs.LG]Learning to Crawl
    • [cs.LG]Less is More: An Exploration of Data Redundancy with Active Dataset Subsampling
    • [cs.LG]Matrix Completion in the Unit Hypercube via Structured Matrix Factorization
    • [cs.LG]Matrix-Free Preconditioning in Online Learning
    • [cs.LG]Memory-Driven Mixed Low Precision Quantization For Enabling Deep Network Inference On Microcontrollers
    • [cs.LG]Meta Dropout: Learning to Perturb Features for Generalization
    • [cs.LG]Meta-Surrogate Benchmarking for Hyperparameter Optimization
    • [cs.LG]Multi-Objective Generalized Linear Bandits
    • [cs.LG]Near-Term Quantum-Classical Associative Adversarial Networks
    • [cs.LG]On the Generalization Gap in Reparameterizable Reinforcement Learning
    • [cs.LG]One-element Batch Training by Moving Window
    • [cs.LG]Particle Filter Recurrent Neural Networks
    • [cs.LG]Path-Augmented Graph Transformer Network
    • [cs.LG]Provably Efficient Q-Learning with Low Switching Cost
    • [cs.LG]Quantifying the alignment of graph and features in deep learning
    • [cs.LG]Recursive Sketches for Modular Deep Learning
    • [cs.LG]Regression with Conditional GAN
    • [cs.LG]Regret Bounds for Thompson Sampling in Restless Bandit Problems
    • [cs.LG]Reinforcement Learning and Adaptive Sampling for Optimized DNN Compilation
    • [cs.LG]Reinforcement Learning for Slate-based Recommender Systems: A Tractable Decomposition and Practical Methodology
    • [cs.LG]Securing Connected & Autonomous Vehicles: Challenges Posed by Adversarial Machine Learning and The Way Forward
    • [cs.LG]Sequential no-Substitution k-Median-Clustering
    • [cs.LG]Structured Monte Carlo Sampling for Nonisotropic Distributions via Determinantal Point Processes
    • [cs.LG]Toward Runtime-Throttleable Neural Networks
    • [cs.LG]Training Generative Adversarial Networks from Incomplete Observations using Factorised Discriminators
    • [cs.LG]Understanding Goal-Oriented Active Learning via Influence Functions
    • [cs.LG]Unsupervised Paraphrasing without Translation
    • [cs.LG]Unsupervised pre-training helps to conserve views from input distribution
    • [cs.LG]Using Text Embeddings for Causal Inference
    • [cs.LG]Wasserstein Style Transfer
    • [cs.LG]What Can Neural Networks Reason About?
    • [cs.LO]Data Complexity and Rewritability of Ontology-Mediated Queries in Metric Temporal Logic under the Event-Based Semantics (Full Version)
    • [cs.LO]Towards Finding Longer Proofs
    • [cs.MA]Ridesharing with Driver Location Preferences
    • [cs.NE]A Hippocampus Model for Online One-Shot Storage of Pattern Sequences
    • [cs.NE]Spatial Evolutionary Generative Adversarial Networks
    • [cs.NI]Standing on the Shoulders of Giants: AI-driven Calibration of Localisation Technologies
    • [cs.OH]Definitively Identifying an Inherent Limitation to Actual Cognition
    • [cs.RO]A Realtime Autonomous Robot Navigation Framework for Human like High-level Interaction and Task Planning in Global Dynamic Environment
    • [cs.RO]Assistive robot operated via P300-based Brain Computer Interface
    • [cs.RO]Bayesian Grasp: Robotic visual stable grasp based on prior tactile knowledge
    • [cs.RO]Grounding Language Attributes to Objects using Bayesian Eigenobjects
    • [cs.RO]Handling robot constraints within a Set-Based Multi-Task Priority Inverse Kinematics Framework
    • [cs.RO]Partial Computing Offloading Assisted Cloud Point Registration in Multi-robot SLAM
    • [cs.SD]A Music Classification Model based on Metric Learning and Feature Extraction from MP3 Audio Files
    • [cs.SD]Multilabel Automated Recognition of Emotions Induced Through Music
    • [cs.SE]Cognitively-inspired Agent-based Service Composition for Mobile & Pervasive Computing
    • [cs.SI]Algorithmic Detection and Analysis of Vaccine-Denialist Sentiment Clusters in Social Networks
    • [cs.SI]Information Source Detection with Limited Time Knowledge
    • [cs.SI]The role of bot squads in the political propaganda on Twitter
    • [cs.SY]Shared control schematic for brain controlled vehicle based on fuzzy logic
    • [eess.IV]Exploiting Epistemic Uncertainty of Anatomy Segmentation for Anomaly Detection in Retinal OCT
    • [eess.IV]Video from Stills: Lensless Imaging with Rolling Shutter
    • [eess.IV]Weakly supervised training of pixel resolution segmentation models on whole slide images
    • [eess.SP]Separating an Outlier from a Change
    • [eess.SP]The Meta Distributions of the SIR/SNR and Data Rate in Coexisting Sub-6GHz and Millimeter-wave Cellular Networks
    • [eess.SP]Vector-Valued Graph Trend Filtering with Non-Convex Penalties
    • [math.NA]On condition numbers of symmetric and nonsymmetric domain decomposition methods
    • [math.OC]Accelerating Min-Max Optimization with Application to Minimal Bounding Sphere
    • [math.OC]Interior-point Methods Strike Back: Solving the Wasserstein Barycenter Problem
    • [math.OC]Linear interpolation gives better gradients than Gaussian smoothing in derivative-free optimization
    • [math.OC]Recovery of binary sparse signals from compressed linear measurements via polynomial optimization
    • [math.OC]Scaling Up Quasi-Newton Algorithms: Communication Efficient Distributed SR1
    • [math.OC]Zeroth-Order Stochastic Alternating Direction Method of Multipliers for Nonconvex Nonsmooth Optimization
    • [math.ST]A New Mixed Generalized Negative Binomial Distribution
    • [math.ST]A note on quadratic forms of stationary functional time series under mild conditions
    • [math.ST]Complex sampling designs: uniform limit theorems and applications
    • [math.ST]From Halfspace M-depth to Multiple-output Expectile Regression
    • [math.ST]Global empirical risk minimizers with “shape constraints” are rate optimal in general dimensions
    • [math.ST]Limit distribution theory for multiple isotonic regression
    • [math.ST]On stochastic gradient Langevin dynamics with dependent data streams: the fully non-convex case
    • [math.ST]Spiked separable covariance matrices and principal components
    • [math.ST]Statistical bounds for entropic optimal transport: sample complexity and the central limit theorem
    • [math.ST]The spiked matrix model with generative priors
    • [physics.geo-ph]Towards automatically building starting models for full-waveform inversion using global optimization methods: A PSO approach via DEAP + Devito
    • [physics.soc-ph]Customer mobility and congestion in supermarkets
    • [stat.CO]Temporal Parallelization of Bayesian Filters and Smoothers
    • [stat.ME]ADDIS: an adaptive discarding algorithm for online FDR control with conservative nulls
    • [stat.ME]Essential regression
    • [stat.ME]Heterogeneous causal effects with imperfect compliance: a novel Bayesian machine learning approach
    • [stat.ME]Mean-dependent nonstationary spatial models
    • [stat.ME]Using Propensity Scores to Develop and Evaluate Treatment Rules with Observational Data
    • [stat.ML]Analysis of high-dimensional Continuous Time Markov Chains using the Local Bouncy Particle Sampler
    • [stat.ML]Enriched Mixtures of Gaussian Process Experts
    • [stat.ML]Global Momentum Compression for Sparse Communication in Distributed SGD
    • [stat.ML]Modeling Uncertainty by Learning a Hierarchy of Deep Neural Connections
    • [stat.ML]Monotonic Gaussian Process Flow
    • [stat.ML]Multiple Causes: A Causal Graphical View
    • [stat.ML]Noisy and Incomplete Boolean Matrix Factorizationvia Expectation Maximization
    • [stat.ML]On the Convergence of Memory-Based Distributed SGD
    • [stat.ML]Rarely-switching linear bandits: optimization of causal effects for the real world
    • [stat.ML]Semi-Implicit Generative Model
    • [stat.ML]Sinkhorn Barycenters with Free Support via Frank-Wolfe Algorithm
    • [stat.ML]The Label Complexity of Active Learning from Observational Data
    • [stat.ML]Ultimate Power of Inference Attacks: Privacy Risks of High-Dimensional Models

