今日学术视野(2019.6.12)

cs.AI - 人工智能

cs.CL - 计算与语言 cs.CV - 机器视觉与模式识别 cs.CY - 计算与社会 cs.DC - 分布式、并行与集群计算 cs.GT - 计算机科学与博弈论 cs.HC - 人机接口 cs.IR - 信息检索 cs.IT - 信息论 cs.LG - 自动学习 cs.NE - 神经与进化计算 cs.NI - 网络和互联网体系结构 cs.RO - 机器人学 cs.SD - 声音处理 cs.SE - 软件工程 cs.SI - 社交网络与信息网络 cs.SY - 系统与控制 eess.IV - 图像与视频处理 eess.SP - 信号处理 math.DS - 动力系统 math.OC - 优化与控制 math.ST - 统计理论 physics.soc-ph - 物理学与社会 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

• [cs.AI]”Did You Hear That?” Learning to Play Video Games from Audio Cues
• [cs.AI]A Ride-Matching Strategy For Large Scale Dynamic Ridesharing Services Based on Polar Coordinates
• [cs.AI]Automatic Algorithm Selection In Multi-agent Pathfinding
• [cs.AI]Best-First Width Search for Multi Agent Privacy-preserving Planning
• [cs.AI]Extension of Rough Set Based on Positive Transitive Relation
• [cs.AI]Federated AI lets a team imagine together: Federated Learning of GANs
• [cs.AI]Forward and Backward Knowledge Transfer for Sentiment Classification
• [cs.AI]Inductive Logic Programming via Differentiable Deep Neural Logic Networks
• [cs.AI]Project Thyia: A Forever Gameplayer
• [cs.AI]Strategies to architect AI Safety: Defense to guard AI from Adversaries
• [cs.AI]The Riddle of Togelby
• [cs.CL]A Multi-task Approach for Named Entity Recognition in Social Media Data
• [cs.CL]A Survey on Neural Machine Reading Comprehension
• [cs.CL]A Survey on Neural Network Language Models
• [cs.CL]AGRR-2019: A Corpus for Gapping Resolution in Russian
• [cs.CL]Argument Generation with Retrieval, Planning, and Realization
• [cs.CL]Assessing incrementality in sequence-to-sequence models
• [cs.CL]Automatically Identifying Complaints in Social Media
• [cs.CL]BIGPATENT: A Large-Scale Dataset for Abstractive and Coherent Summarization
• [cs.CL]CAiRE_HKUST at SemEval-2019 Task 3: Hierarchical Attention for Dialogue Emotion Classification
• [cs.CL]Char-RNN for Word Stress Detection in East Slavic Languages
• [cs.CL]Classifying the reported ability in clinical mobility descriptions
• [cs.CL]Clinical Concept Extraction for Document-Level Coding
• [cs.CL]Deep Contextualized Biomedical Abbreviation Expansion
• [cs.CL]Detecting Everyday Scenarios in Narrative Texts
• [cs.CL]Dissecting Content and Context in Argumentative Relation Analysis
• [cs.CL]Domain Adaptive Dialog Generation via Meta Learning
• [cs.CL]Effective Use of Variational Embedding Capacity in Expressive End-to-End Speech Synthesis
• [cs.CL]Embedding Imputation with Grounded Language Information
• [cs.CL]Encouraging Paragraph Embeddings to Remember Sentence Identity Improves Classification
• [cs.CL]GLTR: Statistical Detection and Visualization of Generated Text
• [cs.CL]Gendered Pronoun Resolution using BERT and an extractive question answering formulation
• [cs.CL]Generalized Data Augmentation for Low-Resource Translation
• [cs.CL]Happy Together: Learning and Understanding Appraisal From Natural Language
• [cs.CL]Hierarchical Representation in Neural Language Models: Suppression and Recovery of Expectations
• [cs.CL]Improving Low-Resource Cross-lingual Document Retrieval by Reranking with Deep Bilingual Representations
• [cs.CL]Is Attention Interpretable?
• [cs.CL]LSTM Networks Can Perform Dynamic Counting
• [cs.CL]Learning to Predict Novel Noun-Noun Compounds
• [cs.CL]Learning to combine Grammatical Error Corrections
• [cs.CL]Making Asynchronous Stochastic Gradient Descent Work for Transformers
• [cs.CL]Modeling Noisiness to Recognize Named Entities using Multitask Neural Networks on Social Media
• [cs.CL]Multimodal Logical Inference System for Visual-Textual Entailment
• [cs.CL]Named Entity Recognition on Code-Switched Data: Overview of the CALCS 2018 Shared Task
• [cs.CL]Neural Keyphrase Generation via Reinforcement Learning with Adaptive Rewards
• [cs.CL]Open-Domain Targeted Sentiment Analysis via Span-Based Extraction and Classification
