astro-ph.SR - 太阳和天体物理学恒星
cs.AI - 人工智能 cs.CL - 计算与语言 cs.CR - 加密与安全 cs.CV - 机器视觉与模式识别 cs.CY - 计算与社会 cs.DB - 数据库 cs.DC - 分布式、并行与集群计算 cs.DS - 数据结构与算法 cs.ET - 新兴技术 cs.GR - 计算机图形学 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.NA - 数值分析 cs.NE - 神经与进化计算 cs.NI - 网络和互联网体系结构 cs.OS - 操作系统 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.AS - 语音处理 eess.IV - 图像与视频处理 eess.SP - 信号处理 hep-ph - 高能物理现象学 math.AT - 代数拓扑 math.DG - 微分几何 math.NA - 数值分析 math.OC - 优化与控制 math.ST - 统计理论 physics.comp-ph - 计算物理学 physics.data-an - 数据分析、 统计和概率 q-bio.NC - 神经元与认知 q-fin.ST - 统计金融学 stat.AP - 应用统计 stat.CO - 统计计算 stat.ME - 统计方法论 stat.ML - (统计)机器学习
• [astro-ph.SR]A Curated Image Parameter Dataset from Solar Dynamics Observatory Mission
• [cs.AI]Automated Video Game Testing Using Synthetic and Human-Like Agents
• [cs.AI]Blackbox meets blackbox: Representational Similarity and Stability Analysis of Neural Language Models and Brains
• [cs.AI]Does It Make Sense? And Why? A Pilot Study for Sense Making and Explanation
• [cs.AI]Hierarchical Decision Making by Generating and Following Natural Language Instructions
• [cs.AI]Kandinsky Patterns
• [cs.AI]Pre-training of Graph Augmented Transformers for Medication Recommendation
• [cs.AI]Smoothing Structured Decomposable Circuits
• [cs.CL]A Cross-Sentence Latent Variable Model for Semi-Supervised Text Sequence Matching
• [cs.CL]A Review of Language and Speech Features for Cognitive-Linguistic Assessment
• [cs.CL]A Semi-Supervised Approach for Low-Resourced Text Generation
• [cs.CL]A Study of Feature Extraction techniques for Sentiment Analysis
• [cs.CL]A Survey of Natural Language Generation Techniques with a Focus on Dialogue Systems - Past, Present and Future Directions
• [cs.CL]A computational linguistic study of personal recovery in bipolar disorder
• [cs.CL]Are Girls Neko or Shōjo? Cross-Lingual Alignment of Non-Isomorphic Embeddings with Iterative Normalization
• [cs.CL]Are You Looking? Grounding to Multiple Modalities in Vision-and-Language Navigation
• [cs.CL]Are we there yet? Encoder-decoder neural networks as cognitive models of English past tense inflection
• [cs.CL]Assessing the Ability of Self-Attention Networks to Learn Word Order
• [cs.CL]Back Attention Knowledge Transfer for Low-resource Named Entity Recognition
• [cs.CL]Better Character Language Modeling Through Morphology
• [cs.CL]Boosting Entity Linking Performance by Leveraging Unlabeled Documents
• [cs.CL]Budgeted Policy Learning for Task-Oriented Dialogue Systems
• [cs.CL]ChID: A Large-scale Chinese IDiom Dataset for Cloze Test
• [cs.CL]Coherent Comment Generation for Chinese Articles with a Graph-to-Sequence Model
• [cs.CL]Controllable Paraphrase Generation with a Syntactic Exemplar
• [cs.CL]Curate and Generate: A Corpus and Method for Joint Control of Semantics and Style in Neural NLG
• [cs.CL]Deep Unknown Intent Detection with Margin Loss
• [cs.CL]Distantly Supervised Named Entity Recognition using Positive-Unlabeled Learning
• [cs.CL]Do Neural Dialog Systems Use the Conversation History Effectively? An Empirical Study
• [cs.CL]Domain Adaptation of Neural Machine Translation by Lexicon Induction
• [cs.CL]Domain Adaptive Inference for Neural Machine Translation
• [cs.CL]Dynamically Composing Domain-Data Selection with Clean-Data Selection by “Co-Curricular Learning” for Neural Machine Translation
• [cs.CL]Emotion-Cause Pair Extraction: A New Task to Emotion Analysis in Texts
• [cs.CL]Evaluating Discourse in Structured Text Representations
• [cs.CL]Evaluating Gender Bias in Machine Translation
• [cs.CL]Exploiting Sentential Context for Neural Machine Translation
• [cs.CL]Exploring Phoneme-Level Speech Representations for End-to-End Speech Translation
• [cs.CL]Finding Syntactic Representations in Neural Stacks
• [cs.CL]Fluent Translations from Disfluent Speech in End-to-End Speech Translation
• [cs.CL]From Independent Prediction to Re-ordered Prediction: Integrating Relative Position and Global Label Information to Emotion Cause Identification
• [cs.CL]From Speech Chain to Multimodal Chain: Leveraging Cross-modal Data Augmentation for Semi-supervised Learning
• [cs.CL]From Words to Sentences: A Progressive Learning Approach for Zero-resource Machine Translation with Visual Pivots
• [cs.CL]Gender-preserving Debiasing for Pre-trained Word Embeddings
• [cs.CL]Gendered Ambiguous Pronouns Shared Task: Boosting Model Confidence by Evidence Pooling
• [cs.CL]Global Textual Relation Embedding for Relational Understanding
• [cs.CL]Handling Divergent Reference Texts when Evaluating Table-to-Text Generation
• [cs.CL]HighRES: Highlight-based Reference-less Evaluation of Summarization
• [cs.CL]How Large Are Lions? Inducing Distributions over Quantitative Attributes
• [cs.CL]How multilingual is Multilingual BERT?
• [cs.CL]Improved Zero-shot Neural Machine Translation via Ignoring Spurious Correlations
• [cs.CL]Improving Long Distance Slot Carryover in Spoken Dialogue Systems
• [cs.CL]Joint Effects of Context and User History for Predicting Online Conversation Re-entries
• [cs.CL]Jointly Learning Semantic Parser and Natural Language Generator via Dual Information Maximization
• [cs.CL]KERMIT: Generative Insertion-Based Modeling for Sequences
• [cs.CL]Know More about Each Other: Evolving Dialogue Strategy via Compound Assessment
• [cs.CL]Latent Retrieval for Weakly Supervised Open Domain Question Answering
• [cs.CL]Lattice-Based Transformer Encoder for Neural Machine Translation
• [cs.CL]Learning to Explain: Answering Why-Questions via Rephrasing
• [cs.CL]Massive Styles Transfer with Limited Labeled Data
• [cs.CL]Multi-Task Semantic Dependency Parsing with Policy Gradient for Learning Easy-First Strategies
• [cs.CL]Multi-task Pairwise Neural Ranking for Hashtag Segmentation
• [cs.CL]Multimodal Transformer for Unaligned Multimodal Language Sequences
• [cs.CL]NNE: A Dataset for Nested Named Entity Recognition in English Newswire
• [cs.CL]Optimal coding and the origins of Zipfian laws
• [cs.CL]Phase-based Minimalist Parsing and complexity in non-local dependencies
• [cs.CL]Pitfalls in the Evaluation of Sentence Embeddings
• [cs.CL]Plain English Summarization of Contracts
• [cs.CL]Pretraining Methods for Dialog Context Representation Learning
• [cs.CL]Progressive Self-Supervised Attention Learning for Aspect-Level Sentiment Analysis
• [cs.CL]Promotion of Answer Value Measurement with Domain Effects in Community Question Answering Systems
• [cs.CL]Question Answering as an Automatic Evaluation Metric for News Article Summarization
• [cs.CL]RTHN: A RNN-Transformer Hierarchical Network for Emotion Cause Extraction
• [cs.CL]Recognising Agreement and Disagreement between Stances with Reason Comparing Networks
• [cs.CL]Relation Embedding with Dihedral Group in Knowledge Graph
• [cs.CL]Relational Word Embeddings
• [cs.CL]Resolving Gendered Ambiguous Pronouns with BERT
• [cs.CL]Robust Sequence-to-Sequence Acoustic Modeling with Stepwise Monotonic Attention for Neural TTS
• [cs.CL]Self-Attentional Models for Lattice Inputs
• [cs.CL]Semantically Constrained Multilayer Annotation: The Case of Coreference
• [cs.CL]Sentiment Tagging with Partial Labels using Modular Architectures
• [cs.CL]Sequence Tagging with Contextual and Non-Contextual Subword Representations: A Multilingual Evaluation
• [cs.CL]Sequential Neural Networks as Automata
• [cs.CL]ShEMO — A Large-Scale Validated Database for Persian Speech Emotion Detection
• [cs.CL]SherLIiC: A Typed Event-Focused Lexical Inference Benchmark for Evaluating Natural Language Inference
• [cs.CL]Simultaneous Translation with Flexible Policy via Restricted Imitation Learning
• [cs.CL]System Demo for Transfer Learning across Vision and Text using Domain Specific CNN Accelerator for On-Device NLP Applications
• [cs.CL]TalkSumm: A Dataset and Scalable Annotation Method for Scientific Paper Summarization Based on Conference Talks
• [cs.CL]Task-Guided Pair Embedding in Heterogeneous Network
• [cs.CL]The PhotoBook Dataset: Building Common Ground through Visually-Grounded Dialogue
• [cs.CL]Toward Grammatical Error Detection from Sentence Labels: Zero-shot Sequence Labeling with CNNs and Contextualized Embeddings
• [cs.CL]Tracing Antisemitic Language Through Diachronic Embedding Projections: France 1789-1914
• [cs.CL]Training Neural Machine Translation To Apply Terminology Constraints
• [cs.CL]Training Neural Response Selection for Task-Oriented Dialogue Systems
• [cs.CL]Transcoding compositionally: using attention to find more generalizable solutions
• [cs.CL]Transferable Neural Projection Representations
• [cs.CL]Transforming Complex Sentences into a Semantic Hierarchy
• [cs.CL]Unsupervised Bilingual Lexicon Induction from Mono-lingual Multimodal Data
• [cs.CR]DAWN: Dynamic Adversarial Watermarking of Neural Networks
• [cs.CR]Encryption Scheme Based on Expanded Reed-Solomon Codes
• [cs.CR]Generative Adversarial Networks for Distributed Intrusion Detection in the Internet of Things
• [cs.CR]Human-Usable Password Schemas: Beyond Information-Theoretic Security
• [cs.CR]New non-linearity parameters of Boolean functions
• [cs.CR]Privacy-preserving Crowd-guided AI Decision-making in Ethical Dilemmas
• [cs.CR]Towards Fair and Decentralized Privacy-Preserving Deep Learning with Blockchain
• [cs.CV]3D Appearance Super-Resolution with Deep Learning
• [cs.CV]A Closed-form Solution to Universal Style Transfer
• [cs.CV]A Hybrid RNN-HMM Approach for Weakly Supervised Temporal Action Segmentation
• [cs.CV]Active Object Manipulation Facilitates Visual Object Learning: An Egocentric Vision Study
• [cs.CV]Adversarial Examples for Edge Detection: They Exist, and They Transfer
• [cs.CV]An Adaptive Training-less System for Anomaly Detection in Crowd Scenes
• [cs.CV]Automated Steel Bar Counting and Center Localization with Convolutional Neural Networks
• [cs.CV]Automatic Health Problem Detection from Gait Videos Using Deep Neural Networks
• [cs.CV]Color Constancy Convolutional Autoencoder
• [cs.CV]Comparing two- and three-view Computer Vision
• [cs.CV]Computing Valid p-values for Image Segmentation by Selective Inference
• [cs.CV]Content Adaptive Optimization for Neural Image Compression
• [cs.CV]Cross-Domain Cascaded Deep Feature Translation
• [cs.CV]Data Augmentation for Object Detection via Progressive and Selective Instance-Switching
• [cs.CV]Deep Face Recognition Model Compression via Knowledge Transfer and Distillation
• [cs.CV]Deeply-supervised Knowledge Synergy
• [cs.CV]Depth-Preserving Real-Time Arbitrary Style Transfer
• [cs.CV]Disentangling neural mechanisms for perceptual grouping
• [cs.CV]Dominant Set Clustering and Pooling for Multi-View 3D Object Recognition
• [cs.CV]DualDis: Dual-Branch Disentangling with Adversarial Learning
• [cs.CV]Dynamic Neural Network Decoupling
• [cs.CV]End-to-End Learning of Geometric Deformations of Feature Maps for Virtual Try-On
• [cs.CV]Evaluation of an AI system for the automated detection of glaucoma from stereoscopic optic disc photographs: the European Optic Disc Assessment Study
• [cs.CV]Example-Guided Style Consistent Image Synthesis from Semantic Labeling
• [cs.CV]Exploiting Offset-guided Network for Pose Estimation and Tracking
• [cs.CV]Face Parsing with RoI Tanh-Warping
• [cs.CV]Fashion Editing with Multi-scale Attention Normalization
• [cs.CV]GazeCorrection:Self-Guided Eye Manipulation in the wild using Self-Supervised Generative Adversarial Networks
• [cs.CV]Generating Question Relevant Captions to Aid Visual Question Answering
• [cs.CV]Hierarchical Video Frame Sequence Representation with Deep Convolutional Graph Network
• [cs.CV]How Much Does Audio Matter to Recognize Egocentric Object Interactions?
• [cs.CV]Incremental Few-Shot Learning for Pedestrian Attribute Recognition
• [cs.CV]Iterative Path Reconstruction for Large-Scale Inertial Navigation on Smartphones
• [cs.CV]KarNet: An Efficient Boolean Function Simplifier
• [cs.CV]Learning Object Bounding Boxes for 3D Instance Segmentation on Point Clouds
• [cs.CV]Learning Rotation Adaptive Correlation Filters in Robust Visual Object Tracking
• [cs.CV]Learning to Generate Grounded Image Captions without Localization Supervision
• [cs.CV]Learning to Self-Train for Semi-Supervised Few-Shot Classification
• [cs.CV]Localization in Aerial Imagery with Grid Maps using LocGAN
• [cs.CV]Masked Non-Autoregressive Image Captioning
• [cs.CV]Mining YouTube - A dataset for learning fine-grained action concepts from webly supervised video data
• [cs.CV]Natural Vocabulary Emerges from Free-Form Annotations
• [cs.CV]Panoptic Edge Detection
• [cs.CV]Parametric Shape Modeling and Skeleton Extraction with Radial Basis Functions using Similarity Domains Network
• [cs.CV]Perceptual Embedding Consistency for Seamless Reconstruction of Tilewise Style Transfer
• [cs.CV]Photo-Geometric Autoencoding to Learn 3D Objects from Unlabelled Images
• [cs.CV]RF-Net: An End-to-End Image Matching Network based on Receptive Field
• [cs.CV]RGB and LiDAR fusion based 3D Semantic Segmentation for Autonomous Driving
• [cs.CV]Random Path Selection for Incremental Learning
• [cs.CV]Reconstruct and Represent Video Contents for Captioning via Reinforcement Learning
• [cs.CV]Relational Reasoning using Prior Knowledge for Visual Captioning
• [cs.CV]Resolving Overlapping Convex Objects in Silhouette Images by Concavity Analysis and Gaussian Process
• [cs.CV]Rethinking Loss Design for Large-scale 3D Shape Retrieval
• [cs.CV]Robust copy-move forgery detection by false alarms control
• [cs.CV]Scene Representation Networks: Continuous 3D-Structure-Aware Neural Scene Representations
• [cs.CV]Selective Style Transfer for Text
• [cs.CV]Semi- and Weakly-supervised Human Pose Estimation
• [cs.CV]Separate from Observation: Unsupervised Single Image Layer Separation
• [cs.CV]State-aware Re-identification Feature for Multi-target Multi-camera Tracking
• [cs.CV]Style Transfer With Adaptation to the Central Objects of the Scene
• [cs.CV]Text-based Editing of Talking-head Video
• [cs.CV]The iMet Collection 2019 Challenge Dataset
• [cs.CV]Towards better Validity: Dispersion based Clustering for Unsupervised Person Re-identification
• [cs.CV]Transfer Learning with intelligent training data selection for prediction of Alzheimer’s Disease
• [cs.CV]Triangulation Learning Network: from Monocular to Stereo 3D Object Detection
• [cs.CV]Visual Diagnosis of Dermatological Disorders: Human and Machine Performance
• [cs.CV]Visual Tree Convolutional Neural Network in Image Classification
• [cs.CV]Zero-Shot Semantic Segmentation
• [cs.CV]cGANs with Conditional Convolution Layer
• [cs.CY]Apprentissage de la pensée informatique : de la formation des enseignant$\cdot$e$\cdot$s à la formation de tou$\cdot$te$\cdot$s les citoyen$\cdot$ne$\cdot$s
• [cs.CY]Grade prediction with course and student specific models
• [cs.CY]Peut-on former les enseignant$\cdot$e$\cdot$s en un rien de temps ?
