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