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

    • [cs.AI]Better Future through AI: Avoiding Pitfalls and Guiding AI Towards its Full Potential
    Risto Miikkulainen, Bret Greenstein, Babak Hodjat, Jerry Smith
    http://arxiv.org/abs/1905.13178v1

    • [cs.AI]Constructing High Precision Knowledge Bases with Subjective and Factual Attributes
    Ari Kobren, Pablo Bario, Oksana Yakhnenko, Johann Hibschman, Ian Langmore
    http://arxiv.org/abs/1905.12807v1

    • [cs.AI]Learning Compositional Neural Programs with Recursive Tree Search and Planning
    Thomas Pierrot, Guillaume Ligner, Scott Reed, Olivier Sigaud, Nicolas Perrin, Alexandre Laterre, David Kas, Karim Beguir, Nando de Freitas
    http://arxiv.org/abs/1905.12941v1

    • [cs.AI]Neural Consciousness Flow
    Xiaoran Xu, Wei Feng, Zhiqing Sun, Zhi-Hong Deng
    http://arxiv.org/abs/1905.13049v1

    • [cs.AI]Quantifying consensus of rankings based on q-support patterns
    Zhengui Xue, Zhiwei Lin, Hui Wang, Sally McClean
    http://arxiv.org/abs/1905.12966v1

    • [cs.AI]Unpredictability of AI
    Roman V. Yampolskiy
    http://arxiv.org/abs/1905.13053v1

    • [cs.AI]Using Restart Heuristics to Improve Agent Performance in Angry Birds
    Tommy Liu, Jochen Renz, Peng Zhang, Matthew Stephenson
    http://arxiv.org/abs/1905.12877v1

    • [cs.CG]Persistent homology detects curvature
    Peter Bubenik, Michael Hull, Dhruv Patel, Benjamin Whittle
    http://arxiv.org/abs/1905.13196v1

    • [cs.CL]A Compare-Aggregate Model with Latent Clustering for Answer Selection
    Seunghyun Yoon, Franck Dernoncourt, Doo Soon Kim, Trung Bui, Kyomin Jung
    http://arxiv.org/abs/1905.12897v1

    • [cs.CL]A Simple but Effective Method to Incorporate Multi-turn Context with BERT for Conversational Machine Comprehension
    Yasuhito Ohsugi, Itsumi Saito, Kyosuke Nishida, Hisako Asano, Junji Tomita
    http://arxiv.org/abs/1905.12848v1

    • [cs.CL]Choosing Transfer Languages for Cross-Lingual Learning
    Yu-Hsiang Lin, Chian-Yu Chen, Jean Lee, Zirui Li, Yuyan Zhang, Mengzhou Xia, Shruti Rijhwani, Junxian He, Zhisong Zhang, Xuezhe Ma, Antonios Anastasopoulos, Patrick Littell, Graham Neubig
    http://arxiv.org/abs/1905.12688v1

    • [cs.CL]Controllable Unsupervised Text Attribute Transfer via Editing Entangled Latent Representation
    Ke Wang, Hang Hua, Xiaojun Wan
    http://arxiv.org/abs/1905.12926v1

    • [cs.CL]Geolocating Political Events in Text
    Andrew Halterman
    http://arxiv.org/abs/1905.12713v1

    • [cs.CL]Hierarchical Transformers for Multi-Document Summarization
    Yang Liu, Mirella Lapata
    http://arxiv.org/abs/1905.13164v1

    • [cs.CL]Large Scale Question Paraphrase Retrieval with Smoothed Deep Metric Learning
    Daniele Bonadiman, Anjishnu Kumar, Arpit Mittal
    http://arxiv.org/abs/1905.12786v1

    • [cs.CL]Lattice-based lightly-supervised acoustic model training
    Joachim Fainberg, Ondřej Klejch, Steve Renals, Peter Bell
    http://arxiv.org/abs/1905.13150v1

    • [cs.CL]M-GWAP: An Online and Multimodal Game With A Purpose in WordPress for Mental States Annotation
    Fabio Paolizzo
    http://arxiv.org/abs/1905.12884v1

    • [cs.CL]Reducing Gender Bias in Word-Level Language Models with a Gender-Equalizing Loss Function
    Yusu Qian, Urwa Muaz, Ben Zhang, Jae Won Hyun
    http://arxiv.org/abs/1905.12801v1

    • [cs.CL]Semantically Conditioned Dialog Response Generation via Hierarchical Disentangled Self-Attention
    Wenhu Chen, Jianshu Chen, Pengda Qin, Xifeng Yan, William Yang Wang
    http://arxiv.org/abs/1905.12866v1

    • [cs.CL]The (Non-)Utility of Structural Features in BiLSTM-based Dependency Parsers
    Agnieszka Falenska, Jonas Kuhn
    http://arxiv.org/abs/1905.12676v1

    • [cs.CL]Unbabel’s Submission to the WMT2019 APE Shared Task: BERT-based Encoder-Decoder for Automatic Post-Editing
    António V. Lopes, M. Amin Farajian, Gonçalo M. Correia, Jonay Trenous, André F. T. Martins
    http://arxiv.org/abs/1905.13068v1

    • [cs.CR]Automatically Dismantling Online Dating Fraud
    Guillermo Suarez-Tangil, Matthew Edwards, Claudia Peersman, Gianluca Stringhini, Awais Rashid, Monica Whitty
    http://arxiv.org/abs/1905.12593v2

    • [cs.CV]$d$-SNE: Domain Adaptation using Stochastic Neighborhood Embedding
    Xiang Xu, Xiong Zhou, Ragav Venkatesan, Gurumurthy Swaminathan, Orchid Majumder
    http://arxiv.org/abs/1905.12775v1

    • [cs.CV]A Trainable Multiplication Layer for Auto-correlation and Co-occurrence Extraction
    Hideaki Hayashi, Seiichi Uchida
    http://arxiv.org/abs/1905.12871v1