• [cs.CL]Probing for Semantic Classes: Diagnosing the Meaning Content of Word Embeddings
• [cs.CL]Question Answering as Global Reasoning over Semantic Abstractions
• [cs.CL]Seeing Things from a Different Angle: Discovering Diverse Perspectives about Claims
• [cs.CL]Sentence Centrality Revisited for Unsupervised Summarization
• [cs.CL]Sequence-to-Nuggets: Nested Entity Mention Detection via Anchor-Region Networks
• [cs.CL]The University of Helsinki submissions to the WMT19 news translation task
• [cs.CL]This Email Could Save Your Life: Introducing the Task of Email Subject Line Generation
• [cs.CL]Topic-Aware Neural Keyphrase Generation for Social Media Language
• [cs.CL]UBC-NLP at SemEval-2019 Task 6:Ensemble Learning of Offensive Content With Enhanced Training Data
• [cs.CV]2nd Place and 2nd Place Solution to Kaggle Landmark Recognition andRetrieval Competition 2019
• [cs.CV]A Coarse-to-Fine Framework for Learned Color Enhancement with Non-Local Attention
• [cs.CV]A New Ratio Image Based CNN Algorithm For SAR Despeckling
• [cs.CV]An Attention-based Recurrent Convolutional Network for Vehicle Taillight Recognition
• [cs.CV]An Image Clustering Auto-Encoder Based on Predefined Evenly-Distributed Class Centroids and MMD Distance
• [cs.CV]Attending to Discriminative Certainty for Domain Adaptation
• [cs.CV]BAGS: An automatic homework grading system using the pictures taken by smart phones
• [cs.CV]BDNet: Bengali handwritten numeral digit recognition based on densely connected convolutional neural networks
• [cs.CV]Cross-view Semantic Segmentation for Sensing Surroundings
• [cs.CV]Defending against Adversarial Attacks through Resilient Feature Regeneration
• [cs.CV]DiCENet: Dimension-wise Convolutions for Efficient Networks
• [cs.CV]Distilling Object Detectors with Fine-grained Feature Imitation
• [cs.CV]E-LPIPS: Robust Perceptual Image Similarity via Random Transformation Ensembles
• [cs.CV]Embodied View-Contrastive 3D Feature Learning
• [cs.CV]Fast Hierarchical Neural Network for Feature Learning on Point Cloud
• [cs.CV]Fast Spatially-Varying Indoor Lighting Estimation
• [cs.CV]Generation of Multimodal Justification Using Visual Word Constraint Model for Explainable Computer-Aided Diagnosis
• [cs.CV]Global Context for Convolutional Pose Machines
• [cs.CV]Global Semantic Description of Objects based on Prototype Theory
• [cs.CV]HGC: Hierarchical Group Convolution for Highly Efficient Neural Network
• [cs.CV]In Situ Cane Toad Recognition
• [cs.CV]Intriguing properties of adversarial training
• [cs.CV]Large-scale Landmark Retrieval/Recognition under a Noisy and Diverse Dataset
• [cs.CV]Learning Deep Multi-Level Similarity for Thermal Infrared Object Tracking
• [cs.CV]Learning Individual Styles of Conversational Gesture
• [cs.CV]Learning to Segment Skin Lesions from Noisy Annotations
• [cs.CV]Neurogeometry of perception: isotropic and anisotropic aspects
• [cs.CV]Overcoming Limitations of Mixture Density Networks: A Sampling and Fitting Framework for Multimodal Future Prediction
• [cs.CV]Patch Transformer for Multi-tagging Whole Slide Histopathology Images
• [cs.CV]Pattern-Affinitive Propagation across Depth, Surface Normal and Semantic Segmentation
• [cs.CV]Pixel DAG-Recurrent Neural Network for Spectral-Spatial Hyperspectral Image Classification
• [cs.CV]Progressive Cluster Purification for Transductive Few-shot Learning
• [cs.CV]PyramNet: Point Cloud Pyramid Attention Network and Graph Embedding Module for Classification and Segmentation
• [cs.CV]Referring Expression Grounding by Marginalizing Scene Graph Likelihood
• [cs.CV]Scale Steerable Filters for Locally Scale-Invariant Convolutional Neural Networks
• [cs.CV]Soft-ranking Label Encoding for Robust Facial Age Estimation
• [cs.CV]Structure from Motion for Panorama-Style Videos
• [cs.CV]Synthesizing 3D Shapes from Silhouette Image Collections using Multi-projection Generative Adversarial Networks