• [cs.DB]Efficient Algorithms for Densest Subgraph Discovery
• [cs.DB]Motivo: fast motif counting via succinct color coding and adaptive sampling
• [cs.DC]A Hybrid Cache Architecture for Meeting Per-Tenant Performance Goals in a Private Cloud
• [cs.DC]A Technique for Finding Optimal Program Launch Parameters Targeting Manycore Accelerators
• [cs.DC]Assessing Performance Implications of Deep Copy Operations via Microbenchmarking
• [cs.DC]Mutable Locks: Combining the Best of Spin and Sleep Locks
• [cs.DC]Optimal Register Construction in M&M Systems
• [cs.DC]Patterns for Blockchain Migration
• [cs.DC]Probabilistic Top-k Dominating Query Monitoring over Multiple Uncertain IoT Data Streams in Edge Computing Environments
• [cs.DC]Proximity Neighbor Selection in Blockchain Networks
• [cs.DC]Reconfigurable Atomic Transaction Commit (Extended Version)
• [cs.DS]A Direct $\tilde{O}(1/ε)$ Iteration Parallel Algorithm for Optimal Transport
• [cs.DS]Cores and Other Dense Structures in Complex Networks
• [cs.DS]On the Use of Randomness in Local Distributed Graph Algorithms
• [cs.ET]In-memory hyperdimensional computing
• [cs.GR]3D Magic Mirror: Automatic Video to 3D Caricature Translation
• [cs.HC]An Extensive Review of Computational Dance Automation Techniques and Applications
• [cs.IR]Contextually Propagated Term Weights for Document Representation
• [cs.IR]Federated Hierarchical Hybrid Networks for Clickbait Detection
• [cs.IR]Incorporating System-Level Objectives into Recommender Systems
• [cs.IR]Mining Data from the Congressional Record
• [cs.IR]Personalized Multimedia Item and Key Frame Recommendation
• [cs.IR]Sequential Scenario-Specific Meta Learner for Online Recommendation
• [cs.IR]Technology Knowledge Graph Based on Patent Data
• [cs.IR]Unsupervised Neural Generative Semantic Hashing
• [cs.IR]User Profile Feature-Based Approach to Address the Cold Start Problem in Collaborative Filtering for Personalized Movie Recommendation
• [cs.IT]Closed-Form Analysis of Non-Linear Age-of-Information in Status Updates with an Energy Harvesting Transmitter
• [cs.IT]Cooperative Downlink Interference Transmission and Cancellation for Cellular-Connected UAV: A Divide-and-Conquer Approach
• [cs.IT]Harvest-or-Transmit Policy for Cognitive Radio Networks: A Learning Theoretic Approach
• [cs.IT]Multi-dimensional Spectral Super-Resolution with Prior Knowledge via Frequency-Selective Vandermonde Decomposition and ADMM
• [cs.IT]Performance Evaluation for the Co-existence of eMBB and URLLC Networks: Synchronized versus Unsynchronized TDD
• [cs.IT]Probabilistic Existence Results for Parent-Identifying Schemes
• [cs.IT]The Classical Capacity of a Quantum Erasure Queue-Channel
• [cs.IT]Transmit Power Policy and Ergodic Multicast Rate Analysis of Cognitive Radio Networks in Generalized Fading
• [cs.IT]Two Classes of New MDS Self-dual Codes over Finite Fields
• [cs.LG]A Case for Backward Compatibility for Human-AI Teams
• [cs.LG]A Fast-Optimal Guaranteed Algorithm For Learning Sub-Interval Relationships in Time Series
• [cs.LG]A Language-Agnostic Model for Semantic Source Code Labeling
• [cs.LG]A Novel Hyperparameter-free Approach to Decision Tree Construction that Avoids Overfitting by Design
• [cs.LG]A Perspective on Objects and Systematic Generalization in Model-Based RL
• [cs.LG]A Strong and Robust Baseline for Text-Image Matching
• [cs.LG]A necessary and sufficient stability notion for adaptive generalization
• [cs.LG]Achieving Fairness in Determining Medicaid Eligibility through Fairgroup Construction
• [cs.LG]Achieving Generalizable Robustness of Deep Neural Networks by Stability Training
• [cs.LG]Active Learning for Binary Classification with Abstention
• [cs.LG]Adaptive Online Learning for Gradient-Based Optimizers
• [cs.LG]Adversarial Exploitation of Policy Imitation
• [cs.LG]Adversarial Risk Bounds for Neural Networks through Sparsity based Compression
• [cs.LG]Adversarial Training Generalizes Data-dependent Spectral Norm Regularization
• [cs.LG]Adversarially Robust Generalization Just Requires More Unlabeled Data
• [cs.LG]An Adaptable Framework for Deep Adversarial Label Learning from Weak Supervision
• [cs.LG]An Efficient Graph Convolutional Network Technique for the Travelling Salesman Problem
• [cs.LG]An Empirical Study on Hyperparameters and their Interdependence for RL Generalization
• [cs.LG]An interpretable machine learning framework for modelling human decision behavior
• [cs.LG]Analysis and Improvement of Adversarial Training in DQN Agents With Adversarially-Guided Exploration (AGE)
• [cs.LG]Approximation capability of neural networks on spaces of probability measures and tree-structured domains
• [cs.LG]Architecture Selection via the Trade-off Between Accuracy and Robustness
• [cs.LG]Attributed Graph Clustering via Adaptive Graph Convolution
• [cs.LG]Autonomous Reinforcement Learning of Multiple Interrelated Tasks
• [cs.LG]Big-Data Clustering: K-Means or K-Indicators?
• [cs.LG]Biomedical Named Entity Recognition via Reference-Set Augmented Bootstrapping
• [cs.LG]Blow: a single-scale hyperconditioned flow for non-parallel raw-audio voice conversion
• [cs.LG]Breaking Inter-Layer Co-Adaptation by Classifier Anonymization
• [cs.LG]C2P2: A Collective Cryptocurrency Up/Down Price Prediction Engine
• [cs.LG]Characterizing and Forecasting User Engagement with In-app Action Graph: A Case Study of Snapchat
• [cs.LG]Classification of Crop Tolerance to Heat and Drought: A Deep Convolutional Neural Networks Approach
• [cs.LG]Clustering by Orthogonal NMF Model and Non-Convex Penalty Optimization
• [cs.LG]Conditional Generative Models are not Robust
• [cs.LG]Continual Learning of New Sound Classes using Generative Replay
• [cs.LG]Continual learning with hypernetworks
• [cs.LG]Cooperative neural networks (CoNN): Exploiting prior independence structure for improved classification
• [cs.LG]Correctness Verification of Neural Networks
• [cs.LG]Coupled VAE: Improved Accuracy and Robustness of a Variational Autoencoder
• [cs.LG]DANE: Domain Adaptive Network Embedding
• [cs.LG]Data Sampling for Graph Based Unsupervised Learning: Convex and Greedy Optimization
• [cs.LG]Data-driven Estimation of Sinusoid Frequencies
• [cs.LG]Deep Reasoning Networks: Thinking Fast and Slow
• [cs.LG]DiffQue: Estimating Relative Difficulty of Questions in Community Question Answering Services
• [cs.LG]Dimensionality compression and expansion in Deep Neural Networks
• [cs.LG]Discovering Neural Wirings
• [cs.LG]Discriminative adversarial networks for positive-unlabeled learning
• [cs.LG]Disparate Vulnerability: on the Unfairness of Privacy Attacks Against Machine Learning
• [cs.LG]Distribution oblivious, risk-aware algorithms for multi-armed bandits with unbounded rewards
• [cs.LG]Do place cells dream of conditional probabilities? Learning Neural Nyström representations
• [cs.LG]Ease.ml/meter: Quantitative Overfitting Management for Human-in-the-loop ML Application Development
• [cs.LG]Embedded hyper-parameter tuning by Simulated Annealing
• [cs.LG]Encoder-Powered Generative Adversarial Networks
• [cs.LG]Encoding Invariances in Deep Generative Models
• [cs.LG]Enhancing Transformation-based Defenses using a Distribution Classifier
• [cs.LG]Episodic Memory in Lifelong Language Learning
• [cs.LG]Evaluating Scalable Bayesian Deep Learning Methods for Robust Computer Vision
• [cs.LG]Exact Combinatorial Optimization with Graph Convolutional Neural Networks
• [cs.LG]Exact inference in structured prediction
• [cs.LG]Factor Graph Neural Network
• [cs.LG]Fast and Stable Interval Bounds Propagation for Training Verifiably Robust Models
• [cs.LG]Feature-Based Q-Learning for Two-Player Stochastic Games
• [cs.LG]Gated recurrent units viewed through the lens of continuous time dynamical systems
• [cs.LG]Generating Diverse High-Fidelity Images with VQ-VAE-2
• [cs.LG]Generative Adversarial Networks: A Survey and Taxonomy
• [cs.LG]Graduated Optimization of Black-Box Functions
• [cs.LG]HERA: Partial Label Learning by Combining Heterogeneous Loss with Sparse and Low-Rank Regularization
• [cs.LG]Hierarchical Auxiliary Learning
• [cs.LG]Implicit Regularization in Deep Matrix Factorization
• [cs.LG]Information Competing Process for Learning Diversified Representations
• [cs.LG]Inverse boosting pruning trees for depression detection on Twitter
• [cs.LG]Kernel Instrumental Variable Regression
• [cs.LG]Learner-aware Teaching: Inverse Reinforcement Learning with Preferences and Constraints
• [cs.LG]Learning Attention-based Embeddings for Relation Prediction in Knowledge Graphs
• [cs.LG]Learning Domain Randomization Distributions for Transfer of Locomotion Policies
• [cs.LG]Learning Interpretable Shapelets for Time Series Classification through Adversarial Regularization
• [cs.LG]Learning Representations by Maximizing Mutual Information Across Views
• [cs.LG]Learning Transferable Cooperative Behavior in Multi-Agent Teams
• [cs.LG]Learning low-dimensional state embeddings and metastable clusters from time series data
• [cs.LG]Learning to Clear the Market
• [cs.LG]Load Balancing for Ultra-Dense Networks: A Deep Reinforcement Learning Based Approach
• [cs.LG]Low-rank Random Tensor for Bilinear Pooling
• [cs.LG]Metric Learning for Individual Fairness
• [cs.LG]Minimax bounds for structured prediction
• [cs.LG]Model selection for contextual bandits
• [cs.LG]Modular Universal Reparameterization: Deep Multi-task Learning Across Diverse Domains
• [cs.LG]Near-Optimal Online Egalitarian learning in General Sum Repeated Matrix Games
• [cs.LG]Nemesyst: A Hybrid Parallelism Deep Learning-Based Framework Applied for Internet of Things Enabled Food Retailing Refrigeration Systems
• [cs.LG]Neural Network-based Object Classification by Known and Unknown Features (Based on Text Queries)
• [cs.LG]NeuralDivergence: Exploring and Understanding Neural Networks by Comparing Activation Distributions
• [cs.LG]NodeDrop: A Condition for Reducing Network Size without Effect on Output
• [cs.LG]Nonstochastic Multiarmed Bandits with Unrestricted Delays
• [cs.LG]Off-Policy Evaluation via Off-Policy Classification
• [cs.LG]On Gradient Descent Ascent for Nonconvex-Concave Minimax Problems
• [cs.LG]On Privacy Protection of Latent Dirichlet Allocation Model Training
• [cs.LG]On The Radon—Nikodym Spectral Approach With Optimal Clustering
• [cs.LG]On the Correctness and Sample Complexity of Inverse Reinforcement Learning
• [cs.LG]On the computational complexity of the probabilistic label tree algorithms
• [cs.LG]One-Way Prototypical Networks
• [cs.LG]Optimal Learning of Mallows Block Model
• [cs.LG]Optimal Transport on the Manifold of SPD Matrices for Domain Adaptation
• [cs.LG]Optimal Unsupervised Domain Translation
• [cs.LG]Options as responses: Grounding behavioural hierarchies in multi-agent RL
• [cs.LG]PCA-driven Hybrid network design for enabling Intelligence at the Edge
• [cs.LG]Posterior Variance Analysis of Gaussian Processes with Application to Average Learning Curves
• [cs.LG]Proximal Reliability Optimization for Reinforcement Learning
• [cs.LG]Quantifying Point-Prediction Uncertainty in Neural Networks via Residual Estimation with an I/O Kernel
• [cs.LG]RL-Based Method for Benchmarking the Adversarial Resilience and Robustness of Deep Reinforcement Learning Policies
• [cs.LG]Radial-Based Undersampling for Imbalanced Data Classification
• [cs.LG]Robust Learning Under Label Noise With Iterative Noise-Filtering
• [cs.LG]Self-supervised Body Image Acquisition Using a Deep Neural Network for Sensorimotor Prediction
• [cs.LG]Sequential Triggers for Watermarking of Deep Reinforcement Learning Policies
• [cs.LG]Sparse Representation Classification via Screening for Graphs
• [cs.LG]Stabilizing Off-Policy Q-Learning via Bootstrapping Error Reduction
• [cs.LG]Statistically Significant Discriminative Patterns Searching
• [cs.LG]Super-resolution of Time-series Labels for Bootstrapped Event Detection
• [cs.LG]Temporal Density Extrapolation using a Dynamic Basis Approach
• [cs.LG]The Convergence Rate of Neural Networks for Learned Functions of Different Frequencies
• [cs.LG]The Intrinsic Robustness of Stochastic Bandits to Strategic Manipulation
• [cs.LG]The Principle of Unchanged Optimality in Reinforcement Learning Generalization
• [cs.LG]Topological Autoencoders
• [cs.LG]Toward Building Conversational Recommender Systems: A Contextual Bandit Approach
• [cs.LG]Towards Interactive Training of Non-Player Characters in Video Games
• [cs.LG]Truncated Cauchy Non-negative Matrix Factorization
• [cs.LG]Uniform Error Bounds for Gaussian Process Regression with Application to Safe Control
• [cs.LG]Unsupervised Emergence of Egocentric Spatial Structure from Sensorimotor Prediction
• [cs.LG]Using a Logarithmic Mapping to Enable Lower Discount Factors in Reinforcement Learning
• [cs.LG]Wasserstein Weisfeiler-Lehman Graph Kernels
• [cs.LG]Weakly Supervised Disentanglement by Pairwise Similarities
• [cs.LG]Y-GAN: A Generative Adversarial Network for Depthmap Estimation from Multi-camera Stereo Images
• [cs.LO]Reasoning about disclosure in data integration in the presence of source constraints
• [cs.MA]Multiple Drones driven Hexagonally Partitioned Area Exploration: Simulation and Evaluation
• [cs.NA]Learning Neural PDE Solvers with Convergence Guarantees
• [cs.NE]Hamiltonian Neural Networks
• [cs.NE]Kinetic Market Model: An Evolutionary Algorithm
• [cs.NE]Multi-objective Pruning for CNNs using Genetic Algorithm
• [cs.NE]Neural networks grown and self-organized by noise
• [cs.NE]Push and Pull Search Embedded in an M2M Framework for Solving Constrained Multi-objective Optimization Problems
• [cs.NE]SpikeGrad: An ANN-equivalent Computation Model for Implementing Backpropagation with Spikes
• [cs.NE]Training Detection-Range-Frugal Cooperative Collision Avoidance Models for Quadcopters via Neuroevolution
• [cs.NI]Blockchain for Internet of Things: A Survey
• [cs.NI]Cellular Traffic Prediction and Classification: a comparative evaluation of LSTM and ARIMA
• [cs.NI]On Provisioning Cellular Networks for Distributed Inference
• [cs.NI]Probabilistic QoS-aware Placement of VNF chains at the Edge
• [cs.OS]Cache Contention on Multicore Systems: An Ontology-based Approach
• [cs.RO]Air Learning: An AI Research Platform for Algorithm-Hardware Benchmarking of Autonomous Aerial Robots
• [cs.RO]Analysis of Obstacle based Probabilistic RoadMap Method using Geometric Probability
• [cs.RO]Closed-Loop Control of a Delta-Wing Unmanned Aerial-Aquatic Vehicle
• [cs.RO]Effects of Different Hand-Grounding Locations on Haptic Performance With a Wearable Kinesthetic Haptic Device
• [cs.RO]GAMMA: A General Agent Motion Prediction Model for Autonomous Driving
• [cs.RO]Grid-based Localization Stack for Inspection Drones towards Automation of Large Scale Warehouse Systems
• [cs.RO]Harnessing Reinforcement Learning for Neural Motion Planning
• [cs.RO]Knowledge is Never Enough: Towards Web Aided Deep Open World Recognition
• [cs.RO]Localization Requirements for Autonomous Vehicles
• [cs.RO]Longitudinal Trajectory Prediction of Human-driven Vehicles Near Traffic Lights
• [cs.RO]Rapidly-Exploring Quotient-Space Trees: Motion Planning using Sequential Simplifications
• [cs.RO]Socially Inspired Communication in Swarm Robotics
• [cs.RO]Vision-Based Autonomous UAV Navigation and Landing for Urban Search and Rescue
• [cs.SD]A Surprising Density of Illusionable Natural Speech
• [cs.SE]Gamification of Enterprise Systems: A Synthesis of Mechanics, Dynamics, and Risks
• [cs.SE]Neural Bug Finding: A Study of Opportunities and Challenges
• [cs.SI]Can Women Break the Glass Ceiling?: An Analysis of #MeToo Hashtagged Posts on Twitter
• [cs.SI]Evaluating network partitions through visualization
• [cs.SI]Implication Avoiding Dynamics for Externally Observed Networks
• [cs.SI]Need for Critical Cyber Defence, Security Strategy and Privacy Policy in Bangladesh - Hype or Reality?
• [cs.SI]The Strength of Structural Diversity in Online Social Networks
• [cs.SI]Understanding the Silence of Sexual Harassment Victims Through the #WhyIDidntReport Movement
• [cs.SY]Robust stability of moving horizon estimation for nonlinear systems with bounded disturbances using adaptive arrival cost
• [eess.AS]Evaluating Non-aligned Musical Score Transcriptions with MV2H
• [eess.AS]MelNet: A Generative Model for Audio in the Frequency Domain
• [eess.IV]A Semantic-based Medical Image Fusion Approach
• [eess.IV]Deep Feature Learning from a Hospital-Scale Chest X-ray Dataset with Application to TB Detection on a Small-Scale Dataset
• [eess.IV]Learning Deep Image Priors for Blind Image Denoising
• [eess.IV]Lung cancer screening with low-dose CT scans using a deep learning approach
• [eess.IV]Natural Image Noise Dataset
• [eess.IV]Probabilistic Noise2Void: Unsupervised Content-Aware Denoising
• [eess.SP]A Nonlinear Acceleration Method for Iterative Algorithms
• [eess.SP]Deep Reinforcement Learning Architecture for Continuous Power Allocation in High Throughput Satellites
• [eess.SP]Deterministic and stochastic damage detection via dynamic response analysis
• [eess.SP]Gridless Variational Bayesian Channel Estimation for Antenna Array Systems with Low Resolution ADCs
• [eess.SP]Sparse Bayesian Learning Approach for Discrete Signal Reconstruction
• [eess.SP]What, Where and How to Transfer in SAR Target Recognition Based on Deep CNNs
• [hep-ph]Effective LHC measurements with matrix elements and machine learning
• [math.AT]A numerical measure of the instability of Mapper-type algorithms
• [math.DG]Optimal transport and information geometry
• [math.NA]Exploiting nested task-parallelism in the $\mathcal{H}-LU$ factorization
• [math.OC]A Generic Acceleration Framework for Stochastic Composite Optimization
• [math.OC]Adaptive Model Refinement with Batch Bayesian Sampling for Optimization of Bio-inspired Flow Tailoring
• [math.OC]Data-Pooling in Stochastic Optimization
• [math.OC]Generalized Momentum-Based Methods: A Hamiltonian Perspective
• [math.OC]Higher-Order Accelerated Methods for Faster Non-Smooth Optimization
• [math.OC]Proximal Point Approximations Achieving a Convergence Rate of $\mathcal{O}(1/k)$ for Smooth Convex-Concave Saddle Point Problems: Optimistic Gradient and Extra-gradient Methods
• [math.OC]Robust exploration in linear quadratic reinforcement learning
• [math.OC]Towards Unified Acceleration of High-Order Algorithms under Hölder Continuity and Uniform Convexity
• [math.ST]A mean-field limit for certain deep neural networks
• [math.ST]A test against trend in random sequences
• [math.ST]Asymptotic Properties of Neural Network Sieve Estimators
• [math.ST]Confidence Regions in Wasserstein Distributionally Robust Estimation
• [math.ST]How many variables should be entered in a principal component regression equation?