    • [cs.CV]A survey of Object Classification and Detection based on 2D/3D data
    Xiaoke Shen
    http://arxiv.org/abs/1905.12683v1

    • [cs.CV]AssembleNet: Searching for Multi-Stream Neural Connectivity in Video Architectures
    Michael S. Ryoo, AJ Piergiovanni, Mingxing Tan, Anelia Angelova
    http://arxiv.org/abs/1905.13209v1

    • [cs.CV]Attention: A Big Surprise for Cross-Domain Person Re-Identification
    Haijun Liu, Jian Cheng, Shiguang Wang, Wen Wang
    http://arxiv.org/abs/1905.12830v1

    • [cs.CV]Deep Learning Approach for Receipt Recognition
    Anh Duc Le, Dung Van Pham, Tuan Anh Nguyen
    http://arxiv.org/abs/1905.12817v1

    • [cs.CV]Distant Pedestrian Detection in the Wild using Single Shot Detector with Deep Convolutional Generative Adversarial Networks
    Ranjith Dinakaran, Philip Easom, Li Zhang, Ahmed Bouridane, Richard Jiang, Eran Edirisinghe
    http://arxiv.org/abs/1905.12759v1

    • [cs.CV]Does computer vision matter for action?
    Brady Zhou, Philipp Krähenbühl, Vladlen Koltun
    http://arxiv.org/abs/1905.12887v1

    • [cs.CV]Dynamic Traffic Scene Classification with Space-Time Coherence
    Athma Narayanan, Isht Dwivedi, Behzad Dariush
    http://arxiv.org/abs/1905.12708v1

    • [cs.CV]Emergence of Object Segmentation in Perturbed Generative Models
    Adam Bielski, Paolo Favaro
    http://arxiv.org/abs/1905.12663v1

    • [cs.CV]Entropic Regularisation of Robust Optimal Transport
    Rozenn Dahyot, Hana Alghamdi, Mairead Grogan
    http://arxiv.org/abs/1905.12678v1

    • [cs.CV]Extending Monocular Visual Odometry to Stereo Camera System by Scale Optimization
    Jiawei Mo, Junaed Sattar
    http://arxiv.org/abs/1905.12723v1

    • [cs.CV]GlyphGAN: Style-Consistent Font Generation Based on Generative Adversarial Networks
    Hideaki Hayashi, Kohtaro Abe, Seiichi Uchida
    http://arxiv.org/abs/1905.12502v2

    • [cs.CV]Hierarchical Structure and Joint Training for Large Scale Semi-supervised Object Detection
    Ye Guo, Yali Li, Shengjin Wang
    http://arxiv.org/abs/1905.12863v1

    • [cs.CV]Interactive-predictive neural multimodal systems
    Álvaro Peris, Francisco Casacuberta
    http://arxiv.org/abs/1905.12980v1

    • [cs.CV]Learning Semantics-aware Distance Map with Semantics Layering Network for Amodal Instance Segmentation
    Ziheng Zhang, Anpei Chen, Ling Xie, Jingyi Yu, Shenghua Gao
    http://arxiv.org/abs/1905.12898v1

    • [cs.CV]On Network Design Spaces for Visual Recognition
    Ilija Radosavovic, Justin Johnson, Saining Xie, Wan-Yen Lo, Piotr Dollár
    http://arxiv.org/abs/1905.13214v1

    • [cs.CV]P3SGD: Patient Privacy Preserving SGD for Regularizing Deep CNNs in Pathological Image Classification
    Bingzhe Wu, Shiwan Zhao, Guangyu Sun, Xiaolu Zhang, Zhong Su, Caihong Zeng, Zhihong Liu
    http://arxiv.org/abs/1905.12883v1

    • [cs.CV]RoNIN: Robust Neural Inertial Navigation in the Wild: Benchmark, Evaluations, and New Methods
    Hang Yan, Sachini Herath, Yasutaka Furukawa
    http://arxiv.org/abs/1905.12853v1

    • [cs.CV]The Fashion IQ Dataset: Retrieving Images by Combining Side Information and Relative Natural Language Feedback
    Xiaoxiao Guo, Hui Wu, Yupeng Gao, Steven Rennie, Rogerio Feris
    http://arxiv.org/abs/1905.12794v1

    • [cs.CV]The General Pair-based Weighting Loss for Deep Metric Learning
    Haijun Liu, Jian Cheng, Wen Wang, Yanzhou Su
    http://arxiv.org/abs/1905.12837v1

    • [cs.CV]Towards Photo-Realistic Visible Watermark Removal with Conditional Generative Adversarial Networks
    Xiang Li, Chan Lu, Danni Cheng, Wei-Hong Li, Mei Cao, Bo Liu, Jiechao Ma, Wei-Shi Zheng
    http://arxiv.org/abs/1905.12845v1

    • [cs.CV]Unsupervised Classification of Street Architectures Based on InfoGAN
    Ning Wang, Xianhan Zeng, Renjie Xie, Zefei Gao, Yi Zheng, Ziran Liao, Junyan Yang, Qiao Wang
    http://arxiv.org/abs/1905.12844v1

    • [cs.CV]What Makes Training Multi-Modal Networks Hard?
    Weiyao Wang, Du Tran, Matt Feiszli
    http://arxiv.org/abs/1905.12681v1

    • [cs.CV]iSAID: A Large-scale Dataset for Instance Segmentation in Aerial Images
    Syed Waqas Zamir, Aditya Arora, Akshita Gupta, Salman Khan, Guolei Sun, Fahad Shahbaz Khan, Fan Zhu, Ling Shao, Gui-Song Xia, Xiang Bai
    http://arxiv.org/abs/1905.12886v1

    • [cs.CY]The Security Implications of Data Subject Rights
    Jatinder Singh, Jennifer Cobbe
    http://arxiv.org/abs/1905.13005v1

    • [cs.DC]Evaluation of pilot jobs for Apache Spark applications on HPC clusters
    Valerie Hayot-Sasson, Tristan Glatard
    http://arxiv.org/abs/1905.12720v1

    • [cs.DC]MCompiler: A Synergistic Compilation Framework
    Aniket Shivam, Alexandru Nicolau, Alexander V. Veidenbaum
    http://arxiv.org/abs/1905.12755v1

    • [cs.DC]The Bloom Clock
    Lum Ramabaja
    http://arxiv.org/abs/1905.13064v1

    • [cs.DC]Workflow Management on BFT Blockchains
    Joerg Evermann, Henry Kim
    http://arxiv.org/abs/1905.12652v1

    • [cs.DL]Exploring the Effects of Data Set Choice on Measuring International Research Collaboration: an Example Using the ACM Digital Library and Microsoft Academic Graph
    Ba Xuan Nguyen, Markus Luczak-Roesch, Jesse David Dinneen
    http://arxiv.org/abs/1905.12834v1

    • [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/1905.11342v3

    • [cs.ET]Nonvolatile Spintronic Memory Cells for Neural Networks
    Andrew W. Stephan, Qiuwen Lou, Michael Niemier, X. Sharon Hu, Steven J. Koester
    http://arxiv.org/abs/1905.12679v1

    • [cs.GT]Heuristics in Multi-Winner Approval Voting
    Jaelle Scheuerman, Jason L. Harman, Nicholas Mattei, K. Brent Venable
    http://arxiv.org/abs/1905.12104v2