• [cs.CV]The role of ego vision in view-invariant action recognition
• [cs.CV]Towards Social Artificial Intelligence: Nonverbal Social Signal Prediction in A Triadic Interaction
• [cs.CV]UniDual: A Unified Model for Image and Video Understanding
• [cs.CV]Unsupervised Primitive Discovery for Improved 3D Generative Modeling
• [cs.CV]What and Where to Translate: Local Mask-based Image-to-Image Translation
• [cs.CY]Accuracy Requirements for Early Estimation of Crop Production in Senegal
• [cs.CY]Crypto art: A decentralized view
• [cs.CY]Identifying Data And Information Streams In Cyberspace: A Multi-Dimensional Perspective
• [cs.CY]Mastery Learning-Like Teaching with Achievements
• [cs.DC]Analysis of parallel I/O use on the UK national supercomputing service, ARCHER using Cray LASSi and EPCC SAFE
• [cs.DC]FairLedger: A Fair Blockchain Protocol for Financial Institutions
• [cs.DC]LASSi: Metric based I/O analytics for HPC
• [cs.DC]On the performance of various parallel GMRES implementations on CPU and GPU clusters
• [cs.GT]FaRM: Fair Reward Mechanism for Information Aggregation in Spontaneous Localized Settings (Extended Version)
• [cs.HC]Detecting Clues for Skill Levels and Machine Operation Difficulty from Egocentric Vision
• [cs.IR]Adversarial Mahalanobis Distance-based Attentive Song Recommender for Automatic Playlist Continuation
• [cs.IR]Deep Learning-Based Automatic Downbeat Tracking: A Brief Review
• [cs.IR]Variance Reduction in Gradient Exploration for Online Learning to Rank
• [cs.IT]A Characterization of $q$-binomials and its Application to Coding Theory
• [cs.IT]A Regression Approach to Certain Information Transmission Problems
• [cs.IT]Finding a Generator Matrix of a Multidimensional Cyclic Code
• [cs.IT]Intelligent Reflecting Surface vs. Decode-and-Forward: How Large Surfaces Are Needed to Beat Relaying?
• [cs.IT]Non-Coherent Rate Splitting for the MISO BC with Magnitude CSIT
• [cs.IT]On the Secrecy Performance of NOMA Systems with both External and Internal Eavesdroppers
• [cs.IT]Random Access for Massive Machine-Type Communications
• [cs.IT]Secure Beamforming in MISO NOMA Backscatter Device Aided Symbiotic Radio Networks
• [cs.IT]Sum-Rate Maximization of Uplink Rate Splitting Multiple Access (RSMA) Communication
• [cs.LG]A Closed-Form Learned Pooling for Deep Classification Networks
• [cs.LG]A Survey of Reinforcement Learning Informed by Natural Language
• [cs.LG]A Two-Step Graph Convolutional Decoder for Molecule Generation
• [cs.LG]A general solver to the elliptical mixture model through an approximate Wasserstein manifold
• [cs.LG]A gradual, semi-discrete approach to generative network training via explicit wasserstein minimization
• [cs.LG]Aggregation of pairwise comparisons with reduction of biases
• [cs.LG]Analyzing the Role of Model Uncertainty for Electronic Health Records
• [cs.LG]Attacking Graph Convolutional Networks via Rewiring
• [cs.LG]Attention-based Conditioning Methods for External Knowledge Integration
• [cs.LG]Autonomous Goal Exploration using Learned Goal Spaces for Visuomotor Skill Acquisition in Robots
• [cs.LG]Balanced Off-Policy Evaluation General Action Spaces
• [cs.LG]Bayesian experimental design using regularized determinantal point processes
• [cs.LG]Beyond Adversarial Training: Min-Max Optimization in Adversarial Attack and Defense
• [cs.LG]BlockSwap: Fisher-guided Block Substitution for Network Compression
• [cs.LG]Boosting Soft Actor-Critic: Emphasizing Recent Experience without Forgetting the Past
• [cs.LG]CRCEN: A Generalized Cost-sensitive Neural Network Approach for Imbalanced Classification
• [cs.LG]Commuting Conditional GANs for Robust Multi-Modal Fusion
• [cs.LG]Convolutional Bipartite Attractor Networks
• [cs.LG]Curiosity-Driven Multi-Criteria Hindsight Experience Replay
• [cs.LG]DataLearner: A Data Mining and Knowledge Discovery Tool for Android Smartphones and Tablets