• [math.ST]Inference robust to outliers with l1-norm penalization
• [math.ST]Multi-reference factor analysis: low-rank covariance estimation under unknown translations
• [math.ST]On Testing for Parameters in Ising Models
• [math.ST]Robust Mean Estimation with the Bayesian Median of Means
• [math.ST]Semiparametric Analysis of the Proportional Likelihood Ratio Model and Omnibus Estimation Procedure
• [physics.comp-ph]A new nonlocal forward model for diffuse optical tomography
• [physics.data-an]Revision of ISO 19229 to support the certification of calibration gases for purity
• [q-bio.NC]A detailed study of recurrent neural networks used to model tasks in the cerebral cortex
• [q-bio.NC]Learning to solve the credit assignment problem
• [q-bio.NC]Signal Coding and Perfect Reconstruction using Spike Trains
• [q-fin.ST]Conditional inference on the asset with maximum Sharpe ratio
• [stat.AP]A Dyadic IRT Model
• [stat.AP]Encouraging Equitable Bikeshare: Implications of Docked and Dockless Models for Spatial Equity
• [stat.AP]Generalised linear models for prognosis and intervention: Theory, practice, and implications for machine learning
• [stat.AP]Joint spatial modeling of significant wave height and wave period using the SPDE approach
• [stat.AP]Model Trees for Personalization
• [stat.AP]Statistical analysis of the water level of Huang He river (Yellow river) in China
• [stat.AP]Stress Testing Network Reconstruction via Graphical Causal Mode
• [stat.CO]lspartition: Partitioning-Based Least Squares Regression
• [stat.CO]nprobust: Nonparametric Kernel-Based Estimation and Robust Bias-Corrected Inference
• [stat.ME]Anchored Causal Inference in the Presence of Measurement Error
• [stat.ME]Bayesian Profiling Multiple Imputation for Missing Electronic Health Records
• [stat.ME]Central Quantile Subspace
• [stat.ME]Clustering Multivariate Data using Factor Analytic Bayesian Mixtures with an Unknown Number of Components
• [stat.ME]Combining Heterogeneous Spatial Datasets with Process-based Spatial Fusion Models: A Unifying Framework
• [stat.ME]Comprehensive cluster validity Index based on structural simplicity
• [stat.ME]Confidence Intervals for Selected Parameters
• [stat.ME]Copula-based functional Bayes classification with principal components and partial least squares
• [stat.ME]Covariate-Powered Empirical Bayes Estimation
• [stat.ME]Diagonally-Dominant Principal Component Analysis
• [stat.ME]Estimating Real Log Canonical Thresholds
• [stat.ME]Estimating Time-Varying Causal Excursion Effect in Mobile Health with Binary Outcomes
• [stat.ME]Functional time series prediction under partial observation of the future curve
• [stat.ME]Gap-Measure Tests with Applications to Data Integrity Verification
• [stat.ME]Generating Poisson-Distributed Differentially Private Synthetic Data
• [stat.ME]Kernel Mean Embedding Based Hypothesis Tests for Comparing Spatial Point Patterns
• [stat.ME]Mixture of hidden Markov models for accelerometer data
• [stat.ME]Multiplicative Effect Modeling: The General Case
• [stat.ME]Partial and semi-partial measures of spatial associations for multivariate lattice data
• [stat.ME]Semi-parametric Bayesian variable selection for gene-environment interactions
• [stat.ME]Statistical methods for biomarker data pooled from multiple nested case-control studies
• [stat.ME]The performance of the partially overlapping samples t-tests at the limits
• [stat.ME]Transformed Central Quantile Subspace
• [stat.ME]Unconstrained representation of orthogonal matrices with application to common principle components
• [stat.ML]A Mean Field Theory of Quantized Deep Networks: The Quantization-Depth Trade-Off
• [stat.ML]An Evaluation Toolkit to Guide Model Selection and Cohort Definition in Causal Inference
• [stat.ML]Assessing Algorithmic Fairness with Unobserved Protected Class Using Data Combination
• [stat.ML]Assessing Disparate Impacts of Personalized Interventions: Identifiability and Bounds
• [stat.ML]Bayesian Optimization of Composite Functions
• [stat.ML]Bayesian Prior Networks with PAC Training
• [stat.ML]BreGMN: scaled-Bregman Generative Modeling Networks
• [stat.ML]Capabilities and Limitations of Time-lagged Autoencoders for Slow Mode Discovery in Dynamical Systems
• [stat.ML]Concept Tree: High-Level Representation of Variables for More Interpretable Surrogate Decision Trees
• [stat.ML]Deep ReLU Networks Have Surprisingly Few Activation Patterns
• [stat.ML]GANchors: Realistic Image Perturbation Distributions for Anchors Using Generative Models
• [stat.ML]Graphon Estimation from Partially Observed Network Data
• [stat.ML]Hybrid Machine Learning Forecasts for the FIFA Women’s World Cup 2019
• [stat.ML]Learning Perceptually-Aligned Representations via Adversarial Robustness
• [stat.ML]MEMe: An Accurate Maximum Entropy Method for Efficient Approximations in Large-Scale Machine Learning
• [stat.ML]MaxGap Bandit: Adaptive Algorithms for Approximate Ranking
• [stat.ML]Nonparametric Functional Approximation with Delaunay Triangulation
• [stat.ML]Robust Gaussian Process Regression for Real-Time High Precision GPS Signal Enhancement
• [stat.ML]Robust approximate linear regression without correspondence
• [stat.ML]Separable Layers Enable Structured Efficient Linear Substitutions
• [stat.ML]Streaming Variational Monte Carlo
• [stat.ML]Symmetry-adapted generation of 3d point sets for the targeted discovery of molecules
• [stat.ML]Tensor Restricted Isometry Property Analysis For a Large Class of Random Measurement Ensembles
• [stat.ML]The Extended Dawid-Skene Model: Fusing Information from Multiple Data Schemas
• [stat.ML]Towards Task and Architecture-Independent Generalization Gap Predictors
• [stat.ML]Universal Boosting Variational Inference
• [stat.ML]What do AI algorithms actually learn? - On false structures in deep learning
·····································
• [astro-ph.SR]A Curated Image Parameter Dataset from Solar Dynamics Observatory Mission
Azim Ahmadzadeh, Dustin J. Kempton, Rafal A. Angryk
http://arxiv.org/abs/1906.01062v1
• [cs.AI]Automated Video Game Testing Using Synthetic and Human-Like Agents
Sinan Ariyurek, Aysu Betin-Can, Elif Surer
http://arxiv.org/abs/1906.00317v1
• [cs.AI]Blackbox meets blackbox: Representational Similarity and Stability Analysis of Neural Language Models and Brains
Samira Abnar, Lisa Beinborn, Rochelle Choenni, Willem Zuidema
http://arxiv.org/abs/1906.01539v1
• [cs.AI]Does It Make Sense? And Why? A Pilot Study for Sense Making and Explanation
Cunxiang Wang, Shuailong Liang, Yue Zhang, Xiaonan Li, Tian Gao
http://arxiv.org/abs/1906.00363v1
• [cs.AI]Hierarchical Decision Making by Generating and Following Natural Language Instructions
Hengyuan Hu, Denis Yarats, Qucheng Gong, Yuandong Tian, Mike Lewis
http://arxiv.org/abs/1906.00744v1
• [cs.AI]Kandinsky Patterns
Heimo Mueller, Andreas Holzinger
http://arxiv.org/abs/1906.00657v1
• [cs.AI]Pre-training of Graph Augmented Transformers for Medication Recommendation
Junyuan Shang, Tengfei Ma, Cao Xiao, Jimeng Sun
http://arxiv.org/abs/1906.00346v1
• [cs.AI]Smoothing Structured Decomposable Circuits
Andy Shih, Guy Van den Broeck, Paul Beame, Antoine Amarilli
http://arxiv.org/abs/1906.00311v1
• [cs.CL]A Cross-Sentence Latent Variable Model for Semi-Supervised Text Sequence Matching
Jihun Choi, Taeuk Kim, Sang-goo Lee
http://arxiv.org/abs/1906.01343v1
• [cs.CL]A Review of Language and Speech Features for Cognitive-Linguistic Assessment
Rohit Voleti, Julie M. Liss, Visar Berisha
http://arxiv.org/abs/1906.01157v1
• [cs.CL]A Semi-Supervised Approach for Low-Resourced Text Generation
Hongyu Zang, Xiaojun Wan
http://arxiv.org/abs/1906.00584v1
• [cs.CL]A Study of Feature Extraction techniques for Sentiment Analysis
Avinash Madasu, Sivasankar E
http://arxiv.org/abs/1906.01573v1
• [cs.CL]A Survey of Natural Language Generation Techniques with a Focus on Dialogue Systems - Past, Present and Future Directions
Sashank Santhanam, Samira Shaikh
http://arxiv.org/abs/1906.00500v1
• [cs.CL]A computational linguistic study of personal recovery in bipolar disorder
Glorianna Jagfeld
http://arxiv.org/abs/1906.01010v1
• [cs.CL]Are Girls Neko or Shōjo? Cross-Lingual Alignment of Non-Isomorphic Embeddings with Iterative Normalization
Mozhi Zhang, Keyulu Xu, Ken-ichi Kawarabayashi, Stefanie Jegelka, Jordan Boyd-Graber
http://arxiv.org/abs/1906.01622v1
• [cs.CL]Are You Looking? Grounding to Multiple Modalities in Vision-and-Language Navigation
Ronghang Hu, Daniel Fried, Anna Rohrbach, Dan Klein, Trevor Darrell, Kate Saenko
http://arxiv.org/abs/1906.00347v2
• [cs.CL]Are we there yet? Encoder-decoder neural networks as cognitive models of English past tense inflection
Maria Corkery, Yevgen Matusevych, Sharon Goldwater
http://arxiv.org/abs/1906.01280v1
• [cs.CL]Assessing the Ability of Self-Attention Networks to Learn Word Order
Baosong Yang, Longyue Wang, Derek F. Wong, Lidia S. Chao, Zhaopeng Tu
http://arxiv.org/abs/1906.00592v1
• [cs.CL]Back Attention Knowledge Transfer for Low-resource Named Entity Recognition
Linghao Sun, Huixiong Yi, Huanhuan Chen
http://arxiv.org/abs/1906.01183v1
• [cs.CL]Better Character Language Modeling Through Morphology
Terra Blevins, Luke Zettlemoyer
http://arxiv.org/abs/1906.01037v1
• [cs.CL]Boosting Entity Linking Performance by Leveraging Unlabeled Documents
Phong Le, Ivan Titov
http://arxiv.org/abs/1906.01250v1
• [cs.CL]Budgeted Policy Learning for Task-Oriented Dialogue Systems
Zhirui Zhang, Xiujun Li, Jianfeng Gao, Enhong Chen
http://arxiv.org/abs/1906.00499v1
• [cs.CL]ChID: A Large-scale Chinese IDiom Dataset for Cloze Test
Chujie Zheng, Minlie Huang, Aixin Sun
http://arxiv.org/abs/1906.01265v1
• [cs.CL]Coherent Comment Generation for Chinese Articles with a Graph-to-Sequence Model
Wei Li, Jingjing Xu, Yancheng He, Shengli Yan, Yunfang Wu, Xu sun
http://arxiv.org/abs/1906.01231v1
• [cs.CL]Controllable Paraphrase Generation with a Syntactic Exemplar
Mingda Chen, Qingming Tang, Sam Wiseman, Kevin Gimpel
http://arxiv.org/abs/1906.00565v1
• [cs.CL]Curate and Generate: A Corpus and Method for Joint Control of Semantics and Style in Neural NLG
Shereen Oraby, Vrindavan Harrison, Abteen Ebrahimi, Marilyn Walker
http://arxiv.org/abs/1906.01334v1
• [cs.CL]Deep Unknown Intent Detection with Margin Loss
Ting-En Lin, Hua Xu
http://arxiv.org/abs/1906.00434v1
• [cs.CL]Distantly Supervised Named Entity Recognition using Positive-Unlabeled Learning
Minlong Peng, Xiaoyu Xing, Qi Zhang, Jinlan Fu, Xuanjing Huang
http://arxiv.org/abs/1906.01378v1
• [cs.CL]Do Neural Dialog Systems Use the Conversation History Effectively? An Empirical Study
Chinnadhurai Sankar, Sandeep Subramanian, Christopher Pal, Sarath Chandar, Yoshua Bengio
http://arxiv.org/abs/1906.01603v1
• [cs.CL]Domain Adaptation of Neural Machine Translation by Lexicon Induction
Junjie Hu, Mengzhou Xia, Graham Neubig, Jaime Carbonell
http://arxiv.org/abs/1906.00376v1
• [cs.CL]Domain Adaptive Inference for Neural Machine Translation
Danielle Saunders, Felix Stahlberg, Adria de Gispert, Bill Byrne
http://arxiv.org/abs/1906.00408v1
• [cs.CL]Dynamically Composing Domain-Data Selection with Clean-Data Selection by “Co-Curricular Learning” for Neural Machine Translation
Wei Wang, Isaac Caswell, Ciprian Chelba
http://arxiv.org/abs/1906.01130v1
• [cs.CL]Emotion-Cause Pair Extraction: A New Task to Emotion Analysis in Texts
Rui Xia, Zixiang Ding
http://arxiv.org/abs/1906.01267v1
• [cs.CL]Evaluating Discourse in Structured Text Representations
Elisa Ferracane, Greg Durrett, Junyi Jessy Li, Katrin Erk
http://arxiv.org/abs/1906.01472v1
• [cs.CL]Evaluating Gender Bias in Machine Translation
Gabriel Stanovsky, Noah A. Smith, Luke Zettlemoyer
http://arxiv.org/abs/1906.00591v1
• [cs.CL]Exploiting Sentential Context for Neural Machine Translation
Xing Wang, Zhaopeng Tu, Longyue Wang, Shuming Shi
http://arxiv.org/abs/1906.01268v1
• [cs.CL]Exploring Phoneme-Level Speech Representations for End-to-End Speech Translation
Elizabeth Salesky, Matthias Sperber, Alan W Black
http://arxiv.org/abs/1906.01199v1
• [cs.CL]Finding Syntactic Representations in Neural Stacks
William Merrill, Lenny Khazan, Noah Amsel, Yiding Hao, Simon Mendelsohn, Robert Frank
http://arxiv.org/abs/1906.01594v1
• [cs.CL]Fluent Translations from Disfluent Speech in End-to-End Speech Translation
Elizabeth Salesky, Matthias Sperber, Alex Waibel
http://arxiv.org/abs/1906.00556v1
• [cs.CL]From Independent Prediction to Re-ordered Prediction: Integrating Relative Position and Global Label Information to Emotion Cause Identification
Zixiang Ding, Huihui He, Mengran Zhang, Rui Xia
http://arxiv.org/abs/1906.01230v1
• [cs.CL]From Speech Chain to Multimodal Chain: Leveraging Cross-modal Data Augmentation for Semi-supervised Learning
Johanes Effendi, Andros Tjandra, Sakriani Sakti, Satoshi Nakamura
http://arxiv.org/abs/1906.00579v1
• [cs.CL]From Words to Sentences: A Progressive Learning Approach for Zero-resource Machine Translation with Visual Pivots
Shizhe Chen, Qin Jin, Jianlong Fu
http://arxiv.org/abs/1906.00872v1
• [cs.CL]Gender-preserving Debiasing for Pre-trained Word Embeddings
Masahiro Kaneko, Danushka Bollegala
http://arxiv.org/abs/1906.00742v1
• [cs.CL]Gendered Ambiguous Pronouns Shared Task: Boosting Model Confidence by Evidence Pooling
Sandeep Attree
http://arxiv.org/abs/1906.00839v1
• [cs.CL]Global Textual Relation Embedding for Relational Understanding
Zhiyu Chen, Hanwen Zha, Honglei Liu, Wenhu Chen, Xifeng Yan, Yu Su
http://arxiv.org/abs/1906.00550v1
• [cs.CL]Handling Divergent Reference Texts when Evaluating Table-to-Text Generation
Bhuwan Dhingra, Manaal Faruqui, Ankur Parikh, Ming-Wei Chang, Dipanjan Das, William W. Cohen
http://arxiv.org/abs/1906.01081v1
• [cs.CL]HighRES: Highlight-based Reference-less Evaluation of Summarization
Hardy, Shashi Narayan, Andreas Vlachos
http://arxiv.org/abs/1906.01361v1
• [cs.CL]How Large Are Lions? Inducing Distributions over Quantitative Attributes
Yanai Elazar, Abhijit Mahabal, Deepak Ramachandran, Tania Bedrax-Weiss, Dan Roth
http://arxiv.org/abs/1906.01327v1
• [cs.CL]How multilingual is Multilingual BERT?