    • [cs.HC]Visualizing a Moving Target: A Design Study on Task Parallel Programs in the Presence of Evolving Data and Concerns
    Katy Williams, Alex Bigelow, Kate Isaacs
    http://arxiv.org/abs/1905.13135v1

    • [cs.IR]Collaborative Self-Attention for Recommender Systems
    Kai-Lang Yao, Wu-Jun Li
    http://arxiv.org/abs/1905.13133v1

    • [cs.IR]Deep Adversarial Social Recommendation
    Wenqi Fan, Tyler Derr, Yao Ma, Jianping Wang, Jiliang Tang, Qing Li
    http://arxiv.org/abs/1905.13160v1

    • [cs.IR]Deep Cross Networks with Aesthetic Preference for Cross-domain Recommendation
    Jian Liu, Pengpeng Zhao, Yanchi Liu, Victor S. Sheng, Fuzheng Zhuang, Jiajie Xu, Xiaofang Zhou, Hui Xiong
    http://arxiv.org/abs/1905.13030v1

    • [cs.IR]Explainable Fashion Recommendation: A Semantic Attribute Region Guided Approach
    Min Hou, Le Wu, Enhong Chen, Zhi Li, Vincent W. Zheng, Qi Liu
    http://arxiv.org/abs/1905.12862v1

    • [cs.IR]FairSearch: A Tool For Fairness in Ranked Search Results
    Meike Zehlike, Tom Sühr, Carlos Castillo, Ivan Kitanovski
    http://arxiv.org/abs/1905.13134v1

    • [cs.IR]Job Recommendation through Progression of Job Selection
    Amber Nigam, Aakash Roy, Hartaran Singh, Aabhas Tonwer
    http://arxiv.org/abs/1905.13136v1

    • [cs.IR]On the Effectiveness of Low-rank Approximations for Collaborative Filtering compared to Neural Networks
    Marcel Kurovski, Florian Wilhelm
    http://arxiv.org/abs/1905.12967v1

    • [cs.IR]SAIN: Self-Attentive Integration Network for Recommendation
    Seoungjun Yun, Raehyun Kim, Miyoung Ko, Jaewoo Kang
    http://arxiv.org/abs/1905.13130v1

    • [cs.IR]STAR-GCN: Stacked and Reconstructed Graph Convolutional Networks for Recommender Systems
    Jiani Zhang, Xingjian Shi, Shenglin Zhao, Irwin King
    http://arxiv.org/abs/1905.13129v1

    • [cs.IT]A New Approach to Lossy Network Compression of a Tuple of Correlated Multivariate Gaussian RVs
    Charalambos D. Charalambous, Jan H. van Schuppen
    http://arxiv.org/abs/1905.12695v1

    • [cs.IT]A Novel Time-Based Modulation Scheme in Time-Asynchronous Channels for Molecular Communications
    Qingchao Li
    http://arxiv.org/abs/1905.13084v1

    • [cs.IT]Deep Learning for Direct Hybrid Precoding in Millimeter Wave Massive MIMO Systems
    Xiaofeng Li, Ahmed Alkhateeb
    http://arxiv.org/abs/1905.13212v1

    • [cs.IT]Neural Entropic Estimation: A faster path to mutual information estimation
    Chan Chung, Ali Al-Bashabsheh, Hing Pang Huang, Michael Lim, Da Sun Handason Tam, Chao Zhao
    http://arxiv.org/abs/1905.12957v1

    • [cs.IT]On Multiple-Access Systems with Queue-Length Dependent Service Quality
    Daewon Seo, Avhishek Chatterjee, Lav R. Varshney
    http://arxiv.org/abs/1905.13099v1

    • [cs.IT]Partially APN Boolean functions and classes of functions that are not APN infinitely often
    Lilya Budaghyan, Nikolay S. Kaleyski, Soonhak Kwon, Constanza Riera, Pantelimon Stanica
    http://arxiv.org/abs/1905.13025v1

    • [cs.IT]Skew constacyclic codes over a non-chain ring $\mathbb{F}_{q}[u,v]/\langle f(u),g(v), uv-vu\rangle$**
    Swati Bhardwaj, Madhu Raka
    http://arxiv.org/abs/1905.12933v1

    • [cs.LG]A General Optimization Framework for Dynamic Time Warping
    Dave Deriso, Stephen Boyd
    http://arxiv.org/abs/1905.12893v1

    • [cs.LG]A Generalized Framework of Sequence Generation with Application to Undirected Sequence Models
    Elman Mansimov, Alex Wang, Kyunghyun Cho
    http://arxiv.org/abs/1905.12790v1

    • [cs.LG]Active Learning in the Overparameterized and Interpolating Regime
    Mina Karzand, Robert D. Nowak
    http://arxiv.org/abs/1905.12782v1

    • [cs.LG]Adversarial Imitation Learning from Incomplete Demonstrations
    Mingfei Sun, Xiaojuan Ma
    http://arxiv.org/abs/1905.12310v2

    • [cs.LG]Adversarial Sub-sequence for Text Generation
    Xingyuan Chen, Yanzhe Li, Peng Jin, Jiuhua Zhang, Xinyu Dai, Jiajun Chen, Gang Song
    http://arxiv.org/abs/1905.12835v1

    • [cs.LG]AlignFlow: Cycle Consistent Learning from Multiple Domains via Normalizing Flows
    Aditya Grover, Christopher Chute, Rui Shu, Zhangjie Cao, Stefano Ermon
    http://arxiv.org/abs/1905.12892v1

    • [cs.LG]An adaptive nearest neighbor rule for classification
    Akshay Balsubramani, Sanjoy Dasgupta, Yoav Freund, Shay Moran
    http://arxiv.org/abs/1905.12717v1

    • [cs.LG]Bandlimiting Neural Networks Against Adversarial Attacks
    Yuping Lin, Kasra Ahmadi K. A., Hui Jiang
    http://arxiv.org/abs/1905.12797v1

    • [cs.LG]Batch weight for domain adaptation with mass shift
    Mikołaj Bińkowski, R Devon Hjelm, Aaron Courville
    http://arxiv.org/abs/1905.12760v1

    • [cs.LG]Beyond Online Balanced Descent: An Optimal Algorithm for Smoothed Online Optimization
    Gautam Goel, Yiheng Lin, Haoyuan Sun, Adam Wierman
    http://arxiv.org/abs/1905.12776v1

    • [cs.LG]Confirmatory Bayesian Online Change Point Detection in the Covariance Structure of Gaussian Processes
    Jiyeon Han, Kyowoon Lee, Anh Tong, Jaesik Choi
    http://arxiv.org/abs/1905.13168v1

    • [cs.LG]Convergence of Distributed Stochastic Variance Reduced Methods without Sampling Extra Data
    Shicong Cen, Huishuai Zhang, Yuejie Chi, Wei Chen, Tie-Yan Liu
    http://arxiv.org/abs/1905.12648v1

    • [cs.LG]Cross-modal Variational Auto-encoder with Distributed Latent Spaces and Associators
    Dae Ung Jo, ByeongJu Lee, Jongwon Choi, Haanju Yoo, Jin Young Choi
    http://arxiv.org/abs/1905.12867v1