• [cs.LG]Deep Batch Active Learning by Diverse, Uncertain Gradient Lower Bounds
• [cs.LG]Deep Spatio-Temporal Neural Networks for Click-Through Rate Prediction
• [cs.LG]Degrees of Freedom Analysis of Unrolled Neural Networks
• [cs.LG]Distributed Learning with Random Features
• [cs.LG]Dynamic Network Embedding via Incremental Skip-gram with Negative Sampling
• [cs.LG]Efficient Project Gradient Descent for Ensemble Adversarial Attack
• [cs.LG]Errors-in-variables Modeling of Personalized Treatment-Response Trajectories
• [cs.LG]Evaluating the Robustness of Nearest Neighbor Classifiers: A Primal-Dual Perspective
• [cs.LG]Factorization Bandits for Online Influence Maximization
• [cs.LG]Few-Shot Learning with Per-Sample Rich Supervision
• [cs.LG]Four Things Everyone Should Know to Improve Batch Normalization
• [cs.LG]Generative Continual Concept Learning
• [cs.LG]Improved Adversarial Robustness via Logit Regularization Methods
• [cs.LG]Improving Neural Language Modeling via Adversarial Training
• [cs.LG]Intrinsically Efficient, Stable, and Bounded Off-Policy Evaluation for Reinforcement Learning
• [cs.LG]Joint Semantic Domain Alignment and Target Classifier Learning for Unsupervised Domain Adaptation
• [cs.LG]Learning Fair Naive Bayes Classifiers by Discovering and Eliminating Discrimination Patterns
• [cs.LG]Learning Radiative Transfer Models for Climate Change Applications in Imaging Spectroscopy
• [cs.LG]ML-LOO: Detecting Adversarial Examples with Feature Attribution
• [cs.LG]Making Classical Machine Learning Pipelines Differentiable: A Neural Translation Approach
• [cs.LG]Making targeted black-box evasion attacks effective and efficient
• [cs.LG]Maximum Weighted Loss Discrepancy
• [cs.LG]Multi-objects Generation with Amortized Structural Regularization
• [cs.LG]Network Implosion: Effective Model Compression for ResNets via Static Layer Pruning and Retraining
• [cs.LG]Note on the bias and variance of variational inference
• [cs.LG]Novelty Detection via Network Saliency in Visual-based Deep Learning
• [cs.LG]On the Optimality of Sparse Model-Based Planning for Markov Decision Processes
• [cs.LG]On the Vulnerability of Capsule Networks to Adversarial Attacks
• [cs.LG]Online Forecasting of Total-Variation-bounded Sequences
• [cs.LG]Optimal Off-Policy Evaluation for Reinforcement Learning with Marginalized Importance Sampling
• [cs.LG]Partially Linear Additive Gaussian Graphical Models
• [cs.LG]Provably Robust Boosted Decision Stumps and Trees against Adversarial Attacks
• [cs.LG]Quadratic Suffices for Over-parametrization via Matrix Chernoff Bound
• [cs.LG]Quantifying Layerwise Information Discarding of Neural Networks
• [cs.LG]Real or Fake? Learning to Discriminate Machine from Human Generated Text
• [cs.LG]Reducing the variance in online optimization by transporting past gradients
• [cs.LG]Redundancy-Free Computation Graphs for Graph Neural Networks
• [cs.LG]Robust Bi-Tempered Logistic Loss Based on Bregman Divergences
• [cs.LG]RobustTrend: A Huber Loss with a Combined First and Second Order Difference Regularization for Time Series Trend Filtering
• [cs.LG]Robustness Verification of Tree-based Models
• [cs.LG]SAR: Learning Cross-Language API Mappings with Little Knowledge
• [cs.LG]SVRG for Policy Evaluation with Fewer Gradient Evaluations
• [cs.LG]Self-Supervised Exploration via Disagreement
• [cs.LG]Sensitivity of Deep Convolutional Networks to Gabor Noise
• [cs.LG]Simultaneous Classification and Novelty Detection Using Deep Neural Networks
• [cs.LG]Stochastic Mirror Descent on Overparameterized Nonlinear Models: Convergence, Implicit Regularization, and Generalization
• [cs.LG]Stretching the Effectiveness of MLE from Accuracy to Bias for Pairwise Comparisons
• [cs.LG]The Generalization-Stability Tradeoff in Neural Network Pruning
• [cs.LG]Time-Series Anomaly Detection Service at Microsoft
• [cs.LG]Transfer Learning by Modeling a Distribution over Policies