Telmo Pires, Eva Schlinger, Dan Garrette
http://arxiv.org/abs/1906.01502v1
• [cs.CL]Improved Zero-shot Neural Machine Translation via Ignoring Spurious Correlations
Jiatao Gu, Yong Wang, Kyunghyun Cho, Victor O. K. Li
http://arxiv.org/abs/1906.01181v1
• [cs.CL]Improving Long Distance Slot Carryover in Spoken Dialogue Systems
Tongfei Chen, Chetan Naik, Hua He, Pushpendre Rastogi, Lambert Mathias
http://arxiv.org/abs/1906.01149v1
• [cs.CL]Joint Effects of Context and User History for Predicting Online Conversation Re-entries
Xingshan Zeng, Jing Li, Lu Wang, Kam-Fai Wong
http://arxiv.org/abs/1906.01185v1
• [cs.CL]Jointly Learning Semantic Parser and Natural Language Generator via Dual Information Maximization
Hai Ye, Wenjie Li, Lu Wang
http://arxiv.org/abs/1906.00575v1
• [cs.CL]KERMIT: Generative Insertion-Based Modeling for Sequences
William Chan, Nikita Kitaev, Kelvin Guu, Mitchell Stern, Jakob Uszkoreit
http://arxiv.org/abs/1906.01604v1
• [cs.CL]Know More about Each Other: Evolving Dialogue Strategy via Compound Assessment
Siqi Bao, Huang He, Fan Wang, Rongzhong Lian, Hua Wu
http://arxiv.org/abs/1906.00549v1
• [cs.CL]Latent Retrieval for Weakly Supervised Open Domain Question Answering
Kenton Lee, Ming-Wei Chang, Kristina Toutanova
http://arxiv.org/abs/1906.00300v1
• [cs.CL]Lattice-Based Transformer Encoder for Neural Machine Translation
Fengshun Xiao, Jiangtong Li, Hai Zhao, Rui Wang, Kehai Chen
http://arxiv.org/abs/1906.01282v1
• [cs.CL]Learning to Explain: Answering Why-Questions via Rephrasing
Allen Nie, Erin D. Bennett, Noah D. Goodman
http://arxiv.org/abs/1906.01243v1
• [cs.CL]Massive Styles Transfer with Limited Labeled Data
Hongyu Zang, Xiaojun Wan
http://arxiv.org/abs/1906.00580v1
• [cs.CL]Multi-Task Semantic Dependency Parsing with Policy Gradient for Learning Easy-First Strategies
Shuhei Kurita, Anders Søgaard
http://arxiv.org/abs/1906.01239v1
• [cs.CL]Multi-task Pairwise Neural Ranking for Hashtag Segmentation
Mounica Maddela, Wei Xu, Daniel Preoţiuc-Pietro
http://arxiv.org/abs/1906.00790v1
• [cs.CL]Multimodal Transformer for Unaligned Multimodal Language Sequences
Yao-Hung Hubert Tsai, Shaojie Bai, Paul Pu Liang, J. Zico Kolter, Louis-Philippe Morency, Ruslan Salakhutdinov
http://arxiv.org/abs/1906.00295v1
• [cs.CL]NNE: A Dataset for Nested Named Entity Recognition in English Newswire
Nicky Ringland, Xiang Dai, Ben Hachey, Sarvnaz Karimi, Cecile Paris, James R. Curran
http://arxiv.org/abs/1906.01359v1
• [cs.CL]Optimal coding and the origins of Zipfian laws
Ramon Ferrer-i-Cancho, Christian Bentz
http://arxiv.org/abs/1906.01545v1
• [cs.CL]Phase-based Minimalist Parsing and complexity in non-local dependencies
Cristiano Chesi
http://arxiv.org/abs/1906.00908v1
• [cs.CL]Pitfalls in the Evaluation of Sentence Embeddings
Steffen Eger, Andreas Rücklé, Iryna Gurevych
http://arxiv.org/abs/1906.01575v1
• [cs.CL]Plain English Summarization of Contracts
Laura Manor, Junyi Jessy Li
http://arxiv.org/abs/1906.00424v1
• [cs.CL]Pretraining Methods for Dialog Context Representation Learning
Shikib Mehri, Evgeniia Razumovskaia, Tiancheng Zhao, Maxine Eskenazi
http://arxiv.org/abs/1906.00414v2
• [cs.CL]Progressive Self-Supervised Attention Learning for Aspect-Level Sentiment Analysis
Jialong Tang, Ziyao Lu, Jinsong Su, Yubin Ge, Linfeng Song, Le Sun, Jiebo Luo
http://arxiv.org/abs/1906.01213v1
• [cs.CL]Promotion of Answer Value Measurement with Domain Effects in Community Question Answering Systems
Binbin Jin, Enhong Chen, Hongke Zhao, Zhenya Huang, Qi Liu, Hengshu Zhu, Shui Yu
http://arxiv.org/abs/1906.00156v1
• [cs.CL]Question Answering as an Automatic Evaluation Metric for News Article Summarization
Matan Eyal, Tal Baumel, Michael Elhadad
http://arxiv.org/abs/1906.00318v1
• [cs.CL]RTHN: A RNN-Transformer Hierarchical Network for Emotion Cause Extraction
Rui Xia, Mengran Zhang, Zixiang Ding
http://arxiv.org/abs/1906.01236v1
• [cs.CL]Recognising Agreement and Disagreement between Stances with Reason Comparing Networks
Chang Xu, Cecile Paris, Surya Nepal, Ross Sparks
http://arxiv.org/abs/1906.01392v1
• [cs.CL]Relation Embedding with Dihedral Group in Knowledge Graph
Canran Xu, Ruijiang Li
http://arxiv.org/abs/1906.00687v1
• [cs.CL]Relational Word Embeddings
Jose Camacho-Collados, Luis Espinosa-Anke, Steven Schockaert
http://arxiv.org/abs/1906.01373v1
• [cs.CL]Resolving Gendered Ambiguous Pronouns with BERT
Matei Ionita, Yury Kashnitsky, Ken Krige, Vladimir Larin, Dennis Logvinenko, Atanas Atanasov
http://arxiv.org/abs/1906.01161v1
• [cs.CL]Robust Sequence-to-Sequence Acoustic Modeling with Stepwise Monotonic Attention for Neural TTS
Mutian He, Yan Deng, Lei He
http://arxiv.org/abs/1906.00672v1
• [cs.CL]Self-Attentional Models for Lattice Inputs
Matthias Sperber, Graham Neubig, Ngoc-Quan Pham, Alex Waibel
http://arxiv.org/abs/1906.01617v1
• [cs.CL]Semantically Constrained Multilayer Annotation: The Case of Coreference
Jakob Prange, Nathan Schneider, Omri Abend
http://arxiv.org/abs/1906.00663v2
• [cs.CL]Sentiment Tagging with Partial Labels using Modular Architectures
Xiao Zhang, Dan Goldwasser
http://arxiv.org/abs/1906.00534v2
• [cs.CL]Sequence Tagging with Contextual and Non-Contextual Subword Representations: A Multilingual Evaluation
Benjamin Heinzerling, Michael Strube
http://arxiv.org/abs/1906.01569v1
• [cs.CL]Sequential Neural Networks as Automata
William Merrill
http://arxiv.org/abs/1906.01615v1
• [cs.CL]ShEMO — A Large-Scale Validated Database for Persian Speech Emotion Detection
Omid Mohamad Nezami, Paria Jamshid Lou, Mansoureh Karami
http://arxiv.org/abs/1906.01155v1
• [cs.CL]SherLIiC: A Typed Event-Focused Lexical Inference Benchmark for Evaluating Natural Language Inference
Martin Schmitt, Hinrich Schütze
http://arxiv.org/abs/1906.01393v1
• [cs.CL]Simultaneous Translation with Flexible Policy via Restricted Imitation Learning
Baigong Zheng, Renjie Zheng, Mingbo Ma, Liang Huang
http://arxiv.org/abs/1906.01135v1
• [cs.CL]System Demo for Transfer Learning across Vision and Text using Domain Specific CNN Accelerator for On-Device NLP Applications
Baohua Sun, Lin Yang, Michael Lin, Wenhan Zhang, Patrick Dong, Charles Young, Jason Dong
http://arxiv.org/abs/1906.01145v1
• [cs.CL]TalkSumm: A Dataset and Scalable Annotation Method for Scientific Paper Summarization Based on Conference Talks
Guy Lev, Michal Shmueli-Scheuer, Jonathan Herzig, Achiya Jerbi, David Konopnicki
http://arxiv.org/abs/1906.01351v1
• [cs.CL]Task-Guided Pair Embedding in Heterogeneous Network
Chanyoung Park, Donghyun Kim, Qi Zhu, Jiawei Han, Hwanjo Yu
http://arxiv.org/abs/1906.01546v1
• [cs.CL]The PhotoBook Dataset: Building Common Ground through Visually-Grounded Dialogue
Janosch Haber, Tim Baumgärtner, Ece Takmaz, Lieke Gelderloos, Elia Bruni, Raquel Fernández
http://arxiv.org/abs/1906.01530v1
• [cs.CL]Toward Grammatical Error Detection from Sentence Labels: Zero-shot Sequence Labeling with CNNs and Contextualized Embeddings
Allen Schmaltz
http://arxiv.org/abs/1906.01154v1
• [cs.CL]Tracing Antisemitic Language Through Diachronic Embedding Projections: France 1789-1914
Rocco Tripodi, Massimo Warglien, Simon Levis Sullam, Deborah Paci
http://arxiv.org/abs/1906.01440v1
• [cs.CL]Training Neural Machine Translation To Apply Terminology Constraints
Georgiana Dinu, Prashant Mathur, Marcello Federico, Yaser Al-Onaizan
http://arxiv.org/abs/1906.01105v1
• [cs.CL]Training Neural Response Selection for Task-Oriented Dialogue Systems
Matthew Henderson, Ivan Vulić, Daniela Gerz, Iñigo Casanueva, Paweł Budzianowski, Sam Coope, Georgios Spithourakis, Tsung-Hsien Wen, Nikola Mrkšić, Pei-Hao Su
http://arxiv.org/abs/1906.01543v1
• [cs.CL]Transcoding compositionally: using attention to find more generalizable solutions
Kris Korrel, Dieuwke Hupkes, Verna Dankers, Elia Bruni
http://arxiv.org/abs/1906.01234v1
• [cs.CL]Transferable Neural Projection Representations
Chinnadhurai Sankar, Sujith Ravi, Zornitsa Kozareva
http://arxiv.org/abs/1906.01605v1
• [cs.CL]Transforming Complex Sentences into a Semantic Hierarchy
Christina Niklaus, Matthias Cetto, Andre Freitas, Siegfried Handschuh
http://arxiv.org/abs/1906.01038v1
• [cs.CL]Unsupervised Bilingual Lexicon Induction from Mono-lingual Multimodal Data
Shizhe Chen, Qin Jin, Alexander Hauptmann
http://arxiv.org/abs/1906.00378v1
• [cs.CR]DAWN: Dynamic Adversarial Watermarking of Neural Networks
Sebastian Szyller, Buse Gul Atli, Samuel Marchal, N. Asokan
http://arxiv.org/abs/1906.00830v1
• [cs.CR]Encryption Scheme Based on Expanded Reed-Solomon Codes
Karan Khathuria, Joachim Rosenthal, Violetta Weger
http://arxiv.org/abs/1906.00745v1
• [cs.CR]Generative Adversarial Networks for Distributed Intrusion Detection in the Internet of Things
Aidin Ferdowsi, Walid Saad
http://arxiv.org/abs/1906.00567v1
• [cs.CR]Human-Usable Password Schemas: Beyond Information-Theoretic Security
Elan Rosenfeld, Santosh Vempala, Manuel Blum
http://arxiv.org/abs/1906.00029v1
• [cs.CR]New non-linearity parameters of Boolean functions
Igor Semaev
http://arxiv.org/abs/1906.00426v1
• [cs.CR]Privacy-preserving Crowd-guided AI Decision-making in Ethical Dilemmas
Teng Wang, Jun Zhao, Han Yu, Jinyan Liu, Xinyu Yang, Xuebin Ren, Shuyu Shi
http://arxiv.org/abs/1906.01562v1
• [cs.CR]Towards Fair and Decentralized Privacy-Preserving Deep Learning with Blockchain
Lingjuan Lyu, Jiangshan Yu, Karthik Nandakumar, Yitong Li, Xingjun Ma, Jiong Jin
http://arxiv.org/abs/1906.01167v1
• [cs.CV]3D Appearance Super-Resolution with Deep Learning
Yawei Li, Vagia Tsiminaki, Radu Timofte, Marc Pollefeys, Luc van Gool
http://arxiv.org/abs/1906.00925v2
• [cs.CV]A Closed-form Solution to Universal Style Transfer
Ming Lu, Hao Zhao, Anbang Yao, Yurong Chen, Feng Xu, Li Zhang
http://arxiv.org/abs/1906.00668v1
• [cs.CV]A Hybrid RNN-HMM Approach for Weakly Supervised Temporal Action Segmentation
Hilde Kuehne, Alexander Richard, Juergen Gall
http://arxiv.org/abs/1906.01028v1
• [cs.CV]Active Object Manipulation Facilitates Visual Object Learning: An Egocentric Vision Study
Satoshi Tsutsui, Dian Zhi, Md Alimoor Reza, David Crandall, Chen Yu
http://arxiv.org/abs/1906.01415v1
• [cs.CV]Adversarial Examples for Edge Detection: They Exist, and They Transfer
Christian Cosgrove, Alan L. Yuille
http://arxiv.org/abs/1906.00335v1
• [cs.CV]An Adaptive Training-less System for Anomaly Detection in Crowd Scenes
Arindam Sikdar, Ananda S. Chowdhury
http://arxiv.org/abs/1906.00705v1
• [cs.CV]Automated Steel Bar Counting and Center Localization with Convolutional Neural Networks
Zhun Fan, Jiewei Lu, Benzhang Qiu, Tao Jiang, Kang An, Alex Noel Josephraj, Chuliang Wei
http://arxiv.org/abs/1906.00891v1
• [cs.CV]Automatic Health Problem Detection from Gait Videos Using Deep Neural Networks
Rahil Mehrizi, Xi Peng, Shaoting Zhang, Ruisong Liao, Kang Li
http://arxiv.org/abs/1906.01480v1
• [cs.CV]Color Constancy Convolutional Autoencoder
Firas Laakom, Jenni Raitoharju, Alexandros Iosifidis, Jarno Nikkanen, Moncef Gabbouj
http://arxiv.org/abs/1906.01340v1
• [cs.CV]Comparing two- and three-view Computer Vision
Zsolt Levente Kucsván
http://arxiv.org/abs/1906.01003v1
• [cs.CV]Computing Valid p-values for Image Segmentation by Selective Inference
Kosuke Tanizaki, Noriaki Hashimoto, Yu Inatsu, Hidekata Hontani, Ichiro Takeuchi
http://arxiv.org/abs/1906.00629v1
• [cs.CV]Content Adaptive Optimization for Neural Image Compression
Joaquim Campos, Meierhans Simon, Abdelaziz Djelouah, Christopher Schroers
http://arxiv.org/abs/1906.01223v1
• [cs.CV]Cross-Domain Cascaded Deep Feature Translation
Oren Katzir, Dani Lischinski, Daniel Cohen-Or
http://arxiv.org/abs/1906.01526v1
• [cs.CV]Data Augmentation for Object Detection via Progressive and Selective Instance-Switching
Hao Wang, Qilong Wang, Fan Yang, Weiqi Zhang, Wangmeng Zuo
http://arxiv.org/abs/1906.00358v1
• [cs.CV]Deep Face Recognition Model Compression via Knowledge Transfer and Distillation
Jayashree Karlekar, Jiashi Feng, Zi Sian Wong, Sugiri Pranata
http://arxiv.org/abs/1906.00619v1
• [cs.CV]Deeply-supervised Knowledge Synergy
Dawei Sun, Anbang Yao, Aojun Zhou, Hao Zhao
http://arxiv.org/abs/1906.00675v2
• [cs.CV]Depth-Preserving Real-Time Arbitrary Style Transfer
Konstantin Kozlovtsev, Victor Kitov
http://arxiv.org/abs/1906.01123v1
• [cs.CV]Disentangling neural mechanisms for perceptual grouping
Junkyung Kim, Drew Linsley, Kalpit Thakkar, Thomas Serre
http://arxiv.org/abs/1906.01558v1
• [cs.CV]Dominant Set Clustering and Pooling for Multi-View 3D Object Recognition
Chu Wang, Marcello Pelillo, Kaleem Siddiqi
http://arxiv.org/abs/1906.01592v1
• [cs.CV]DualDis: Dual-Branch Disentangling with Adversarial Learning
Thomas Robert, Nicolas Thome, Matthieu Cord
http://arxiv.org/abs/1906.00804v1
• [cs.CV]Dynamic Neural Network Decoupling
Yuchao Li, Rongrong Ji, Shaohui Lin, Baochang Zhang, Chenqian Yan, Yongjian Wu, Feiyue Huang, Ling Shao
http://arxiv.org/abs/1906.01166v1
• [cs.CV]End-to-End Learning of Geometric Deformations of Feature Maps for Virtual Try-On
Thibaut Issenhuth, Jérémie Mary, Clément Calauzennes
http://arxiv.org/abs/1906.01347v1
• [cs.CV]Evaluation of an AI system for the automated detection of glaucoma from stereoscopic optic disc photographs: the European Optic Disc Assessment Study
Thomas W. Rogers, Nicolas Jaccard, Francis Carbonaro, Hans G. Lemij, Koenraad A. Vermeer, Nicolaas J. Reus, Sameer Trikha
http://arxiv.org/abs/1906.01272v1
• [cs.CV]Example-Guided Style Consistent Image Synthesis from Semantic Labeling
Miao Wang, Guo-Ye Yang, Ruilong Li, Run-Ze Liang, Song-Hai Zhang, Peter. M. Hall, Shi-Min Hu
http://arxiv.org/abs/1906.01314v1
• [cs.CV]Exploiting Offset-guided Network for Pose Estimation and Tracking
Rui Zhang, Zheng Zhu, Peng Li, Rui Wu, Chaoxu Guo, Guan Huang, Hailun Xia
http://arxiv.org/abs/1906.01344v1
• [cs.CV]Face Parsing with RoI Tanh-Warping
Jinpeng Lin, Hao Yang, Dong Chen, Ming Zeng, Fang Wen, Lu Yuan
http://arxiv.org/abs/1906.01342v1
• [cs.CV]Fashion Editing with Multi-scale Attention Normalization
Haoye Dong, Xiaodan Liang, Yixuan Zhang, Xujie Zhang, Zhenyu Xie, Bowen Wu, Ziqi Zhang, Xiaohui Shen, Jian Yin
http://arxiv.org/abs/1906.00884v1
• [cs.CV]GazeCorrection:Self-Guided Eye Manipulation in the wild using Self-Supervised Generative Adversarial Networks
Jichao Zhang, Meng Sun, Jingjing Chen, Hao Tang, Yan Yan, Xueying Qin, Nicu Sebe
http://arxiv.org/abs/1906.00805v1
• [cs.CV]Generating Question Relevant Captions to Aid Visual Question Answering
Jialin Wu, Zeyuan Hu, Raymond J. Mooney
http://arxiv.org/abs/1906.00513v1
• [cs.CV]Hierarchical Video Frame Sequence Representation with Deep Convolutional Graph Network
Feng Mao, Xiang Wu, Hui Xue, Rong Zhang
http://arxiv.org/abs/1906.00377v1
• [cs.CV]How Much Does Audio Matter to Recognize Egocentric Object Interactions?