    • [cs.LG]Data-Dependent Differentially Private Parameter Learning for Directed Graphical Models
    Amrita Roy Chowdhury, Theodoros Rekatsinas, Somesh Jha
    http://arxiv.org/abs/1905.12813v1

    • [cs.LG]Deep ensemble learning for Alzheimers disease classification
    Ning An, Huitong Ding, Jiaoyun Yang, Rhoda Au, Ting Fang Alvin Ang
    http://arxiv.org/abs/1905.12827v1

    • [cs.LG]Deep multi-class learning from label proportions
    Gabriel Dulac-Arnold, Neil Zeghidour, Marco Cuturi, Lucas Beyer, Jean-Philippe Vert
    http://arxiv.org/abs/1905.12909v1

    • [cs.LG]Defining Admissible Rewards for High Confidence Policy Evaluation
    Niranjani Prasad, Barbara E Engelhardt, Finale Doshi-Velez
    http://arxiv.org/abs/1905.13167v1

    • [cs.LG]Diffusion Variational Autoencoders
    Henry Li, Ofir Lindenbaum, Xiuyuan Cheng, Alexander Cloninger
    http://arxiv.org/abs/1905.12724v1

    • [cs.LG]Distribution-dependent and Time-uniform Bounds for Piecewise i.i.d Bandits
    Subhojyoti Mukherjee, Odalric-Ambrym Maillard
    http://arxiv.org/abs/1905.13159v1

    • [cs.LG]Effective Medical Test Suggestions Using Deep Reinforcement Learning
    Yang-En Chen, Kai-Fu Tang, Yu-Shao Peng, Edward Y. Chang
    http://arxiv.org/abs/1905.12916v1

    • [cs.LG]Equipping Experts/Bandits with Long-term Memory
    Kai Zheng, Haipeng Luo, Ilias Diakonikolas, Liwei Wang
    http://arxiv.org/abs/1905.12950v1

    • [cs.LG]Evaluating structure learning algorithms with a balanced scoring function
    Anthony Constantinou
    http://arxiv.org/abs/1905.12666v1

    • [cs.LG]Exploiting Uncertainty of Loss Landscape for Stochastic Optimization
    Vineeth S. Bhaskara, Sneha Desai
    http://arxiv.org/abs/1905.13200v1

    • [cs.LG]Fair Regression: Quantitative Definitions and Reduction-based Algorithms
    Alekh Agarwal, Miroslav Dudík, Zhiwei Steven Wu
    http://arxiv.org/abs/1905.12843v1

    • [cs.LG]Fairness and Missing Values
    Fernando Martínez-Plumed, Cèsar Ferri, David Nieves, José Hernández-Orallo
    http://arxiv.org/abs/1905.12728v1

    • [cs.LG]Finite-time Analysis of Approximate Policy Iteration for the Linear Quadratic Regulator
    Karl Krauth, Stephen Tu, Benjamin Recht
    http://arxiv.org/abs/1905.12842v1

    • [cs.LG]Function approximation by deep networks
    H. N. Mhaskar, T. Poggio
    http://arxiv.org/abs/1905.12882v1

    • [cs.LG]Generalization Bounds of Stochastic Gradient Descent for Wide and Deep Neural Networks
    Yuan Cao, Quanquan Gu
    http://arxiv.org/abs/1905.13210v1

    • [cs.LG]Generalized Separable Nonnegative Matrix Factorization
    Junjun Pan, Nicolas Gillis
    http://arxiv.org/abs/1905.12995v1

    • [cs.LG]Generating Contrastive Explanations with Monotonic Attribute Functions
    Ronny Luss, Pin-Yu Chen, Amit Dhurandhar, Prasanna Sattigeri, Karthikeyan Shanmugam, Chun-Chen Tu
    http://arxiv.org/abs/1905.12698v1

    • [cs.LG]Graph Learning Network: A Structure Learning Algorithm
    Darwin Saire Pilco, Adín Ramírez Rivera
    http://arxiv.org/abs/1905.12665v1

    • [cs.LG]Graph Neural Tangent Kernel: Fusing Graph Neural Networks with Graph Kernels
    Simon S. Du, Kangcheng Hou, Barnabás Póczos, Ruslan Salakhutdinov, Ruosong Wang, Keyulu Xu
    http://arxiv.org/abs/1905.13192v1

    • [cs.LG]Graph Normalizing Flows
    Jenny Liu, Aviral Kumar, Jimmy Ba, Jamie Kiros, Kevin Swersky
    http://arxiv.org/abs/1905.13177v1

    • [cs.LG]ImJoy: an open-source computational platform for the deep learning era
    Wei Ouyang, Florian Mueller, Martin Hjelmare, Emma Lundberg, Christophe Zimmer
    http://arxiv.org/abs/1905.13105v1

    • [cs.LG]Imitation Learning as $f$-Divergence Minimization
    Liyiming Ke, Matt Barnes, Wen Sun, Gilwoo Lee, Sanjiban Choudhury, Siddhartha Srinivasa
    http://arxiv.org/abs/1905.12888v1

    • [cs.LG]Implicit Regularization of Accelerated Methods in Hilbert Spaces
    Nicolò Pagliana, Lorenzo Rosasco
    http://arxiv.org/abs/1905.13000v1

    • [cs.LG]Information theoretic learning of robust deep representations
    Nicolas Pinchaud
    http://arxiv.org/abs/1905.12874v1

    • [cs.LG]Interpretable Adversarial Training for Text
    Samuel Barham, Soheil Feizi
    http://arxiv.org/abs/1905.12864v1

    • [cs.LG]Intrinsic dimension of data representations in deep neural networks
    Alessio Ansuini, Alessandro Laio, Jakob H. Macke, Davide Zoccolan
    http://arxiv.org/abs/1905.12784v1

    • [cs.LG]Latent Space Secrets of Denoising Text-Autoencoders
    Tianxiao Shen, Jonas Mueller, Regina Barzilay, Tommi Jaakkola
    http://arxiv.org/abs/1905.12777v1

    • [cs.LG]Learning Nonsymmetric Determinantal Point Processes
    Mike Gartrell, Victor-Emmanuel Brunel, Elvis Dohmatob, Syrine Krichene
    http://arxiv.org/abs/1905.12962v1

    • [cs.LG]Learning Representations by Humans, for Humans
    Sophie Hilgard, Nir Rosenfeld, Mahzarin R. Banaji, Jack Cao, David C. Parkes
    http://arxiv.org/abs/1905.12686v1

    • [cs.LG]Learning by Active Nonlinear Diffusion
    Mauro Maggioni, James M. Murphy
    http://arxiv.org/abs/1905.12989v1

    • [cs.LG]Learning to Balance: Bayesian Meta-Learning for Imbalanced and Out-of-distribution Tasks
    Donghyun Na, Hae Beom Lee, Saehoon Kim, Minseop Park, Eunho Yang, Sung Ju Hwang
    http://arxiv.org/abs/1905.12917v1

    • [cs.LG]Learning to Crawl
    Utkarsh Upadhyay, Robert Busa-Fekete, Wojciech Kotlowski, David Pal, Balazs Szorenyi
    http://arxiv.org/abs/1905.12781v1