• [cs.LG]Understanding Generalization through Visualizations
• [cs.LG]Understanding overfitting peaks in generalization error: Analytical risk curves for $l_2$ and $l_1$ penalized interpolation
• [cs.LG]Unit Impulse Response as an Explainer of Redundancy in a Deep Convolutional Neural Network
• [cs.LG]Using learned optimizers to make models robust to input noise
• [cs.LG]Watch, Try, Learn: Meta-Learning from Demonstrations and Reward
• [cs.LG]apricot: Submodular selection for data summarization in Python
• [cs.NE]Class-specific Differential Detection in Diffractive Optical Neural Networks Improves Inference Accuracy
• [cs.NE]Data-driven Reconstruction of Nonlinear Dynamics from Sparse Observation
• [cs.NE]Enhanced Optimization with Composite Objectives and Novelty Pulsation
• [cs.NE]Exploration and Exploitation in Symbolic Regression using Quality-Diversity and Evolutionary Strategies Algorithms
• [cs.NE]When and Why Metaheuristics Researchers Can Ignore “No Free Lunch” Theorems
• [cs.NI]Optimal Task Offloading and Resource Allocation for Fog Computing
• [cs.RO]A Hierarchical Network for Diverse Trajectory Proposals
• [cs.RO]Composition of Safety Constraints With Applications to Decentralized Fixed-Wing Collision Avoidance
• [cs.RO]Control of A High Performance Bipedal Robot using Viscoelastic Liquid Cooled Actuators
• [cs.RO]Data Efficient and Safe Learning for Locomotion via Simplified Model
• [cs.RO]DensePhysNet: Learning Dense Physical Object Representations via Multi-step Dynamic Interactions
• [cs.RO]Enabling Robust State Estimation through Measurement Error Covariance Adaptation
• [cs.RO]Movable-Object-Aware Visual SLAM via Weakly Supervised Semantic Segmentation
• [cs.RO]Peristaltic locomotion without digital controllers: Exploiting the origami multi-stability to coordinate robotic motions
• [cs.RO]Rethinking Trajectory Evaluation for SLAM: a Probabilistic, Continuous-Time Approach
• [cs.RO]Simplified Kinematics of Continuum Robot Equilibrium Modulation via Moment Coupling Effects and Model Calibration
• [cs.RO]Trajectory Optimization for Robust Humanoid Locomotion with Sample-Efficient Learning
• [cs.SD]Deep Music Analogy Via Latent Representation Disentanglement
• [cs.SD]rVAD: An Unsupervised Segment-Based Robust Voice Activity Detection Method
• [cs.SE]Recovering Variable Names for Minified Code with Usage Contexts
• [cs.SI]Approximate Identification of the Optimal Epidemic Source in Complex Networks
• [cs.SI]Detection and Prediction of Users Attitude Based on Real-Time and Batch Sentiment Analysis of Facebook Comments
• [cs.SI]Having the Last Word: Understanding How to Sample Discussions Online
• [cs.SI]Learning Individual Treatment Effects from Networked Observational Data
• [cs.SI]News Labeling as Early as Possible: Real or Fake?
• [cs.SI]Transfer Learning for Hate Speech Detection in Social Media
• [cs.SY]Region of Attraction for Power Systems using Gaussian Process and Converse Lyapunov Function — Part I: Theoretical Framework and Off-line Study
• [eess.IV]3DFPN-HS$^2$: 3D Feature Pyramid Network Based High Sensitivity and Specificity Pulmonary Nodule Detection
• [eess.IV]Association of genomic subtypes of lower-grade gliomas with shape features automatically extracted by a deep learning algorithm
• [eess.IV]Automatic Segmentation of Vestibular Schwannoma from T2-Weighted MRI by Deep Spatial Attention with Hardness-Weighted Loss
• [eess.IV]Consensus Neural Network for Medical Imaging Denoising with Only Noisy Training Samples
• [eess.IV]Interpreting Age Effects of Human Fetal Brain from Spontaneous fMRI using Deep 3D Convolutional Neural Networks
• [eess.IV]Multiparametric Deep Learning and Radiomics for Tumor Grading and Treatment Response Assessment of Brain Cancer: Preliminary Results
• [eess.IV]Semi-supervised Complex-valued GAN for Polarimetric SAR Image Classification