Alejandro Cartas, Jordi Luque, Petia Radeva, Carlos Segura, Mariella Dimiccoli
http://arxiv.org/abs/1906.00634v1
• [cs.CV]Incremental Few-Shot Learning for Pedestrian Attribute Recognition
Liuyu Xiang, Xiaoming Jin, Guiguang Ding, Jungong Han, Leida Li
http://arxiv.org/abs/1906.00330v1
• [cs.CV]Iterative Path Reconstruction for Large-Scale Inertial Navigation on Smartphones
Santiago Cortés Reina, Yuxin Hou, Juho Kannala, Arno Solin
http://arxiv.org/abs/1906.00360v1
• [cs.CV]KarNet: An Efficient Boolean Function Simplifier
Shanka Subhra Mondal, Abhilash Nandy, Ritesh Agrawal, Debashis Sen
http://arxiv.org/abs/1906.01363v1
• [cs.CV]Learning Object Bounding Boxes for 3D Instance Segmentation on Point Clouds
Bo Yang, Jianan Wang, Ronald Clark, Qingyong Hu, Sen Wang, Andrew Markham, Niki Trigoni
http://arxiv.org/abs/1906.01140v1
• [cs.CV]Learning Rotation Adaptive Correlation Filters in Robust Visual Object Tracking
Litu Rout, Priya Mariam Raju, Deepak Mishra, Rama Krishna Sai Subrahmanyam Gorthi
http://arxiv.org/abs/1906.01551v1
• [cs.CV]Learning to Generate Grounded Image Captions without Localization Supervision
Chih-Yao Ma, Yannis Kalantidis, Ghassan AlRegib, Peter Vajda, Marcus Rohrbach, Zsolt Kira
http://arxiv.org/abs/1906.00283v1
• [cs.CV]Learning to Self-Train for Semi-Supervised Few-Shot Classification
Qianru Sun, Xinzhe Li, Yaoyao Liu, Shibao Zheng, Tat-Seng Chua, Bernt Schiele
http://arxiv.org/abs/1906.00562v1
• [cs.CV]Localization in Aerial Imagery with Grid Maps using LocGAN
Haohao Hu, Junyi Zhu, Sascha Wirges, Martin Lauer
http://arxiv.org/abs/1906.01540v1
• [cs.CV]Masked Non-Autoregressive Image Captioning
Junlong Gao, Xi Meng, Shiqi Wang, Xia Li, Shanshe Wang, Siwei Ma, Wen Gao
http://arxiv.org/abs/1906.00717v1
• [cs.CV]Mining YouTube - A dataset for learning fine-grained action concepts from webly supervised video data
Hilde Kuehne, Ahsan Iqbal, Alexander Richard, Juergen Gall
http://arxiv.org/abs/1906.01012v1
• [cs.CV]Natural Vocabulary Emerges from Free-Form Annotations
Jordi Pont-Tuset, Michael Gygli, Vittorio Ferrari
http://arxiv.org/abs/1906.01542v1
• [cs.CV]Panoptic Edge Detection
Yuan Hu, Yingtian Zou, Jiashi Feng
http://arxiv.org/abs/1906.00590v1
• [cs.CV]Parametric Shape Modeling and Skeleton Extraction with Radial Basis Functions using Similarity Domains Network
Sedat Ozer
http://arxiv.org/abs/1906.00265v1
• [cs.CV]Perceptual Embedding Consistency for Seamless Reconstruction of Tilewise Style Transfer
Amal Lahiani, Nassir Navab, Shadi Albarqouni, Eldad Klaiman
http://arxiv.org/abs/1906.00617v1
• [cs.CV]Photo-Geometric Autoencoding to Learn 3D Objects from Unlabelled Images
Shangzhe Wu, Christian Rupprecht, Andrea Vedaldi
http://arxiv.org/abs/1906.01568v1
• [cs.CV]RF-Net: An End-to-End Image Matching Network based on Receptive Field
Xuelun Shen, Cheng Wang, Xin Li, Zenglei Yu, Jonathan Li, Chenglu Wen, Ming Cheng, Zijian He
http://arxiv.org/abs/1906.00604v1
• [cs.CV]RGB and LiDAR fusion based 3D Semantic Segmentation for Autonomous Driving
Khaled El Madawy, Hazem Rashed, Ahmad El Sallab, Omar Nasr, Hanan Kamel, Senthil Yogamani
http://arxiv.org/abs/1906.00208v1
• [cs.CV]Random Path Selection for Incremental Learning
Jathushan Rajasegaran, Munawar Hayat, Salman Khan, Fahad Shahbaz Khan, Ling Shao
http://arxiv.org/abs/1906.01120v1
• [cs.CV]Reconstruct and Represent Video Contents for Captioning via Reinforcement Learning
Wei Zhang, Bairui Wang, Lin Ma, Wei Liu
http://arxiv.org/abs/1906.01452v1
• [cs.CV]Relational Reasoning using Prior Knowledge for Visual Captioning
Jingyi Hou, Xinxiao Wu, Yayun Qi, Wentian Zhao, Jiebo Luo, Yunde Jia
http://arxiv.org/abs/1906.01290v1
• [cs.CV]Resolving Overlapping Convex Objects in Silhouette Images by Concavity Analysis and Gaussian Process
Sahar Zafari, Mariia Murashkina, Tuomas Eerola, Jouni Sampo, Heikki Kälviäinen, Heikki Haario
http://arxiv.org/abs/1906.01049v1
• [cs.CV]Rethinking Loss Design for Large-scale 3D Shape Retrieval
Zhaoqun Li, Cheng Xu, Biao Leng
http://arxiv.org/abs/1906.00546v1
• [cs.CV]Robust copy-move forgery detection by false alarms control
Thibaud Ehret
http://arxiv.org/abs/1906.00649v1
• [cs.CV]Scene Representation Networks: Continuous 3D-Structure-Aware Neural Scene Representations
Vincent Sitzmann, Michael Zollhöfer, Gordon Wetzstein
http://arxiv.org/abs/1906.01618v1
• [cs.CV]Selective Style Transfer for Text
Raul Gomez, Ali Furkan Biten, Lluis Gomez, Jaume Gibert, Marçal Rusiñol, Dimosthenis Karatzas
http://arxiv.org/abs/1906.01466v1
• [cs.CV]Semi- and Weakly-supervised Human Pose Estimation
Norimichi Ukita, Yusuke Uematsu
http://arxiv.org/abs/1906.01399v1
• [cs.CV]Separate from Observation: Unsupervised Single Image Layer Separation
Yunfei Liu, Feng Lu
http://arxiv.org/abs/1906.00734v1
• [cs.CV]State-aware Re-identification Feature for Multi-target Multi-camera Tracking
Peng Li, Jiabin Zhang, Zheng Zhu, Yanwei Li, Lu Jiang, Guan Huang
http://arxiv.org/abs/1906.01357v1
• [cs.CV]Style Transfer With Adaptation to the Central Objects of the Scene
Alexey Schekalev, Victor Kitov
http://arxiv.org/abs/1906.01134v1
• [cs.CV]Text-based Editing of Talking-head Video
Ohad Fried, Ayush Tewari, Michael Zollhöfer, Adam Finkelstein, Eli Shechtman, Dan B Goldman, Kyle Genova, Zeyu Jin, Christian Theobalt, Maneesh Agrawala
http://arxiv.org/abs/1906.01524v1
• [cs.CV]The iMet Collection 2019 Challenge Dataset
Chenyang Zhang, Christine Kaeser-Chen, Grace Vesom, Jennie Choi, Maria Kessler, Serge Belongie
http://arxiv.org/abs/1906.00901v2
• [cs.CV]Towards better Validity: Dispersion based Clustering for Unsupervised Person Re-identification
Guodong Ding, Salman Khan, Zhenmin Tang, Jian Zhang, Fatih Porikli
http://arxiv.org/abs/1906.01308v1
• [cs.CV]Transfer Learning with intelligent training data selection for prediction of Alzheimer’s Disease
Naimul Mefraz Khan, Marcia Hon, Nabila Abraham
http://arxiv.org/abs/1906.01160v1
• [cs.CV]Triangulation Learning Network: from Monocular to Stereo 3D Object Detection
Zengyi Qin, Jinglu Wang, Yan Lu
http://arxiv.org/abs/1906.01193v1
• [cs.CV]Visual Diagnosis of Dermatological Disorders: Human and Machine Performance
Jeremy Kawahara, Ghassan Hamarneh
http://arxiv.org/abs/1906.01256v1
• [cs.CV]Visual Tree Convolutional Neural Network in Image Classification
Yuntao Liu, Yong Dou, Ruochun Jin, Peng Qiao
http://arxiv.org/abs/1906.01536v1
• [cs.CV]Zero-Shot Semantic Segmentation
Maxime Bucher, Tuan-Hung Vu, Matthieu Cord, Patrick Pérez
http://arxiv.org/abs/1906.00817v1
• [cs.CV]cGANs with Conditional Convolution Layer
Min-Cheol Sagong, Yong-Goo Shin, Yoon-Jae Yeo, Seung Park, Sung-Jea Ko
http://arxiv.org/abs/1906.00709v1
• [cs.CY]Apprentissage de la pensée informatique : de la formation des enseignant$\cdot$e$\cdot$s à la formation de tou$\cdot$te$\cdot$s les citoyen$\cdot$ne$\cdot$s
Corinne Atlan, Jean-Pierre Archambault, Olivier Banus, Frédéric Bardeau, Amélie Blandeau, Antonin Cois, Martine Courbin, Gérard Giraudon, Saint-Clair Lefèvre, Valérie Letard, Bastien Masse, Florent Masseglia, Benjamin Ninassi, Sophie de Quatrebarbes, Margarida Romero, Didier Roy, Thierry Vieville
http://arxiv.org/abs/1906.00647v1
• [cs.CY]Grade prediction with course and student specific models
Agoritsa Polyzou, George Karypis
http://arxiv.org/abs/1906.00792v1
• [cs.CY]Peut-on former les enseignant$\cdot$e$\cdot$s en un rien de temps ?
Christelle Mariais, David Roche, Laurence Farhi, Sabrina Barnabé, Sonia Cruchon, Sophie de Quatrebarbes, Thierry Vieville
http://arxiv.org/abs/1906.00633v1
• [cs.DB]Efficient Algorithms for Densest Subgraph Discovery
Yixiang Fang, Kaiqiang Yu, Reynold Cheng, Laks V. S. Lakshmanan, Xuemin Lin
http://arxiv.org/abs/1906.00341v1
• [cs.DB]Motivo: fast motif counting via succinct color coding and adaptive sampling
Marco Bressan, Stefano Leucci, Alessandro Panconesi
http://arxiv.org/abs/1906.01599v1
• [cs.DC]A Hybrid Cache Architecture for Meeting Per-Tenant Performance Goals in a Private Cloud
Taejoon Kim, Yu Gu, Jinoh Kim
http://arxiv.org/abs/1906.01260v1
• [cs.DC]A Technique for Finding Optimal Program Launch Parameters Targeting Manycore Accelerators
Alexander Brandt, Davood Mohajerani, Marc Moreno Maza, Jeeva Paudel, Lin-Xiao Wang
http://arxiv.org/abs/1906.00142v1
• [cs.DC]Assessing Performance Implications of Deep Copy Operations via Microbenchmarking
Millad Ghane, Sunita Chandrasekaran, Margaret S. Cheung
http://arxiv.org/abs/1906.01128v1
• [cs.DC]Mutable Locks: Combining the Best of Spin and Sleep Locks
Romolo Marotta, Davide Tiriticco, Pierangelo Di Sanzo, Alessandro Pellegrini, Francesco Quaglia
http://arxiv.org/abs/1906.00490v1
• [cs.DC]Optimal Register Construction in M&M Systems
Vassos Hadzilacos, Xing Hu, Sam Toueg
http://arxiv.org/abs/1906.00298v1
• [cs.DC]Patterns for Blockchain Migration
HMN Dilum Bandara, Xiwei Xu, Ingo Weber
http://arxiv.org/abs/1906.00239v1
• [cs.DC]Probabilistic Top-k Dominating Query Monitoring over Multiple Uncertain IoT Data Streams in Edge Computing Environments
Chuan-Chi Lai, Tien-Chun Wang, Chuan-Ming Liu, Li-Chun Wang
http://arxiv.org/abs/1906.00219v1
• [cs.DC]Proximity Neighbor Selection in Blockchain Networks
Yusuke Aoki, Kazuyuki Shudo
http://arxiv.org/abs/1906.00719v1
• [cs.DC]Reconfigurable Atomic Transaction Commit (Extended Version)
Manuel Bravo, Alexey Gotsman
http://arxiv.org/abs/1906.01365v1
• [cs.DS]A Direct $\tilde{O}(1/ε)$ Iteration Parallel Algorithm for Optimal Transport
Arun Jambulapati, Aaron Sidford, Kevin Tian
http://arxiv.org/abs/1906.00618v1
• [cs.DS]Cores and Other Dense Structures in Complex Networks
Edoardo Galimberti
http://arxiv.org/abs/1906.01050v1
• [cs.DS]On the Use of Randomness in Local Distributed Graph Algorithms
Mohsen Ghaffari, Fabian Kuhn
http://arxiv.org/abs/1906.00482v1
• [cs.ET]In-memory hyperdimensional computing
Geethan Karunaratne, Manuel Le Gallo, Giovanni Cherubini, Luca Benini, Abbas Rahimi, Abu Sebastian
http://arxiv.org/abs/1906.01548v1
• [cs.GR]3D Magic Mirror: Automatic Video to 3D Caricature Translation
Yudong Guo, Luo Jiang, Lin Cai, Juyong Zhang
http://arxiv.org/abs/1906.00544v1
• [cs.HC]An Extensive Review of Computational Dance Automation Techniques and Applications
Manish Joshi, Sangeeta Jadhav
http://arxiv.org/abs/1906.00606v1
• [cs.IR]Contextually Propagated Term Weights for Document Representation
Casper Hansen, Christian Hansen, Stephen Alstrup, Jakob Grue Simonsen, Christina Lioma
http://arxiv.org/abs/1906.00674v1
• [cs.IR]Federated Hierarchical Hybrid Networks for Clickbait Detection
Feng Liao, Hankz Hankui Zhuo, Xiaoling Huang, Yu Zhang
http://arxiv.org/abs/1906.00638v1
• [cs.IR]Incorporating System-Level Objectives into Recommender Systems
Himan Abdollahpouri
http://arxiv.org/abs/1906.01435v1
• [cs.IR]Mining Data from the Congressional Record
Zhengyu Ma, Tianjiao Qi, James Route, Amir Ziai
http://arxiv.org/abs/1906.00529v1
• [cs.IR]Personalized Multimedia Item and Key Frame Recommendation
Le Wu1, Lei Chen1, Yonghui Yang1, Richang Hong1, Yong Ge2, Xing Xie3, Meng Wang1
http://arxiv.org/abs/1906.00246v1
• [cs.IR]Sequential Scenario-Specific Meta Learner for Online Recommendation
Zhengxiao Du, Xiaowei Wang, Hongxia Yang, Jingren Zhou, Jie Tang
http://arxiv.org/abs/1906.00391v1
• [cs.IR]Technology Knowledge Graph Based on Patent Data
Serhad Sarica, Jianxi Luo, Kristin L. Wood
http://arxiv.org/abs/1906.00411v2
• [cs.IR]Unsupervised Neural Generative Semantic Hashing
Casper Hansen, Christian Hansen, Jakob Grue Simonsen, Stephen Alstrup, Christina Lioma
http://arxiv.org/abs/1906.00671v1
• [cs.IR]User Profile Feature-Based Approach to Address the Cold Start Problem in Collaborative Filtering for Personalized Movie Recommendation
Lasitha Uyangoda, Supunmali Ahangama, Tharindu Ranasinghe
http://arxiv.org/abs/1906.00365v1
• [cs.IT]Closed-Form Analysis of Non-Linear Age-of-Information in Status Updates with an Energy Harvesting Transmitter
Xi Zheng, Sheng Zhou, Zhiyuan Jiang, Zhisheng Niu
http://arxiv.org/abs/1906.00192v1
• [cs.IT]Cooperative Downlink Interference Transmission and Cancellation for Cellular-Connected UAV: A Divide-and-Conquer Approach
Weidong Mei, Rui Zhang
http://arxiv.org/abs/1906.00220v1
• [cs.IT]Harvest-or-Transmit Policy for Cognitive Radio Networks: A Learning Theoretic Approach
Kalpant Pathak, Adrish Banerjee
http://arxiv.org/abs/1906.00548v1
• [cs.IT]Multi-dimensional Spectral Super-Resolution with Prior Knowledge via Frequency-Selective Vandermonde Decomposition and ADMM
Yinchuan Li, Xiaodong Wang, Zegang Ding
http://arxiv.org/abs/1906.00278v1
• [cs.IT]Performance Evaluation for the Co-existence of eMBB and URLLC Networks: Synchronized versus Unsynchronized TDD
Ursula Challita, Kimmo Hiltunen, Miurel Tercero
http://arxiv.org/abs/1906.00287v1
• [cs.IT]Probabilistic Existence Results for Parent-Identifying Schemes
Yujie Gu, Minquan Cheng, Grigory Kabatiansky, Ying Miao
http://arxiv.org/abs/1906.01031v1
• [cs.IT]The Classical Capacity of a Quantum Erasure Queue-Channel
Prabha Mandayam, Krishna Jagannathan, Avhishek Chatterjee
http://arxiv.org/abs/1906.01356v1
• [cs.IT]Transmit Power Policy and Ergodic Multicast Rate Analysis of Cognitive Radio Networks in Generalized Fading
Athira Subhash, Muralikrishnan Srinivasan, Sheetal Kalyani, Lajos Hanzo
http://arxiv.org/abs/1906.00598v1
• [cs.IT]Two Classes of New MDS Self-dual Codes over Finite Fields
Xiaolei Fang, Jinquan Luo
http://arxiv.org/abs/1906.00380v1
• [cs.LG]A Case for Backward Compatibility for Human-AI Teams
Gagan Bansal, Besmira Nushi, Ece Kamar, Dan Weld, Walter Lasecki, Eric Horvitz
http://arxiv.org/abs/1906.01148v1
• [cs.LG]A Fast-Optimal Guaranteed Algorithm For Learning Sub-Interval Relationships in Time Series
Saurabh Agrawal, Saurabh Verma, Anuj Karpatne, Stefan Liess, Snigdhansu Chatterjee, Vipin Kumar
http://arxiv.org/abs/1906.01450v1
• [cs.LG]A Language-Agnostic Model for Semantic Source Code Labeling
Ben Gelman, Bryan Hoyle, Jessica Moore, Joshua Saxe, David Slater
http://arxiv.org/abs/1906.01032v1
• [cs.LG]A Novel Hyperparameter-free Approach to Decision Tree Construction that Avoids Overfitting by Design
Rafael Garcia Leiva, Antonio Fernandez Anta, Vincenzo Mancuso, Paolo Casari
http://arxiv.org/abs/1906.01246v1
• [cs.LG]A Perspective on Objects and Systematic Generalization in Model-Based RL
Sjoerd van Steenkiste, Klaus Greff, Jürgen Schmidhuber
http://arxiv.org/abs/1906.01035v1
• [cs.LG]A Strong and Robust Baseline for Text-Image Matching
Fangyu Liu, Rongtian Ye
http://arxiv.org/abs/1906.01205v1
• [cs.LG]A necessary and sufficient stability notion for adaptive generalization
Katrina Ligett, Moshe Shenfeld
http://arxiv.org/abs/1906.00930v1
• [cs.LG]Achieving Fairness in Determining Medicaid Eligibility through Fairgroup Construction
Boli Fang, Miao Jiang, Jerry Shen
http://arxiv.org/abs/1906.00128v1
• [cs.LG]Achieving Generalizable Robustness of Deep Neural Networks by Stability Training
Jan Laermann, Wojciech Samek, Nils Strodthoff
http://arxiv.org/abs/1906.00735v1
• [cs.LG]Active Learning for Binary Classification with Abstention
Shubhanshu Shekhar, Mohammad Ghavamzadeh, Tara Javidi
http://arxiv.org/abs/1906.00303v1
• [cs.LG]Adaptive Online Learning for Gradient-Based Optimizers
Saeed Masoudian, Ali Arabzadeh, Mahdi Jafari Siavoshani, Milad Jalal, Alireza Amouzad
http://arxiv.org/abs/1906.00290v1
• [cs.LG]Adversarial Exploitation of Policy Imitation
Vahid Behzadan, William Hsu
http://arxiv.org/abs/1906.01121v1
• [cs.LG]Adversarial Risk Bounds for Neural Networks through Sparsity based Compression
Emilio Rafael Balda, Arash Behboodi, Niklas Koep, Rudolf Mathar
http://arxiv.org/abs/1906.00698v1
• [cs.LG]Adversarial Training Generalizes Data-dependent Spectral Norm Regularization
Kevin Roth, Yannic Kilcher, Thomas Hofmann
http://arxiv.org/abs/1906.01527v1
• [cs.LG]Adversarially Robust Generalization Just Requires More Unlabeled Data
Runtian Zhai, Tianle Cai, Di He, Chen Dan, Kun He, John Hopcroft, Liwei Wang
http://arxiv.org/abs/1906.00555v1
• [cs.LG]An Adaptable Framework for Deep Adversarial Label Learning from Weak Supervision
Chidubem Arachie, Bert Huang
http://arxiv.org/abs/1906.00512v1
• [cs.LG]An Efficient Graph Convolutional Network Technique for the Travelling Salesman Problem
Chaitanya K. Joshi, Thomas Laurent, Xavier Bresson
http://arxiv.org/abs/1906.01227v1
• [cs.LG]An Empirical Study on Hyperparameters and their Interdependence for RL Generalization
Xingyou Song, Yilun Du, Jacob Jackson
http://arxiv.org/abs/1906.00431v1
• [cs.LG]An interpretable machine learning framework for modelling human decision behavior
Mengzhuo Guo, Qingpeng Zhang, Xiuwu Liao, Youhua Chen
http://arxiv.org/abs/1906.01233v1
• [cs.LG]Analysis and Improvement of Adversarial Training in DQN Agents With Adversarially-Guided Exploration (AGE)
Vahid Behzadan, William Hsu
http://arxiv.org/abs/1906.01119v1
• [cs.LG]Approximation capability of neural networks on spaces of probability measures and tree-structured domains
Tomas Pevny, Vojtech Kovarik
http://arxiv.org/abs/1906.00764v1
• [cs.LG]Architecture Selection via the Trade-off Between Accuracy and Robustness
Zhun Deng, Cynthia Dwork, Jialiang Wang, Yao Zhao
http://arxiv.org/abs/1906.01354v1
• [cs.LG]Attributed Graph Clustering via Adaptive Graph Convolution
Xiaotong Zhang, Han Liu, Qimai Li, Xiao-Ming Wu
http://arxiv.org/abs/1906.01210v1
• [cs.LG]Autonomous Reinforcement Learning of Multiple Interrelated Tasks
Vieri Giuliano Santucci, Gianluca Baldassarre, Emilio Cartoni
http://arxiv.org/abs/1906.01374v1
• [cs.LG]Big-Data Clustering: K-Means or K-Indicators?