    • [cs.LG]Less is More: An Exploration of Data Redundancy with Active Dataset Subsampling
    Kashyap Chitta, Jose M. Alvarez, Elmar Haussmann, Clement Farabet
    http://arxiv.org/abs/1905.12737v1

    • [cs.LG]Matrix Completion in the Unit Hypercube via Structured Matrix Factorization
    Emanuele Bugliarello, Swayambhoo Jain, Vineeth Rakesh
    http://arxiv.org/abs/1905.12881v1

    • [cs.LG]Matrix-Free Preconditioning in Online Learning
    Ashok Cutkosky, Tamas Sarlos
    http://arxiv.org/abs/1905.12721v1

    • [cs.LG]Memory-Driven Mixed Low Precision Quantization For Enabling Deep Network Inference On Microcontrollers
    Manuele Rusci, Alessandro Capotondi, Luca Benini
    http://arxiv.org/abs/1905.13082v1

    • [cs.LG]Meta Dropout: Learning to Perturb Features for Generalization
    Hae Beom Lee, Taewook Nam, Eunho Yang, Sung Ju Hwang
    http://arxiv.org/abs/1905.12914v1

    • [cs.LG]Meta-Surrogate Benchmarking for Hyperparameter Optimization
    Aaron Klein, Zhenwen Dai, Frank Hutter, Neil Lawrence, Javier Gonzalez
    http://arxiv.org/abs/1905.12982v1

    • [cs.LG]Multi-Objective Generalized Linear Bandits
    Shiyin Lu, Guanghui Wang, Yao Hu, Lijun Zhang
    http://arxiv.org/abs/1905.12879v1

    • [cs.LG]Near-Term Quantum-Classical Associative Adversarial Networks
    Eric R. Anschuetz, Cristian Zanoci
    http://arxiv.org/abs/1905.13205v1

    • [cs.LG]On the Generalization Gap in Reparameterizable Reinforcement Learning
    Huan Wang, Stephan Zheng, Caiming Xiong, Richard Socher
    http://arxiv.org/abs/1905.12654v1

    • [cs.LG]One-element Batch Training by Moving Window
    Przemysław Spurek, Szymon Knop, Jacek Tabor, Igor Podolak, Bartosz Wójcik
    http://arxiv.org/abs/1905.12947v1

    • [cs.LG]Particle Filter Recurrent Neural Networks
    Xiao Ma, Peter Karkus, David Hsu, Wee Sun Lee
    http://arxiv.org/abs/1905.12885v1

    • [cs.LG]Path-Augmented Graph Transformer Network
    Benson Chen, Regina Barzilay, Tommi Jaakkola
    http://arxiv.org/abs/1905.12712v1

    • [cs.LG]Provably Efficient Q-Learning with Low Switching Cost
    Yu Bai, Tengyang Xie, Nan Jiang, Yu-Xiang Wang
    http://arxiv.org/abs/1905.12849v1

    • [cs.LG]Quantifying the alignment of graph and features in deep learning
    Yifan Qian, Paul Expert, Tom Rieu, Pietro Panzarasa, Mauricio Barahona
    http://arxiv.org/abs/1905.12921v1

    • [cs.LG]Recursive Sketches for Modular Deep Learning
    Badih Ghazi, Rina Panigrahy, Joshua R. Wang
    http://arxiv.org/abs/1905.12730v1

    • [cs.LG]Regression with Conditional GAN
    Karan Aggarwal, Matthieu Kirchmeyer, Pranjul Yadav, S. Sathiya Keerthi, Patrick Gallinari
    http://arxiv.org/abs/1905.12868v1

    • [cs.LG]Regret Bounds for Thompson Sampling in Restless Bandit Problems
    Young Hun Jung, Ambuj Tewari
    http://arxiv.org/abs/1905.12673v1

    • [cs.LG]Reinforcement Learning and Adaptive Sampling for Optimized DNN Compilation
    Byung Hoon Ahn, Prannoy Pilligundla, Hadi Esmaeilzadeh
    http://arxiv.org/abs/1905.12799v1

    • [cs.LG]Reinforcement Learning for Slate-based Recommender Systems: A Tractable Decomposition and Practical Methodology
    Eugene Ie, Vihan Jain, Jing Wang, Sanmit Navrekar, Ritesh Agarwal, Rui Wu, Heng-Tze Cheng, Morgane Lustman, Vince Gatto, Paul Covington, Jim McFadden, Tushar Chandra, Craig Boutilier
    http://arxiv.org/abs/1905.12767v1

    • [cs.LG]Securing Connected & Autonomous Vehicles: Challenges Posed by Adversarial Machine Learning and The Way Forward
    Adnan Qayyum, Muhammad Usama, Junaid Qadir, Ala Al-Fuqaha
    http://arxiv.org/abs/1905.12762v1

    • [cs.LG]Sequential no-Substitution k-Median-Clustering
    Tom Hess, Sivan Sabato
    http://arxiv.org/abs/1905.12925v1

    • [cs.LG]Structured Monte Carlo Sampling for Nonisotropic Distributions via Determinantal Point Processes
    Krzysztof Choromanski, Aldo Pacchiano, Jack Parker-Holder, Yunhao Tang
    http://arxiv.org/abs/1905.12667v1

    • [cs.LG]Toward Runtime-Throttleable Neural Networks
    Jesse Hostetler
    http://arxiv.org/abs/1905.13179v1

    • [cs.LG]Training Generative Adversarial Networks from Incomplete Observations using Factorised Discriminators
    Daniel Stoller, Sebastian Ewert, Simon Dixon
    http://arxiv.org/abs/1905.12660v1

    • [cs.LG]Understanding Goal-Oriented Active Learning via Influence Functions
    Minjie Xu, Gary Kazantsev
    http://arxiv.org/abs/1905.13183v1

    • [cs.LG]Unsupervised Paraphrasing without Translation
    Aurko Roy, David Grangier
    http://arxiv.org/abs/1905.12752v1

    • [cs.LG]Unsupervised pre-training helps to conserve views from input distribution
    Nicolas Pinchaud
    http://arxiv.org/abs/1905.12889v1

    • [cs.LG]Using Text Embeddings for Causal Inference
    Victor Veitch, Dhanya Sridhar, David M. Blei
    http://arxiv.org/abs/1905.12741v1

    • [cs.LG]Wasserstein Style Transfer
    Youssef Mroueh
    http://arxiv.org/abs/1905.12828v1

    • [cs.LG]What Can Neural Networks Reason About?
    Keyulu Xu, Jingling Li, Mozhi Zhang, Simon S. Du, Ken-ichi Kawarabayashi, Stefanie Jegelka
    http://arxiv.org/abs/1905.13211v1

    • [cs.LO]Data Complexity and Rewritability of Ontology-Mediated Queries in Metric Temporal Logic under the Event-Based Semantics (Full Version)
    Vladislav Ryzhikov, Przemyslaw Andrzej Walega, Michael Zakharyaschev
    http://arxiv.org/abs/1905.12990v1

    • [cs.LO]Towards Finding Longer Proofs
    Zsolt Zombori, Adrián Csiszárik, Henryk Michalewski, Cezary Kaliszyk, Josef Urban
    http://arxiv.org/abs/1905.13100v1