• [eess.SP]A Distributed Event-Triggered Control Strategy for DC Microgrids Based on Publish-Subscribe Model Over Industrial Wireless Sensor Networks
• [eess.SP]Learned Conjugate Gradient Descent Network for Massive MIMO Detection
• [eess.SP]Resource Management optimally in Non-Orthogonal Multiple Access Networks for fifth-generation by using game-theoretic
• [eess.SP]Supervised and Semi-Supervised Learning for MIMO Blind Detection with Low-Resolution ADCs
• [math.DS]Physics-Informed Probabilistic Learning of Linear Embeddings of Non-linear Dynamics With Guaranteed Stability
• [math.OC]A Non-Asymptotic Analysis of Network Independence for Distributed Stochastic Gradient Descent
• [math.OC]Stochastic In-Face Frank-Wolfe Methods for Non-Convex Optimization and Sparse Neural Network Training
• [math.ST]A New One Parameter Bimodal Skew Logistic Distribution and its Applications
• [math.ST]Estimation Rates for Sparse Linear Cyclic Causal Models
• [math.ST]Nonparametric Independence Testing for Right-Censored Data using Optimal Transport
• [math.ST]On statistical Calderón problems
• [math.ST]Optimal Convergence for Stochastic Optimization with Multiple Expectation Constraints
• [physics.soc-ph]The key to the weak-ties phenomenon
• [stat.AP]A Comprehensive Hidden Markov Model for Hourly Rainfall Time Series
• [stat.AP]A Naive Bayes Approach for NFL Passing Evaluation using Tracking Data Extracted from Images
• [stat.AP]Big Variates: Visualizing and identifying key variables in a multivariate world
• [stat.AP]Efficient Bayesian estimation for GARCH-type models via Sequential Monte Carlo
• [stat.AP]Incorporating Open Data into Introductory Courses in Statistics
• [stat.AP]Modeling Excess Deaths After a Natural Disaster with Application to Hurricane Maria
• [stat.AP]On Copula-based Collective Risk Models
• [stat.AP]Pitfalls and Protocols in Practice of Manufacturing Data Science
• [stat.CO]A Low Rank Gaussian Process Prediction Model for Very Large Datasets
• [stat.ME]An Approximate Restricted Likelihood Ratio Test for Variance Components in Generalized Linear Mixed Models
• [stat.ME]Graph Independence Testing
• [stat.ME]Multimodal Data Fusion of Non-Gaussian Spatial Fields in Sensor Networks
• [stat.ME]On the Structure of Ordered Latent Trait Models
• [stat.ML]A Variant of Gaussian Process Dynamical Systems
• [stat.ML]A cost-reducing partial labeling estimator in text classification problem
• [stat.ML]Bayesian Automatic Relevance Determination for Utility Function Specification in Discrete Choice Models
• [stat.ML]Bayesian Tensor Filtering: Smooth, Locally-Adaptive Factorization of Functional Matrices
• [stat.ML]Benchmarking Minimax Linkage
• [stat.ML]Confidence intervals for class prevalences under prior probability shift
• [stat.ML]Goodness-of-fit Test for Latent Block Models
• [stat.ML]Guidelines for Responsible and Human-Centered Use of Explainable Machine Learning
• [stat.ML]Inference and Uncertainty Quantification for Noisy Matrix Completion
• [stat.ML]Integrative Factorization of Bidimensionally Linked Matrices
• [stat.ML]Lift Up and Act! Classifier Performance in Resource-Constrained Applications
• [stat.ML]Multiway clustering via tensor block models
• [stat.ML]Neural Spline Flows
• [stat.ML]On the Insufficiency of the Large Margins Theory in Explaining the Performance of Ensemble Methods
• [stat.ML]Optimal Transport Relaxations with Application to Wasserstein GANs
• [stat.ML]Robust conditional GANs under missing or uncertain labels
• [stat.ML]Sampling Humans for Optimizing Preferences in Coloring Artwork
• [stat.ML]Sparse Variational Inference: Bayesian Coresets from Scratch
• [stat.ML]The Broad Optimality of Profile Maximum Likelihood
• [stat.ML]The Impact of Regularization on High-dimensional Logistic Regression
• [stat.ML]The Implicit Bias of AdaGrad on Separable Data
• [stat.ML]The Implicit Metropolis-Hastings Algorithm