Feiyu Chen, Yuchen Yang, Liwei Xu, Taiping Zhang, Yin Zhang
http://arxiv.org/abs/1906.00938v1
• [cs.LG]Biomedical Named Entity Recognition via Reference-Set Augmented Bootstrapping
Joel Mathew, Shobeir Fakhraei, José Luis Ambite
http://arxiv.org/abs/1906.00282v1
• [cs.LG]Blow: a single-scale hyperconditioned flow for non-parallel raw-audio voice conversion
Joan Serrà, Santiago Pascual, Carlos Segura
http://arxiv.org/abs/1906.00794v1
• [cs.LG]Breaking Inter-Layer Co-Adaptation by Classifier Anonymization
Ikuro Sato, Kohta Ishikawa, Guoqing Liu, Masayuki Tanaka
http://arxiv.org/abs/1906.01150v1
• [cs.LG]C2P2: A Collective Cryptocurrency Up/Down Price Prediction Engine
Chongyang Bai, Tommy White, Linda Xiao, V. S. Subrahmanian, Ziheng Zhou
http://arxiv.org/abs/1906.00564v1
• [cs.LG]Characterizing and Forecasting User Engagement with In-app Action Graph: A Case Study of Snapchat
Yozen Liu, Xiaolin Shi, Lucas Pierce, Xiang Ren
http://arxiv.org/abs/1906.00355v1
• [cs.LG]Classification of Crop Tolerance to Heat and Drought: A Deep Convolutional Neural Networks Approach
Saeed Khaki, Zahra Khalilzadeh
http://arxiv.org/abs/1906.00454v1
• [cs.LG]Clustering by Orthogonal NMF Model and Non-Convex Penalty Optimization
Shuai Wang, Tsung-Hui Chang, Ying Cui, Jong-Shi Pang
http://arxiv.org/abs/1906.00570v1
• [cs.LG]Conditional Generative Models are not Robust
Ethan Fetaya, Jörn-Henrik Jacobsen, Richard Zemel
http://arxiv.org/abs/1906.01171v1
• [cs.LG]Continual Learning of New Sound Classes using Generative Replay
Zhepei Wang, Cem Subakan, Efthymios Tzinis, Paris Smaragdis, Laurent Charlin
http://arxiv.org/abs/1906.00654v1
• [cs.LG]Continual learning with hypernetworks
Johannes von Oswald, Christian Henning, João Sacramento, Benjamin F. Grewe
http://arxiv.org/abs/1906.00695v1
• [cs.LG]Cooperative neural networks (CoNN): Exploiting prior independence structure for improved classification
Harsh Shrivastava, Eugene Bart, Bob Price, Hanjun Dai, Bo Dai, Srinivas Aluru
http://arxiv.org/abs/1906.00291v1
• [cs.LG]Correctness Verification of Neural Networks
Yichen Yang, Martin Rinard
http://arxiv.org/abs/1906.01030v1
• [cs.LG]Coupled VAE: Improved Accuracy and Robustness of a Variational Autoencoder
Shichen Cao, Jingjing Li, Kenric P. Nelson, Mark A. Kon
http://arxiv.org/abs/1906.00536v1
• [cs.LG]DANE: Domain Adaptive Network Embedding
Yizhou Zhang, Guojie Song, Lun Du, Shuwen Yang, Yilun Jin
http://arxiv.org/abs/1906.00684v1
• [cs.LG]Data Sampling for Graph Based Unsupervised Learning: Convex and Greedy Optimization
Saeed Vahidian, Alexander Cloninger, Baharan Mirzasoleiman
http://arxiv.org/abs/1906.01021v1
• [cs.LG]Data-driven Estimation of Sinusoid Frequencies
Gautier Izacard, Sreyas Mohan, Carlos Fernandez-Granda
http://arxiv.org/abs/1906.00823v1
• [cs.LG]Deep Reasoning Networks: Thinking Fast and Slow
Di Chen, Yiwei Bai, Wenting Zhao, Sebastian Ament, John M. Gregoire, Carla P. Gomes
http://arxiv.org/abs/1906.00855v2
• [cs.LG]DiffQue: Estimating Relative Difficulty of Questions in Community Question Answering Services
Deepak Thukral, Adesh Pandey, Rishabh Gupta, Vikram Goyal, Tanmoy Chakraborty
http://arxiv.org/abs/1906.00145v1
• [cs.LG]Dimensionality compression and expansion in Deep Neural Networks
Stefano Recanatesi, Matthew Farrell, Madhu Advani, Timothy Moore, Guillaume Lajoie, Eric Shea-Brown
http://arxiv.org/abs/1906.00443v1
• [cs.LG]Discovering Neural Wirings
Mitchell Wortsman, Ali Farhadi, Mohammad Rastegari
http://arxiv.org/abs/1906.00586v1
• [cs.LG]Discriminative adversarial networks for positive-unlabeled learning
Fangqing Liu, Hui Chen, Hao Wu
http://arxiv.org/abs/1906.00642v1
• [cs.LG]Disparate Vulnerability: on the Unfairness of Privacy Attacks Against Machine Learning
Mohammad Yaghini, Bogdan Kulynych, Carmela Troncoso
http://arxiv.org/abs/1906.00389v1
• [cs.LG]Distribution oblivious, risk-aware algorithms for multi-armed bandits with unbounded rewards
Anmol Kagrecha, Jayakrishnan Nair, Krishna Jagannathan
http://arxiv.org/abs/1906.00569v1
• [cs.LG]Do place cells dream of conditional probabilities? Learning Neural Nyström representations
Mariano Tepper
http://arxiv.org/abs/1906.01102v1
• [cs.LG]Ease.ml/meter: Quantitative Overfitting Management for Human-in-the-loop ML Application Development
Frances Ann Hubis, Wentao Wu, Ce Zhang
http://arxiv.org/abs/1906.00299v2
• [cs.LG]Embedded hyper-parameter tuning by Simulated Annealing
Matteo Fischetti, Matteo Stringher
http://arxiv.org/abs/1906.01504v1
• [cs.LG]Encoder-Powered Generative Adversarial Networks
Jiseob Kim, Seungjae Jung, Hyundo Lee, Byoung-Tak Zhang
http://arxiv.org/abs/1906.00541v1
• [cs.LG]Encoding Invariances in Deep Generative Models
Viraj Shah, Ameya Joshi, Sambuddha Ghosal, Balaji Pokuri, Soumik Sarkar, Baskar Ganapathysubramanian, Chinmay Hegde
http://arxiv.org/abs/1906.01626v1
• [cs.LG]Enhancing Transformation-based Defenses using a Distribution Classifier
Connie Kou, Hwee Kuan Lee, Teck Khim Ng, Ee-Chien Chang
http://arxiv.org/abs/1906.00258v1
• [cs.LG]Episodic Memory in Lifelong Language Learning
Cyprien de Masson d’Autume, Sebastian Ruder, Lingpeng Kong, Dani Yogatama
http://arxiv.org/abs/1906.01076v1
• [cs.LG]Evaluating Scalable Bayesian Deep Learning Methods for Robust Computer Vision
Fredrik K. Gustafsson, Martin Danelljan, Thomas B. Schön
http://arxiv.org/abs/1906.01620v1
• [cs.LG]Exact Combinatorial Optimization with Graph Convolutional Neural Networks
Maxime Gasse, Didier Chételat, Nicola Ferroni, Laurent Charlin, Andrea Lodi
http://arxiv.org/abs/1906.01629v1
• [cs.LG]Exact inference in structured prediction
Kevin Bello, Jean Honorio
http://arxiv.org/abs/1906.00451v1
• [cs.LG]Factor Graph Neural Network
Zhen Zhang, Fan Wu, Wee Sun Lee
http://arxiv.org/abs/1906.00554v1
• [cs.LG]Fast and Stable Interval Bounds Propagation for Training Verifiably Robust Models
Paweł Morawiecki, Przemysław Spurek, Marek Śmieja, Jacek Tabor
http://arxiv.org/abs/1906.00628v1
• [cs.LG]Feature-Based Q-Learning for Two-Player Stochastic Games
Zeyu Jia, Lin F. Yang, Mengdi Wang
http://arxiv.org/abs/1906.00423v1
• [cs.LG]Gated recurrent units viewed through the lens of continuous time dynamical systems
Ian D. Jordan, Piotr Aleksander Sokol, Il Memming Park
http://arxiv.org/abs/1906.01005v1
• [cs.LG]Generating Diverse High-Fidelity Images with VQ-VAE-2
Ali Razavi, Aaron van den Oord, Oriol Vinyals
http://arxiv.org/abs/1906.00446v1
• [cs.LG]Generative Adversarial Networks: A Survey and Taxonomy
Zhengwei Wang, Qi She, Tomas E. Ward
http://arxiv.org/abs/1906.01529v1
• [cs.LG]Graduated Optimization of Black-Box Functions
Weijia Shao, Christian Geißler, Fikret Sivrikaya
http://arxiv.org/abs/1906.01279v1
• [cs.LG]HERA: Partial Label Learning by Combining Heterogeneous Loss with Sparse and Low-Rank Regularization
Gengyu Lyu, Songhe Feng, Yi Jin, Guojun Dai, Congyan Lang, Yidong Li
http://arxiv.org/abs/1906.00551v1
• [cs.LG]Hierarchical Auxiliary Learning
Jaehoon Cha, Kyeong Soo Kim, Sanghyuk Lee
http://arxiv.org/abs/1906.00852v1
• [cs.LG]Implicit Regularization in Deep Matrix Factorization
Sanjeev Arora, Nadav Cohen, Wei Hu, Yuping Luo
http://arxiv.org/abs/1905.13655v2
• [cs.LG]Information Competing Process for Learning Diversified Representations
Jie Hu, Rongrong Ji, ShengChuan Zhang, Xiaoshuai Sun, Qixiang Ye, Chia-Wen Lin, Qi Tian
http://arxiv.org/abs/1906.01288v1
• [cs.LG]Inverse boosting pruning trees for depression detection on Twitter
Lei Tong, Xiangrong, Qianni Zhang, Abdul Sadka, Ling Li, Huiyu Zhou
http://arxiv.org/abs/1906.00398v1
• [cs.LG]Kernel Instrumental Variable Regression
Rahul Singh, Maneesh Sahani, Arthur Gretton
http://arxiv.org/abs/1906.00232v1
• [cs.LG]Learner-aware Teaching: Inverse Reinforcement Learning with Preferences and Constraints
Sebastian Tschiatschek, Ahana Ghosh, Luis Haug, Rati Devidze, Adish Singla
http://arxiv.org/abs/1906.00429v1
• [cs.LG]Learning Attention-based Embeddings for Relation Prediction in Knowledge Graphs
Deepak Nathani, Jatin Chauhan, Charu Sharma, Manohar Kaul
http://arxiv.org/abs/1906.01195v1
• [cs.LG]Learning Domain Randomization Distributions for Transfer of Locomotion Policies
Melissa Mozifian, Juan Camilo Gamboa Higuera, David Meger, Gregory Dudek
http://arxiv.org/abs/1906.00410v1
• [cs.LG]Learning Interpretable Shapelets for Time Series Classification through Adversarial Regularization
Yichang Wang, Rémi Emonet, Elisa Fromont, Simon Malinowski, Etienne Menager, Loic Mosser, Romain Tavenard
http://arxiv.org/abs/1906.00917v1
• [cs.LG]Learning Representations by Maximizing Mutual Information Across Views
Philip Bachman, R Devon Hjelm, William Buchwalter
http://arxiv.org/abs/1906.00910v1
• [cs.LG]Learning Transferable Cooperative Behavior in Multi-Agent Teams
Akshat Agarwal, Sumit Kumar, Katia Sycara
http://arxiv.org/abs/1906.01202v1
• [cs.LG]Learning low-dimensional state embeddings and metastable clusters from time series data
Yifan Sun, Yaqi Duan, Hao Gong, Mengdi Wang
http://arxiv.org/abs/1906.00302v1
• [cs.LG]Learning to Clear the Market
Weiran Shen, Sébastien Lahaie, Renato Paes Leme
http://arxiv.org/abs/1906.01184v1
• [cs.LG]Load Balancing for Ultra-Dense Networks: A Deep Reinforcement Learning Based Approach
Yue Xu, Wenjun Xu, Zhi Wang, Jiaru Lin, Shuguang Cui
http://arxiv.org/abs/1906.00767v1
• [cs.LG]Low-rank Random Tensor for Bilinear Pooling
Yan Zhang, Krikamol Muandet, Qianli Ma, Heiko Neumann, Siyu Tang
http://arxiv.org/abs/1906.01004v1
• [cs.LG]Metric Learning for Individual Fairness
Christina Ilvento
http://arxiv.org/abs/1906.00250v1
• [cs.LG]Minimax bounds for structured prediction
Kevin Bello, Asish Ghoshal, Jean Honorio
http://arxiv.org/abs/1906.00449v1
• [cs.LG]Model selection for contextual bandits
Dylan J. Foster, Akshay Krishnamurthy, Haipeng Luo
http://arxiv.org/abs/1906.00531v1
• [cs.LG]Modular Universal Reparameterization: Deep Multi-task Learning Across Diverse Domains
Elliot Meyerson, Risto Miikkulainen
http://arxiv.org/abs/1906.00097v1
• [cs.LG]Near-Optimal Online Egalitarian learning in General Sum Repeated Matrix Games
Aristide Tossou, Christos Dimitrakakis, Jaroslaw Rzepecki, Katja Hofmann
http://arxiv.org/abs/1906.01609v1
• [cs.LG]Nemesyst: A Hybrid Parallelism Deep Learning-Based Framework Applied for Internet of Things Enabled Food Retailing Refrigeration Systems
George Onoufriou, Ronald Bickerton, Simon Pearson, Georgios Leontidis
http://arxiv.org/abs/1906.01600v1
• [cs.LG]Neural Network-based Object Classification by Known and Unknown Features (Based on Text Queries)
A. Artemov, I. Bolokhov, D. Kem, I. Khasenevich
http://arxiv.org/abs/1906.00800v1
• [cs.LG]NeuralDivergence: Exploring and Understanding Neural Networks by Comparing Activation Distributions
Haekyu Park, Fred Hohman, Duen Horng Chau
http://arxiv.org/abs/1906.00332v1
• [cs.LG]NodeDrop: A Condition for Reducing Network Size without Effect on Output
Louis Jensen, Jacob Harer, Sang Chin
http://arxiv.org/abs/1906.01026v1
• [cs.LG]Nonstochastic Multiarmed Bandits with Unrestricted Delays
Tobias Sommer Thune, Nicolò Cesa-Bianchi, Yevgeny Seldin
http://arxiv.org/abs/1906.00670v1
• [cs.LG]Off-Policy Evaluation via Off-Policy Classification
Alex Irpan, Kanishka Rao, Konstantinos Bousmalis, Chris Harris, Julian Ibarz, Sergey Levine
http://arxiv.org/abs/1906.01624v1
• [cs.LG]On Gradient Descent Ascent for Nonconvex-Concave Minimax Problems
Tianyi Lin, Chi Jin, Michael I. Jordan
http://arxiv.org/abs/1906.00331v1
• [cs.LG]On Privacy Protection of Latent Dirichlet Allocation Model Training
Fangyuan Zhao, Xuebin Ren, Shusen Yang, Xinyu Yang