    • [cs.MA]Ridesharing with Driver Location Preferences
    Duncan Rheingans-Yoo, Scott Duke Kominers, Hongyao Ma, David C. Parkes
    http://arxiv.org/abs/1905.13191v1

    • [cs.NE]A Hippocampus Model for Online One-Shot Storage of Pattern Sequences
    Jan Melchior, Mehdi Bayati, Amir Azizi, Sen Cheng, Laurenz Wiskott
    http://arxiv.org/abs/1905.12937v1

    • [cs.NE]Spatial Evolutionary Generative Adversarial Networks
    Jamal Toutouh, Erik Hemberg, Una-May O’Reilly
    http://arxiv.org/abs/1905.12702v1

    • [cs.NI]Standing on the Shoulders of Giants: AI-driven Calibration of Localisation Technologies
    Aftab Khan, Tim Farnham, Roget Kou, Usman Raza, Thajanee Premalal, Aleksandar Stanoev, William Thompson
    http://arxiv.org/abs/1905.13118v1

    • [cs.OH]Definitively Identifying an Inherent Limitation to Actual Cognition
    Arthur Charlesworth
    http://arxiv.org/abs/1905.13010v1

    • [cs.RO]A Realtime Autonomous Robot Navigation Framework for Human like High-level Interaction and Task Planning in Global Dynamic Environment
    Sung-Hyeon Joo, Sumaira Manzoor, Yuri Goncalves Rocha, Hyun-Uk Lee, Tae-Yong Kuc
    http://arxiv.org/abs/1905.12942v1

    • [cs.RO]Assistive robot operated via P300-based Brain Computer Interface
    Filippo Arrichiello, Paolo Di Lillo, Daniele Di Vito, Gianluca Antonelli, Stefano Chiaverini
    http://arxiv.org/abs/1905.12927v1

    • [cs.RO]Bayesian Grasp: Robotic visual stable grasp based on prior tactile knowledge
    Teng Xue, Wenhai Liu, Mingshuo Han, Zhenyu Pan, Jin Ma, Quanquan Shao, Weiming Wang
    http://arxiv.org/abs/1905.12920v1

    • [cs.RO]Grounding Language Attributes to Objects using Bayesian Eigenobjects
    Vanya Cohen, Benjamin Burchfiel, Thao Nguyen, Nakul Gopalan, Stefanie Tellex, George Konidaris
    http://arxiv.org/abs/1905.13153v1

    • [cs.RO]Handling robot constraints within a Set-Based Multi-Task Priority Inverse Kinematics Framework
    Paolo Di Lillo, Stefano Chiaverini, Gianluca Antonelli
    http://arxiv.org/abs/1905.12945v1

    • [cs.RO]Partial Computing Offloading Assisted Cloud Point Registration in Multi-robot SLAM
    Biwei Li, Zhenqiang Mi, Yu Guo, Yang Yang, Mohammad S. Obaidat
    http://arxiv.org/abs/1905.12973v1

    • [cs.SD]A Music Classification Model based on Metric Learning and Feature Extraction from MP3 Audio Files
    Angelo C. Mendes da Silva, Mauricio A. Nunes, Raul Fonseca Neto
    http://arxiv.org/abs/1905.12804v1

    • [cs.SD]Multilabel Automated Recognition of Emotions Induced Through Music
    Fabio Paolizzo, Natalia Pichierri, Daniele Casali, Daniele Giardino, Marco Matta, Giovanni Costantini
    http://arxiv.org/abs/1905.12629v1

    • [cs.SE]Cognitively-inspired Agent-based Service Composition for Mobile & Pervasive Computing
    Oscar J. Romero
    http://arxiv.org/abs/1905.12630v1

    • [cs.SI]Algorithmic Detection and Analysis of Vaccine-Denialist Sentiment Clusters in Social Networks
    Bjarke Mønsted, Sune Lehmann
    http://arxiv.org/abs/1905.12908v1

    • [cs.SI]Information Source Detection with Limited Time Knowledge
    Xuecheng Liu, Luoyi Fu, Bo Jiang, Xiaojun Lin, Xinbing Wang
    http://arxiv.org/abs/1905.12913v1

    • [cs.SI]The role of bot squads in the political propaganda on Twitter
    Guido Caldarelli, Rocco De Nicola, Fabio Del Vigna, Marinella Petrocchi, Fabio Saracco
    http://arxiv.org/abs/1905.12687v1

    • [cs.SY]Shared control schematic for brain controlled vehicle based on fuzzy logic
    Na Dong, Wen-qi Zhang, Zhong-ke Gao
    http://arxiv.org/abs/1905.13044v1

    • [eess.IV]Exploiting Epistemic Uncertainty of Anatomy Segmentation for Anomaly Detection in Retinal OCT
    Philipp Seeböck, José Ignacio Orlando, Thomas Schlegl, Sebastian M. Waldstein, Hrvoje Bogunović, Sophie Klimscha, Georg Langs, Ursula Schmidt-Erfurth
    http://arxiv.org/abs/1905.12806v1

    • [eess.IV]Video from Stills: Lensless Imaging with Rolling Shutter
    Nick Antipa, Patrick Oare, Emrah Bostan, Ren Ng, Laura Waller
    http://arxiv.org/abs/1905.13221v1

    • [eess.IV]Weakly supervised training of pixel resolution segmentation models on whole slide images
    Nicolas Pinchaud
    http://arxiv.org/abs/1905.12931v1

    • [eess.SP]Separating an Outlier from a Change
    Deniz Sargun, C. Emre Koksal
    http://arxiv.org/abs/1905.12915v1

    • [eess.SP]The Meta Distributions of the SIR/SNR and Data Rate in Coexisting Sub-6GHz and Millimeter-wave Cellular Networks
    Hazem Ibrahim, Hina Tabassum, Uyen T. Nguyen
    http://arxiv.org/abs/1905.12002v2

    • [eess.SP]Vector-Valued Graph Trend Filtering with Non-Convex Penalties
    Rohan Varma, Harlin Lee, Jelena Kovačević, Yuejie Chi
    http://arxiv.org/abs/1905.12692v1

    • [math.NA]On condition numbers of symmetric and nonsymmetric domain decomposition methods
    Juan Galvis
    http://arxiv.org/abs/1905.12800v1

    • [math.OC]Accelerating Min-Max Optimization with Application to Minimal Bounding Sphere
    Hakan Gokcesu, Kaan Gokcesu, Suleyman Serdar Kozat
    http://arxiv.org/abs/1905.12733v1

    • [math.OC]Interior-point Methods Strike Back: Solving the Wasserstein Barycenter Problem
    Dongdong Ge, Haoyue Wang, Zikai Xiong, Yinyu Ye
    http://arxiv.org/abs/1905.12895v1

    • [math.OC]Linear interpolation gives better gradients than Gaussian smoothing in derivative-free optimization
    Albert S Berahas, Liyuan Cao, Krzysztof Choromanski, Katya Scheinberg
    http://arxiv.org/abs/1905.13043v1

    • [math.OC]Recovery of binary sparse signals from compressed linear measurements via polynomial optimization
    Sophie M. Fosson, Mohammad Abuabiah
    http://arxiv.org/abs/1905.13181v1