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• [cs.AI]“Did You Hear That?” Learning to Play Video Games from Audio Cues
Raluca D. Gaina, Matthew Stephenson
http://arxiv.org/abs/1906.04027v1

• [cs.AI]A Ride-Matching Strategy For Large Scale Dynamic Ridesharing Services Based on Polar Coordinates
Jiyao Li, Vicki H. Allan
http://arxiv.org/abs/1906.03394v1

• [cs.AI]Automatic Algorithm Selection In Multi-agent Pathfinding
Devon Sigurdson, Vadib Bulitko, Sven Koenig, Carlos Hernandez, William Yeoh
http://arxiv.org/abs/1906.03992v1

• [cs.AI]Best-First Width Search for Multi Agent Privacy-preserving Planning
Alfonso E. Gerevini, Nir Lipovetzky, Francesco Percassi, Alessandro Saetti, Ivan Serina
http://arxiv.org/abs/1906.03955v1

• [cs.AI]Extension of Rough Set Based on Positive Transitive Relation
Min Shu, Wei Zhu
http://arxiv.org/abs/1906.03337v1

• [cs.AI]Federated AI lets a team imagine together: Federated Learning of GANs
Rajagopal. A, Nirmala. V
http://arxiv.org/abs/1906.03595v1

• [cs.AI]Forward and Backward Knowledge Transfer for Sentiment Classification
Hao Wang, Bing Liu, Shuai Wang, Nianzu Ma, Yan Yang
http://arxiv.org/abs/1906.03506v1

• [cs.AI]Inductive Logic Programming via Differentiable Deep Neural Logic Networks
Ali Payani, Faramarz Fekri
http://arxiv.org/abs/1906.03523v1