http://arxiv.org/abs/1906.01178v1
• [cs.LG]On The Radon—Nikodym Spectral Approach With Optimal Clustering
Vladislav Gennadievich Malyshkin
http://arxiv.org/abs/1906.00460v1
• [cs.LG]On the Correctness and Sample Complexity of Inverse Reinforcement Learning
Abi Komanduru, Jean Honorio
http://arxiv.org/abs/1906.00422v1
• [cs.LG]On the computational complexity of the probabilistic label tree algorithms
Robert Busa-Fekete, Krzysztof Dembczynski, Alexander Golovnev, Kalina Jasinska, Mikhail Kuznetsov, Maxim Sviridenko, Chao Xu
http://arxiv.org/abs/1906.00294v1
• [cs.LG]One-Way Prototypical Networks
Anna Kruspe
http://arxiv.org/abs/1906.00820v1
• [cs.LG]Optimal Learning of Mallows Block Model
Róbert Busa-Fekete, Dimitris Fotakis, Balázs Szörényi, Manolis Zampetakis
http://arxiv.org/abs/1906.01009v1
• [cs.LG]Optimal Transport on the Manifold of SPD Matrices for Domain Adaptation
Or Yair, Felix Dietrich, Ronen Talmon, Ioannis G. Kevrekidis
http://arxiv.org/abs/1906.00616v1
• [cs.LG]Optimal Unsupervised Domain Translation
Emmanuel de Bézenac, Ibrahim Ayed, Patrick Gallinari
http://arxiv.org/abs/1906.01292v1
• [cs.LG]Options as responses: Grounding behavioural hierarchies in multi-agent RL
Alexander Sasha Vezhnevets, Yuhuai Wu, Remi Leblond, Joel Leibo
http://arxiv.org/abs/1906.01470v1
• [cs.LG]PCA-driven Hybrid network design for enabling Intelligence at the Edge
Indranil Chakraborty, Deboleena Roy, Isha Garg, Aayush Ankit, Kaushik Roy
http://arxiv.org/abs/1906.01493v1
• [cs.LG]Posterior Variance Analysis of Gaussian Processes with Application to Average Learning Curves
Armin Lederer, Jonas Umlauft, Sandra Hirche
http://arxiv.org/abs/1906.01404v1
• [cs.LG]Proximal Reliability Optimization for Reinforcement Learning
Narendra Patwardhan, Zequn Wang
http://arxiv.org/abs/1906.01127v1
• [cs.LG]Quantifying Point-Prediction Uncertainty in Neural Networks via Residual Estimation with an I/O Kernel
Xin Qiu, Elliot Meyerson, Risto Miikkulainen
http://arxiv.org/abs/1906.00588v1
• [cs.LG]RL-Based Method for Benchmarking the Adversarial Resilience and Robustness of Deep Reinforcement Learning Policies
Vahid Behzadan, William Hsu
http://arxiv.org/abs/1906.01110v1
• [cs.LG]Radial-Based Undersampling for Imbalanced Data Classification
Michał Koziarski
http://arxiv.org/abs/1906.00452v1
• [cs.LG]Robust Learning Under Label Noise With Iterative Noise-Filtering
Duc Tam Nguyen, Thi-Phuong-Nhung Ngo, Zhongyu Lou, Michael Klar, Laura Beggel, Thomas Brox
http://arxiv.org/abs/1906.00216v1
• [cs.LG]Self-supervised Body Image Acquisition Using a Deep Neural Network for Sensorimotor Prediction
Alban Laflaquière, Verena V. Hafner
http://arxiv.org/abs/1906.00825v1
• [cs.LG]Sequential Triggers for Watermarking of Deep Reinforcement Learning Policies
Vahid Behzadan, William Hsu
http://arxiv.org/abs/1906.01126v1
• [cs.LG]Sparse Representation Classification via Screening for Graphs
Cencheng Shen, Li Chen, Yuexiao Dong, Carey Priebe
http://arxiv.org/abs/1906.01601v1
• [cs.LG]Stabilizing Off-Policy Q-Learning via Bootstrapping Error Reduction
Aviral Kumar, Justin Fu, George Tucker, Sergey Levine
http://arxiv.org/abs/1906.00949v1
• [cs.LG]Statistically Significant Discriminative Patterns Searching
Hoang Son Pham, Gwendal Virlet, Dominique Lavenier, Alexandre Termier
http://arxiv.org/abs/1906.01581v1
• [cs.LG]Super-resolution of Time-series Labels for Bootstrapped Event Detection
Ivan Kiskin, Udeepa Meepegama, Steven Roberts
http://arxiv.org/abs/1906.00254v1
• [cs.LG]Temporal Density Extrapolation using a Dynamic Basis Approach
Georg Krempl, Dominik Lang, Vera Hofer
http://arxiv.org/abs/1906.00912v1
• [cs.LG]The Convergence Rate of Neural Networks for Learned Functions of Different Frequencies
Ronen Basri, David Jacobs, Yoni Kasten, Shira Kritchman
http://arxiv.org/abs/1906.00425v1
• [cs.LG]The Intrinsic Robustness of Stochastic Bandits to Strategic Manipulation
Zhe Feng, David C. Parkes, Haifeng Xu
http://arxiv.org/abs/1906.01528v1
• [cs.LG]The Principle of Unchanged Optimality in Reinforcement Learning Generalization
Alex Irpan, Xingyou Song
http://arxiv.org/abs/1906.00336v1
• [cs.LG]Topological Autoencoders
Michael Moor, Max Horn, Bastian Rieck, Karsten Borgwardt
http://arxiv.org/abs/1906.00722v1
• [cs.LG]Toward Building Conversational Recommender Systems: A Contextual Bandit Approach
Xiaoying Zhang, Hong Xie, Hang Li, John C. S. Lui
http://arxiv.org/abs/1906.01219v1
• [cs.LG]Towards Interactive Training of Non-Player Characters in Video Games
Igor Borovikov, Jesse Harder, Michael Sadovsky, Ahmad Beirami
http://arxiv.org/abs/1906.00535v1
• [cs.LG]Truncated Cauchy Non-negative Matrix Factorization
Naiyang Guan, Tongliang Liu, Yangmuzi Zhang, Dacheng Tao, Larry S. Davis
http://arxiv.org/abs/1906.00495v1
• [cs.LG]Uniform Error Bounds for Gaussian Process Regression with Application to Safe Control
Armin Lederer, Jonas Umlauft, Sandra Hirche
http://arxiv.org/abs/1906.01376v1
• [cs.LG]Unsupervised Emergence of Egocentric Spatial Structure from Sensorimotor Prediction
Alban Laflaquière, Michael Garcia Ortiz
http://arxiv.org/abs/1906.01401v1
• [cs.LG]Using a Logarithmic Mapping to Enable Lower Discount Factors in Reinforcement Learning
Harm van Seijen, Mehdi Fatemi, Arash Tavakoli
http://arxiv.org/abs/1906.00572v1
• [cs.LG]Wasserstein Weisfeiler-Lehman Graph Kernels
Matteo Togninalli, Elisabetta Ghisu, Felipe Llinares-López, Bastian Rieck, Karsten Borgwardt
http://arxiv.org/abs/1906.01277v1
• [cs.LG]Weakly Supervised Disentanglement by Pairwise Similarities
Junxiang Chen, Kayhan Batmanghelich
http://arxiv.org/abs/1906.01044v1
• [cs.LG]Y-GAN: A Generative Adversarial Network for Depthmap Estimation from Multi-camera Stereo Images
Miguel Alonso Jr
http://arxiv.org/abs/1906.00932v1
• [cs.LO]Reasoning about disclosure in data integration in the presence of source constraints
Michael Benedikt, Pierre Bourhis, Louis Jachiet, Michaël Thomazo
http://arxiv.org/abs/1906.00624v1
• [cs.MA]Multiple Drones driven Hexagonally Partitioned Area Exploration: Simulation and Evaluation
Ayush Datta, Rahul Tallamraju, Kamalakar Karlapalem
http://arxiv.org/abs/1906.00401v1
• [cs.NA]Learning Neural PDE Solvers with Convergence Guarantees
Jun-Ting Hsieh, Shengjia Zhao, Stephan Eismann, Lucia Mirabella, Stefano Ermon
http://arxiv.org/abs/1906.01200v1
• [cs.NE]Hamiltonian Neural Networks
Sam Greydanus, Misko Dzamba, Jason Yosinski
http://arxiv.org/abs/1906.01563v1
• [cs.NE]Kinetic Market Model: An Evolutionary Algorithm
Evandro Luquini, Nizam Omar
http://arxiv.org/abs/1906.01241v1
• [cs.NE]Multi-objective Pruning for CNNs using Genetic Algorithm
Chuanguang Yang, Zhulin An, Chao Li, Boyu Diao, Yongjun Xu
http://arxiv.org/abs/1906.00399v1
• [cs.NE]Neural networks grown and self-organized by noise
Guruprasad Raghavan, Matt Thomson
http://arxiv.org/abs/1906.01039v1
• [cs.NE]Push and Pull Search Embedded in an M2M Framework for Solving Constrained Multi-objective Optimization Problems
Zhun Fan, Zhaojun Wang, Wenji Li, Yutong Yuan, Yugen You, Zhi Yang, Fuzan Sun, Jie Ruan, Zhaocheng Li
http://arxiv.org/abs/1906.00402v1
• [cs.NE]SpikeGrad: An ANN-equivalent Computation Model for Implementing Backpropagation with Spikes
Johannes Christian Thiele, Olivier Bichler, Antoine Dupret
http://arxiv.org/abs/1906.00851v1
• [cs.NE]Training Detection-Range-Frugal Cooperative Collision Avoidance Models for Quadcopters via Neuroevolution
Amir Behjat, Krushang Gabani, Souma Chowdhury
http://arxiv.org/abs/1906.00052v1
• [cs.NI]Blockchain for Internet of Things: A Survey
Hong-Ning Dai, Zibin Zheng, Yan Zhang
http://arxiv.org/abs/1906.00245v1
• [cs.NI]Cellular Traffic Prediction and Classification: a comparative evaluation of LSTM and ARIMA
Amin Azari, Panagiotis Papapetrou, Stojan Denic, Gunnar Peters
http://arxiv.org/abs/1906.00939v1
• [cs.NI]On Provisioning Cellular Networks for Distributed Inference
Sarabjot Singh
http://arxiv.org/abs/1906.01602v1
• [cs.NI]Probabilistic QoS-aware Placement of VNF chains at the Edge
Antonio Brogi, Stefano Forti, Federica Paganelli
http://arxiv.org/abs/1906.00197v1
• [cs.OS]Cache Contention on Multicore Systems: An Ontology-based Approach
Maruthi Rohit Ayyagari
http://arxiv.org/abs/1906.00834v1
• [cs.RO]Air Learning: An AI Research Platform for Algorithm-Hardware Benchmarking of Autonomous Aerial Robots
Srivatsan Krishnan, Behzad Borojerdian, William Fu, Aleksandra Faust, Vijay Janapa Reddi
http://arxiv.org/abs/1906.00421v1
• [cs.RO]Analysis of Obstacle based Probabilistic RoadMap Method using Geometric Probability
Titas Bera, M. Seetharama Bhat, Debasish Ghose
http://arxiv.org/abs/1906.00136v1
• [cs.RO]Closed-Loop Control of a Delta-Wing Unmanned Aerial-Aquatic Vehicle
Joseph Moore
http://arxiv.org/abs/1906.01532v1
• [cs.RO]Effects of Different Hand-Grounding Locations on Haptic Performance With a Wearable Kinesthetic Haptic Device
Sajid Nisar, Melisa Orta Martinez, Takahiro Endo, Fumitoshi Matsuno, Allison M. Okamura
http://arxiv.org/abs/1906.00430v1
• [cs.RO]GAMMA: A General Agent Motion Prediction Model for Autonomous Driving
Yuanfu Luo, Panpan Cai
http://arxiv.org/abs/1906.01566v1
• [cs.RO]Grid-based Localization Stack for Inspection Drones towards Automation of Large Scale Warehouse Systems
Ashwary Anand, Shubh Agrawal, Shivang Agrawal, Aman Chandra, Krishnakant Deshmukh
http://arxiv.org/abs/1906.01299v1
• [cs.RO]Harnessing Reinforcement Learning for Neural Motion Planning
Tom Jurgenson, Aviv Tamar
http://arxiv.org/abs/1906.00214v1
• [cs.RO]Knowledge is Never Enough: Towards Web Aided Deep Open World Recognition
Massimiliano Mancini, Hakan Karaoguz, Elisa Ricci, Patric Jensfelt, Barbara Caputo
http://arxiv.org/abs/1906.01258v1
• [cs.RO]Localization Requirements for Autonomous Vehicles
Tyler G. R. Reid, Sarah E. Houts, Robert Cammarata, Graham Mills, Siddharth Agarwal, Ankit Vora, Gaurav Pandey
http://arxiv.org/abs/1906.01061v1
• [cs.RO]Longitudinal Trajectory Prediction of Human-driven Vehicles Near Traffic Lights
Geunseob Oh, Huei Peng
http://arxiv.org/abs/1906.00486v1
• [cs.RO]Rapidly-Exploring Quotient-Space Trees: Motion Planning using Sequential Simplifications
Andreas Orthey, Marc Toussaint
http://arxiv.org/abs/1906.01350v1
• [cs.RO]Socially Inspired Communication in Swarm Robotics
Nathan White, John Harwell, Maria Gini
http://arxiv.org/abs/1906.01108v1
• [cs.RO]Vision-Based Autonomous UAV Navigation and Landing for Urban Search and Rescue
Mayank Mittal, Rohit Mohan, Wolfram Burgard, Abhinav Valada
http://arxiv.org/abs/1906.01304v1
• [cs.SD]A Surprising Density of Illusionable Natural Speech
Melody Y. Guan, Gregory Valiant
http://arxiv.org/abs/1906.01040v1
• [cs.SE]Gamification of Enterprise Systems: A Synthesis of Mechanics, Dynamics, and Risks
M. Schmidt-Kraepelin, S. Lins, S. Thiebes, A. Sunyaev
http://arxiv.org/abs/1906.01577v1
• [cs.SE]Neural Bug Finding: A Study of Opportunities and Challenges
Andrew Habib, Michael Pradel
http://arxiv.org/abs/1906.00307v1
• [cs.SI]Can Women Break the Glass Ceiling?: An Analysis of #MeToo Hashtagged Posts on Twitter
Naeemul Hassan, Manash Kumar Mandal, Mansurul Bhuiyan, Aparna Moitra, Syed Ishtiaque Ahmed
http://arxiv.org/abs/1906.00896v1
• [cs.SI]Evaluating network partitions through visualization
Chihiro Noguchi, Tatsuro Kawamoto
http://arxiv.org/abs/1906.00699v2
• [cs.SI]Implication Avoiding Dynamics for Externally Observed Networks
Joel Nishimura, Oscar Goodloe
http://arxiv.org/abs/1906.01118v1
• [cs.SI]Need for Critical Cyber Defence, Security Strategy and Privacy Policy in Bangladesh - Hype or Reality?