    • [math.OC]Scaling Up Quasi-Newton Algorithms: Communication Efficient Distributed SR1
    Majid Jahani, Mohammadreza Nazari, Sergey Rusakov, Albert S. Berahas, Martin Takáč
    http://arxiv.org/abs/1905.13096v1

    • [math.OC]Zeroth-Order Stochastic Alternating Direction Method of Multipliers for Nonconvex Nonsmooth Optimization
    Feihu Huang, Shangqian Gao, Songcan Chen, Heng Huang
    http://arxiv.org/abs/1905.12729v1

    • [math.ST]A New Mixed Generalized Negative Binomial Distribution
    Anwar Hassan, Ishfaq Shah Ahmad, Peer Bilal Ahmad
    http://arxiv.org/abs/1905.12852v1

    • [math.ST]A note on quadratic forms of stationary functional time series under mild conditions
    Anne van Delft
    http://arxiv.org/abs/1905.13186v1

    • [math.ST]Complex sampling designs: uniform limit theorems and applications
    Qiyang Han, Jon A. Wellner
    http://arxiv.org/abs/1905.12824v1

    • [math.ST]From Halfspace M-depth to Multiple-output Expectile Regression
    Abdelaati Daouia, Davy Paindaveine
    http://arxiv.org/abs/1905.12718v1

    • [math.ST]Global empirical risk minimizers with “shape constraints” are rate optimal in general dimensions
    Qiyang Han
    http://arxiv.org/abs/1905.12823v1

    • [math.ST]Limit distribution theory for multiple isotonic regression
    Qiyang Han, Cun-Hui Zhang
    http://arxiv.org/abs/1905.12825v1

    • [math.ST]On stochastic gradient Langevin dynamics with dependent data streams: the fully non-convex case
    Ngoc Huy Chau, Éric Moulines, Miklos Rásonyi, Sotirios Sabanis, Ying Zhang
    http://arxiv.org/abs/1905.13142v1

    • [math.ST]Spiked separable covariance matrices and principal components
    Xiucai Ding, Fan Yang
    http://arxiv.org/abs/1905.13060v1

    • [math.ST]Statistical bounds for entropic optimal transport: sample complexity and the central limit theorem
    Gonzalo Mena, Jonathan Weed
    http://arxiv.org/abs/1905.11882v2

    • [math.ST]The spiked matrix model with generative priors
    Benjamin Aubin, Bruno Loureiro, Antoine Maillard, Florent Krzakala, Lenka Zdeborová
    http://arxiv.org/abs/1905.12385v2

    • [physics.geo-ph]Towards automatically building starting models for full-waveform inversion using global optimization methods: A PSO approach via DEAP + Devito
    Oscar F. Mojica, Navjot Kukreja
    http://arxiv.org/abs/1905.12795v1

    • [physics.soc-ph]Customer mobility and congestion in supermarkets
    Fabian Ying, Alisdair O. G. Wallis, Mariano Beguerisse-Díaz, Mason A. Porter, Sam D. Howison
    http://arxiv.org/abs/1905.13098v1

    • [stat.CO]Temporal Parallelization of Bayesian Filters and Smoothers
    Simo Särkkä, Ángel F. García-Fernández
    http://arxiv.org/abs/1905.13002v1

    • [stat.ME]ADDIS: an adaptive discarding algorithm for online FDR control with conservative nulls
    Jinjin Tian, Aaditya Ramdas
    http://arxiv.org/abs/1905.11465v2

    • [stat.ME]Essential regression
    Xin Bing, Florentina Bunea, Marten Wegkamp, Seth Strimas-Mackey
    http://arxiv.org/abs/1905.12696v1

    • [stat.ME]Heterogeneous causal effects with imperfect compliance: a novel Bayesian machine learning approach
    Falco J. Bargagli-Stoffi, Kristof De-Witte, Giorgio Gnecco
    http://arxiv.org/abs/1905.12707v1

    • [stat.ME]Mean-dependent nonstationary spatial models
    Geoffrey Colin Lee Peterson, Joseph Guinness, Adam Terando, Brian J. Reich
    http://arxiv.org/abs/1905.12684v1

    • [stat.ME]Using Propensity Scores to Develop and Evaluate Treatment Rules with Observational Data
    Jeremy Roth, Noah Simon
    http://arxiv.org/abs/1905.12768v1

    • [stat.ML]Analysis of high-dimensional Continuous Time Markov Chains using the Local Bouncy Particle Sampler
    Tingting Zhao, Alexandre Bouchard-Côté
    http://arxiv.org/abs/1905.13120v1

    • [stat.ML]Enriched Mixtures of Gaussian Process Experts
    Charles W. L. Gadd, Sara Wade, Alexis Boukouvalas
    http://arxiv.org/abs/1905.12969v1

    • [stat.ML]Global Momentum Compression for Sparse Communication in Distributed SGD
    Shen-Yi Zhao, Yin-Peng Xie, Hao Gao, Wu-Jun Li
    http://arxiv.org/abs/1905.12948v1

    • [stat.ML]Modeling Uncertainty by Learning a Hierarchy of Deep Neural Connections
    Raanan Y. Rohekar, Yaniv Gurwicz, Shami Nisimov, Gal Novik
    http://arxiv.org/abs/1905.13195v1

    • [stat.ML]Monotonic Gaussian Process Flow
    Ivan Ustyuzhaninov, Ieva Kazlauskaite, Carl Henrik Ek, Neill D. F. Campbell
    http://arxiv.org/abs/1905.12930v1

    • [stat.ML]Multiple Causes: A Causal Graphical View
    Yixin Wang, David M. Blei
    http://arxiv.org/abs/1905.12793v1

    • [stat.ML]Noisy and Incomplete Boolean Matrix Factorizationvia Expectation Maximization
    Lifan Liang, Songjian Lu
    http://arxiv.org/abs/1905.12766v1

    • [stat.ML]On the Convergence of Memory-Based Distributed SGD
    Shen-Yi Zhao, Hao Gao, Wu-Jun Li
    http://arxiv.org/abs/1905.12960v1

    • [stat.ML]Rarely-switching linear bandits: optimization of causal effects for the real world
    Benjamin Lansdell, Sofia Triantafillou, Konrad Kording
    http://arxiv.org/abs/1905.13121v1

    • [stat.ML]Semi-Implicit Generative Model
    Mingzhang Yin, Mingyuan Zhou
    http://arxiv.org/abs/1905.12659v1

    • [stat.ML]Sinkhorn Barycenters with Free Support via Frank-Wolfe Algorithm
    Giulia Luise, Saverio Salzo, Massimiliano Pontil, Carlo Ciliberto
    http://arxiv.org/abs/1905.13194v1

    • [stat.ML]The Label Complexity of Active Learning from Observational Data
    Songbai Yan, Kamalika Chaudhuri, Tara Javidi
    http://arxiv.org/abs/1905.12791v1

    • [stat.ML]Ultimate Power of Inference Attacks: Privacy Risks of High-Dimensional Models
    Sasi Kumar Murakonda, Reza Shokri, George Theodorakopoulos
    http://arxiv.org/abs/1905.12774v1