AKM Bahalul Haque
http://arxiv.org/abs/1906.01285v1
• [cs.SI]The Strength of Structural Diversity in Online Social Networks
Yafei Zhang, Lin Wang, Jonathan J. H. Zhu, Xiaofan Wang, Alex ‘Sandy’ Pentland
http://arxiv.org/abs/1906.00756v1
• [cs.SI]Understanding the Silence of Sexual Harassment Victims Through the #WhyIDidntReport Movement
Abigail Garrett, Naeemul Hassan
http://arxiv.org/abs/1906.00895v1
• [cs.SY]Robust stability of moving horizon estimation for nonlinear systems with bounded disturbances using adaptive arrival cost
Nestor N. Deniz, Marina H. Murillo, Guido Sanchez, Lucas M. Genzelis, Leonardo Giovanini
http://arxiv.org/abs/1906.01060v1
• [eess.AS]Evaluating Non-aligned Musical Score Transcriptions with MV2H
Andrew McLeod
http://arxiv.org/abs/1906.00566v1
• [eess.AS]MelNet: A Generative Model for Audio in the Frequency Domain
Sean Vasquez, Mike Lewis
http://arxiv.org/abs/1906.01083v1
• [eess.IV]A Semantic-based Medical Image Fusion Approach
Fanda Fan, Yunyou Huang, Lei Wang, Xingwang Xiong, Zihan Jiang, Zhifei Zhang, Jianfeng Zhan
http://arxiv.org/abs/1906.00225v1
• [eess.IV]Deep Feature Learning from a Hospital-Scale Chest X-ray Dataset with Application to TB Detection on a Small-Scale Dataset
Ophir Gozes, Hayit Greenspan
http://arxiv.org/abs/1906.00768v1
• [eess.IV]Learning Deep Image Priors for Blind Image Denoising
Xianxu Hou, Hongming Luo, Jingxin Liu, Bolei Xu, Ke Sun, Yuanhao Gong, Bozhi Liu, Guoping Qiu
http://arxiv.org/abs/1906.01259v1
• [eess.IV]Lung cancer screening with low-dose CT scans using a deep learning approach
Jason L. Causey, Yuanfang Guan, Wei Dong, Karl Walker, Jake A. Qualls, Fred Prior, Xiuzhen Huang
http://arxiv.org/abs/1906.00240v1
• [eess.IV]Natural Image Noise Dataset
Benoit Brummer, Christophe De Vleeschouwer
http://arxiv.org/abs/1906.00270v1
• [eess.IV]Probabilistic Noise2Void: Unsupervised Content-Aware Denoising
Alexander Krull, Tomas Vicar, Florian Jug
http://arxiv.org/abs/1906.00651v2
• [eess.SP]A Nonlinear Acceleration Method for Iterative Algorithms
Mahdi Shamsi, Mahmoud Ghandi, Farokh Marvasti
http://arxiv.org/abs/1906.01595v1
• [eess.SP]Deep Reinforcement Learning Architecture for Continuous Power Allocation in High Throughput Satellites
Juan Jose Garau Luis, Markus Guerster, Inigo del Portillo, Edward Crawley, Bruce Cameron
http://arxiv.org/abs/1906.00571v1
• [eess.SP]Deterministic and stochastic damage detection via dynamic response analysis
Michael Oberguggenberger, Martin Schwarz
http://arxiv.org/abs/1906.00797v1
• [eess.SP]Gridless Variational Bayesian Channel Estimation for Antenna Array Systems with Low Resolution ADCs
Jiang Zhu, Chao-kai Wen, Jun Tong, Chongbin Xu, Shi Jin
http://arxiv.org/abs/1906.00576v1
• [eess.SP]Sparse Bayesian Learning Approach for Discrete Signal Reconstruction
Jisheng Dai, An Liu, Hing Cheung So
http://arxiv.org/abs/1906.00309v1
• [eess.SP]What, Where and How to Transfer in SAR Target Recognition Based on Deep CNNs
Zhongling Huang, Zongxu Pan, Bin Lei
http://arxiv.org/abs/1906.01379v1
• [hep-ph]Effective LHC measurements with matrix elements and machine learning
Johann Brehmer, Kyle Cranmer, Irina Espejo, Felix Kling, Gilles Louppe, Juan Pavez
http://arxiv.org/abs/1906.01578v1
• [math.AT]A numerical measure of the instability of Mapper-type algorithms
Francisco Belchí, Jacek Brodzki, Matthew Burfitt, Mahesan Niranjan
http://arxiv.org/abs/1906.01507v1
• [math.DG]Optimal transport and information geometry
Ting-Kam Leonard Wong, Jiaowen Yang
http://arxiv.org/abs/1906.00030v1
• [math.NA]Exploiting nested task-parallelism in the $\mathcal{H}-LU$ factorization
Rocío Carratalá-Sáez, Sven Christophersen, José I. Aliaga, Vicenç Beltran, Steffen Börm, Enrique S. Quintana-Ortí
http://arxiv.org/abs/1906.00874v1
• [math.OC]A Generic Acceleration Framework for Stochastic Composite Optimization
Andrei Kulunchakov, Julien Mairal
http://arxiv.org/abs/1906.01164v1
• [math.OC]Adaptive Model Refinement with Batch Bayesian Sampling for Optimization of Bio-inspired Flow Tailoring
Payam Ghassemi, Sumeet Sanjay Lulekar, Souma Chowdhury
http://arxiv.org/abs/1906.00793v1
• [math.OC]Data-Pooling in Stochastic Optimization
Vishal Gupta, Nathan Kallus
http://arxiv.org/abs/1906.00255v1
• [math.OC]Generalized Momentum-Based Methods: A Hamiltonian Perspective
Jelena Diakonikolas, Michael I. Jordan
http://arxiv.org/abs/1906.00436v1
• [math.OC]Higher-Order Accelerated Methods for Faster Non-Smooth Optimization
Brian Bullins, Richard Peng
http://arxiv.org/abs/1906.01621v1
• [math.OC]Proximal Point Approximations Achieving a Convergence Rate of $\mathcal{O}(1/k)$ for Smooth Convex-Concave Saddle Point Problems: Optimistic Gradient and Extra-gradient Methods
Aryan Mokhtari, Asuman Ozdaglar, Sarath Pattathil
http://arxiv.org/abs/1906.01115v1
• [math.OC]Robust exploration in linear quadratic reinforcement learning
Jack Umenberger, Mina Ferizbegovic, Thomas B. Schön, Håkan Hjalmarsson
http://arxiv.org/abs/1906.01584v1
• [math.OC]Towards Unified Acceleration of High-Order Algorithms under Hölder Continuity and Uniform Convexity
Chaobing Song, Yi Ma
http://arxiv.org/abs/1906.00582v1
• [math.ST]A mean-field limit for certain deep neural networks
Dyego Araújo, Roberto I. Oliveira, Daniel Yukimura
http://arxiv.org/abs/1906.00193v1
• [math.ST]A test against trend in random sequences
Peter Lindqvist
http://arxiv.org/abs/1906.00752v1
• [math.ST]Asymptotic Properties of Neural Network Sieve Estimators
Xiaoxi Shen, Chang Jiang, Lyudmila Sakhanenko, Qing Lu
http://arxiv.org/abs/1906.00875v1
• [math.ST]Confidence Regions in Wasserstein Distributionally Robust Estimation
Jose Blanchet, Karthyek Murthy, Nian Si
http://arxiv.org/abs/1906.01614v1
• [math.ST]How many variables should be entered in a principal component regression equation?
Ji Xu, Daniel Hsu
http://arxiv.org/abs/1906.01139v1
• [math.ST]Inference robust to outliers with l1-norm penalization
Jad Beyhum
http://arxiv.org/abs/1906.01302v1
• [math.ST]Multi-reference factor analysis: low-rank covariance estimation under unknown translations
Boris Landa, Yoel Shkolnisky
http://arxiv.org/abs/1906.00211v1
• [math.ST]On Testing for Parameters in Ising Models
Rajarshi Mukherjee, Gourab Ray
http://arxiv.org/abs/1906.00456v1
• [math.ST]Robust Mean Estimation with the Bayesian Median of Means
Paulo Orenstein
http://arxiv.org/abs/1906.01204v1
• [math.ST]Semiparametric Analysis of the Proportional Likelihood Ratio Model and Omnibus Estimation Procedure
Yair Goldberg, Malka Gorfine
http://arxiv.org/abs/1906.00723v1
• [physics.comp-ph]A new nonlocal forward model for diffuse optical tomography
Wenqi Lu, Jinming Duan, Joshua Deepak Veesa, Iain B. Styles
http://arxiv.org/abs/1906.00882v1
• [physics.data-an]Revision of ISO 19229 to support the certification of calibration gases for purity
Adriaan M. H. van der Veen, Gerard Nieuwenkamp
http://arxiv.org/abs/1906.01216v1
• [q-bio.NC]A detailed study of recurrent neural networks used to model tasks in the cerebral cortex
C. Jarne, R. Laje
http://arxiv.org/abs/1906.01094v1
• [q-bio.NC]Learning to solve the credit assignment problem
Benjamin James Lansdell, Prashanth Prakash, Konrad Paul Kording
http://arxiv.org/abs/1906.00889v1
• [q-bio.NC]Signal Coding and Perfect Reconstruction using Spike Trains
Anik Chattopadhyay, Arunava Banerjee
http://arxiv.org/abs/1906.00092v1
• [q-fin.ST]Conditional inference on the asset with maximum Sharpe ratio
Steven E. Pav
http://arxiv.org/abs/1906.00573v1
• [stat.AP]A Dyadic IRT Model
Brian Gin, Nicholas Sim, Anders Skrondal, Sophia Rabe-Hesketh
http://arxiv.org/abs/1906.01100v1
• [stat.AP]Encouraging Equitable Bikeshare: Implications of Docked and Dockless Models for Spatial Equity
Simon Couch, Heather Kitada Smalley
http://arxiv.org/abs/1906.00129v1
• [stat.AP]Generalised linear models for prognosis and intervention: Theory, practice, and implications for machine learning
Kellyn F. Arnold, Vinny Davies, Marc de Kamps, Peter W. G. Tennant, John Mbotwa, Mark S. Gilthorpe
http://arxiv.org/abs/1906.01461v1
• [stat.AP]Joint spatial modeling of significant wave height and wave period using the SPDE approach
Anders Hildeman, David Bolin, Igor Rychlik
http://arxiv.org/abs/1906.00286v1
• [stat.AP]Model Trees for Personalization
Ali Aouad, Adam N. Elmachtoub, Kris J. Ferreira, Ryan McNellis
http://arxiv.org/abs/1906.01174v1
• [stat.AP]Statistical analysis of the water level of Huang He river (Yellow river) in China
Wang Bo, Zlatinka I. Dimitrova, Nikolay K. Vitanov
http://arxiv.org/abs/1906.00168v1
• [stat.AP]Stress Testing Network Reconstruction via Graphical Causal Mode
Helder Rojas, David Dias
http://arxiv.org/abs/1906.01468v1
• [stat.CO]lspartition: Partitioning-Based Least Squares Regression
Matias D. Cattaneo, Max H. Farrell, Yingjie Feng
http://arxiv.org/abs/1906.00202v1
• [stat.CO]nprobust: Nonparametric Kernel-Based Estimation and Robust Bias-Corrected Inference
Sebastian Calonico, Matias D. Cattaneo, Max H. Farrell
http://arxiv.org/abs/1906.00198v1
• [stat.ME]Anchored Causal Inference in the Presence of Measurement Error
Basil Saeed, Anastasiya Belyaeva, Yuhao Wang, Caroline Uhler
http://arxiv.org/abs/1906.00928v1
• [stat.ME]Bayesian Profiling Multiple Imputation for Missing Electronic Health Records
Yajuan Si, Mari Palta, Maureen Smith
http://arxiv.org/abs/1906.00042v1
• [stat.ME]Central Quantile Subspace
Eliana Christou
http://arxiv.org/abs/1906.00694v1
• [stat.ME]Clustering Multivariate Data using Factor Analytic Bayesian Mixtures with an Unknown Number of Components
Panagiotis Papastamoulis
http://arxiv.org/abs/1906.00348v1
• [stat.ME]Combining Heterogeneous Spatial Datasets with Process-based Spatial Fusion Models: A Unifying Framework
Craig Wang, Reinhard Furrer
http://arxiv.org/abs/1906.00364v1
• [stat.ME]Comprehensive cluster validity Index based on structural simplicity
Anri Mutoh, Masamichi Wada, Kou Amano
http://arxiv.org/abs/1906.00349v1
• [stat.ME]Confidence Intervals for Selected Parameters
Yoav Benjamini, Yotam Hechtlinger, Philip B. Stark
http://arxiv.org/abs/1906.00505v1
• [stat.ME]Copula-based functional Bayes classification with principal components and partial least squares
Wentian Huang, David Ruppert
http://arxiv.org/abs/1906.00538v1
• [stat.ME]Covariate-Powered Empirical Bayes Estimation
Nikolaos Ignatiadis, Stefan Wager
http://arxiv.org/abs/1906.01611v1
• [stat.ME]Diagonally-Dominant Principal Component Analysis
Zheng Tracy Ke, Lingzhou Xue, Fan Yang
http://arxiv.org/abs/1906.00051v1
• [stat.ME]Estimating Real Log Canonical Thresholds
Toru Imai
http://arxiv.org/abs/1906.01341v1
• [stat.ME]Estimating Time-Varying Causal Excursion Effect in Mobile Health with Binary Outcomes
Tianchen Qian, Hyesun Yoo, Predrag Klasnja, Daniel Almirall, Susan A. Murphy
http://arxiv.org/abs/1906.00528v1
• [stat.ME]Functional time series prediction under partial observation of the future curve
Shuhao Jiao
http://arxiv.org/abs/1906.00281v1
• [stat.ME]Gap-Measure Tests with Applications to Data Integrity Verification
Truc Le, Jeffrey Uhlmann
http://arxiv.org/abs/1906.01465v1
• [stat.ME]Generating Poisson-Distributed Differentially Private Synthetic Data
Harrison Quick
http://arxiv.org/abs/1906.00455v1
• [stat.ME]Kernel Mean Embedding Based Hypothesis Tests for Comparing Spatial Point Patterns
Raif M. Rustamov, James T. Klosowski
http://arxiv.org/abs/1906.00116v1
• [stat.ME]Mixture of hidden Markov models for accelerometer data
Marie du Roy de Chaumaray, Matthieu Marbac, Fabien Navarro
http://arxiv.org/abs/1906.01547v1
• [stat.ME]Multiplicative Effect Modeling: The General Case
Jiaqi Yin, Thomas S. Richardson, Linbo Wang
http://arxiv.org/abs/1906.00558v1
• [stat.ME]Partial and semi-partial measures of spatial associations for multivariate lattice data
Matthias Eckardt, Jorge Mateu
http://arxiv.org/abs/1906.01484v1
• [stat.ME]Semi-parametric Bayesian variable selection for gene-environment interactions
Jie Ren, Fei Zhou, Xiaoxi Li, Qi Chen, Hongmei Zhang, Shuangge Ma, Yu Jiang, Cen Wu
http://arxiv.org/abs/1906.01057v1
• [stat.ME]Statistical methods for biomarker data pooled from multiple nested case-control studies
Abigail Sloan, Molin Wang
http://arxiv.org/abs/1906.00818v1
• [stat.ME]The performance of the partially overlapping samples t-tests at the limits
Ben Derrick, Deirdre Toher, Paul White
http://arxiv.org/abs/1906.01006v1
• [stat.ME]Transformed Central Quantile Subspace
Eliana Christou
http://arxiv.org/abs/1906.00696v1
• [stat.ME]Unconstrained representation of orthogonal matrices with application to common principle components
Luca Bagnato, Antonio Punzo
http://arxiv.org/abs/1906.00587v1
• [stat.ML]A Mean Field Theory of Quantized Deep Networks: The Quantization-Depth Trade-Off
Yaniv Blumenfeld, Dar Gilboa, Daniel Soudry
http://arxiv.org/abs/1906.00771v1
• [stat.ML]An Evaluation Toolkit to Guide Model Selection and Cohort Definition in Causal Inference
Yishai Shimoni, Ehud Karavani, Sivan Ravid, Peter Bak, Tan Hung Ng, Sharon Hensley Alford, Denise Meade, Yaara Goldschmidt
http://arxiv.org/abs/1906.00442v1
• [stat.ML]Assessing Algorithmic Fairness with Unobserved Protected Class Using Data Combination
Nathan Kallus, Xiaojie Mao, Angela Zhou
http://arxiv.org/abs/1906.00285v1
• [stat.ML]Assessing Disparate Impacts of Personalized Interventions: Identifiability and Bounds
Nathan Kallus, Angela Zhou
http://arxiv.org/abs/1906.01552v1
• [stat.ML]Bayesian Optimization of Composite Functions
Raul Astudillo, Peter I. Frazier
http://arxiv.org/abs/1906.01537v1
• [stat.ML]Bayesian Prior Networks with PAC Training
Manuel Haussmann, Sebastian Gerwinn, Melih Kandemir
http://arxiv.org/abs/1906.00816v1
• [stat.ML]BreGMN: scaled-Bregman Generative Modeling Networks
Akash Srivastava, Kristjan Greenewald, Farzaneh Mirzazadeh
http://arxiv.org/abs/1906.00313v1
• [stat.ML]Capabilities and Limitations of Time-lagged Autoencoders for Slow Mode Discovery in Dynamical Systems
Wei Chen, Hythem Sidky, Andrew L. Ferguson
http://arxiv.org/abs/1906.00325v1
• [stat.ML]Concept Tree: High-Level Representation of Variables for More Interpretable Surrogate Decision Trees
Xavier Renard, Nicolas Woloszko, Jonathan Aigrain, Marcin Detyniecki
http://arxiv.org/abs/1906.01297v1
• [stat.ML]Deep ReLU Networks Have Surprisingly Few Activation Patterns
Boris Hanin, David Rolnick
http://arxiv.org/abs/1906.00904v1
• [stat.ML]GANchors: Realistic Image Perturbation Distributions for Anchors Using Generative Models
Kurtis Evan David, Harrison Keane, Jun Min Noh
http://arxiv.org/abs/1906.00297v1
• [stat.ML]Graphon Estimation from Partially Observed Network Data
Soumendu Sundar Mukherjee, Sayak Chakrabarti
http://arxiv.org/abs/1906.00494v1
• [stat.ML]Hybrid Machine Learning Forecasts for the FIFA Women’s World Cup 2019
Andreas Groll, Christophe Ley, Gunther Schauberger, Hans Van Eetvelde, Achim Zeileis
http://arxiv.org/abs/1906.01131v1
• [stat.ML]Learning Perceptually-Aligned Representations via Adversarial Robustness
Logan Engstrom, Andrew Ilyas, Shibani Santurkar, Dimitris Tsipras, Brandon Tran, Aleksander Madry
http://arxiv.org/abs/1906.00945v1
• [stat.ML]MEMe: An Accurate Maximum Entropy Method for Efficient Approximations in Large-Scale Machine Learning
Diego Granziol, Binxin Ru, Stefan Zohren, Xiaowen Doing, Michael Osborne, Stephen Roberts
http://arxiv.org/abs/1906.01101v1
• [stat.ML]MaxGap Bandit: Adaptive Algorithms for Approximate Ranking
Sumeet Katariya, Ardhendu Tripathy, Robert Nowak
http://arxiv.org/abs/1906.00547v1
• [stat.ML]Nonparametric Functional Approximation with Delaunay Triangulation
Yehong Liu, Guosheng Yin
http://arxiv.org/abs/1906.00350v1
• [stat.ML]Robust Gaussian Process Regression for Real-Time High Precision GPS Signal Enhancement
Ming Lin, Xiaomin Song, Qi Qian, Hao Li, Liang Sun, Shenghuo Zhu, Rong Jin
http://arxiv.org/abs/1906.01095v1
• [stat.ML]Robust approximate linear regression without correspondence
Erdem Varol, Amin Nejatbakhsh
http://arxiv.org/abs/1906.00273v1
• [stat.ML]Separable Layers Enable Structured Efficient Linear Substitutions
Gavin Gray, Elliot J. Crowley, Amos Storkey
http://arxiv.org/abs/1906.00859v1
• [stat.ML]Streaming Variational Monte Carlo
Yuan Zhao, Josue Nassar, Ian Jordan, Mónica Bugallo, Il Memming Park
http://arxiv.org/abs/1906.01549v1
• [stat.ML]Symmetry-adapted generation of 3d point sets for the targeted discovery of molecules
Niklas W. A. Gebauer, Michael Gastegger, Kristof T. Schütt
http://arxiv.org/abs/1906.00957v1
• [stat.ML]Tensor Restricted Isometry Property Analysis For a Large Class of Random Measurement Ensembles
Feng Zhang, Wendong Wang, Jingyao Hou, Jianjun Wang, Jianwen Huang
http://arxiv.org/abs/1906.01198v1
• [stat.ML]The Extended Dawid-Skene Model: Fusing Information from Multiple Data Schemas
Michael P. J. Camilleri, Christopher K. I. Williams
http://arxiv.org/abs/1906.01251v1
• [stat.ML]Towards Task and Architecture-Independent Generalization Gap Predictors
Scott Yak, Javier Gonzalvo, Hanna Mazzawi
http://arxiv.org/abs/1906.01550v1
• [stat.ML]Universal Boosting Variational Inference
Trevor Campbell, Xinglong Li
http://arxiv.org/abs/1906.01235v1
• [stat.ML]What do AI algorithms actually learn? - On false structures in deep learning
Laura Thesing, Vegard Antun, Anders C. Hansen
http://arxiv.org/abs/1906.01478v1