• [cs.AI]Project Thyia: A Forever Gameplayer
Raluca D. Gaina, Simon M. Lucas, Diego Perez-Liebana
http://arxiv.org/abs/1906.04023v1

• [cs.AI]Strategies to architect AI Safety: Defense to guard AI from Adversaries
Rajagopal. A, Nirmala. V
http://arxiv.org/abs/1906.03466v1

• [cs.AI]The Riddle of Togelby
Daniel Ashlock, Christoph Salge
http://arxiv.org/abs/1906.03997v1

• [cs.CL]A Multi-task Approach for Named Entity Recognition in Social Media Data
Gustavo Aguilar, Suraj Maharjan, Adrian Pastor López-Monroy, Thamar Solorio
http://arxiv.org/abs/1906.04135v1

• [cs.CL]A Survey on Neural Machine Reading Comprehension
Boyu Qiu, Xu Chen, Jungang Xu, Yingfei Sun
http://arxiv.org/abs/1906.03824v1

• [cs.CL]A Survey on Neural Network Language Models
Kun Jing, Jungang Xu, Ben He
http://arxiv.org/abs/1906.03591v1

• [cs.CL]AGRR-2019: A Corpus for Gapping Resolution in Russian
Maria Ponomareva, Kira Droganova, Ivan Smurov, Tatiana Shavrina
http://arxiv.org/abs/1906.04099v1

• [cs.CL]Argument Generation with Retrieval, Planning, and Realization
Xinyu Hua, Zhe Hu, Lu Wang
http://arxiv.org/abs/1906.03717v1

• [cs.CL]Assessing incrementality in sequence-to-sequence models
Dennis Ulmer, Dieuwke Hupkes, Elia Bruni
http://arxiv.org/abs/1906.03293v1

• [cs.CL]Automatically Identifying Complaints in Social Media
Daniel Preotiuc-Pietro, Mihaela Gaman, Nikolaos Aletras
http://arxiv.org/abs/1906.03890v1

• [cs.CL]BIGPATENT: A Large-Scale Dataset for Abstractive and Coherent Summarization
Eva Sharma, Chen Li, Lu Wang
http://arxiv.org/abs/1906.03741v1

• [cs.CL]CAiRE_HKUST at SemEval-2019 Task 3: Hierarchical Attention for Dialogue Emotion Classification
Genta Indra Winata, Andrea Madotto, Zhaojiang Lin, Jamin Shin, Yan Xu, Peng Xu, Pascale Fung
http://arxiv.org/abs/1906.04041v1

• [cs.CL]Char-RNN for Word Stress Detection in East Slavic Languages
Ekaterina Chernyak, Maria Ponomareva, Kirill Milintsevich
http://arxiv.org/abs/1906.04082v1

• [cs.CL]Classifying the reported ability in clinical mobility descriptions
Denis Newman-Griffis, Ayah Zirikly, Guy Divita, Bart Desmet
http://arxiv.org/abs/1906.03348v1

• [cs.CL]Clinical Concept Extraction for Document-Level Coding
Sarah Wiegreffe, Edward Choi, Sherry Yan, Jimeng Sun, Jacob Eisenstein
http://arxiv.org/abs/1906.03380v1

• [cs.CL]Deep Contextualized Biomedical Abbreviation Expansion
Qiao Jin, Jinling Liu, Xinghua Lu
http://arxiv.org/abs/1906.03360v1

• [cs.CL]Detecting Everyday Scenarios in Narrative Texts
Lilian D. A. Wanzare, Michael Roth, Manfred Pinkal
http://arxiv.org/abs/1906.04102v1

• [cs.CL]Dissecting Content and Context in Argumentative Relation Analysis
Juri Opitz, Anette Frank
http://arxiv.org/abs/1906.03338v1

• [cs.CL]Domain Adaptive Dialog Generation via Meta Learning
Kun Qian, Zhou Yu
http://arxiv.org/abs/1906.03520v1

• [cs.CL]Effective Use of Variational Embedding Capacity in Expressive End-to-End Speech Synthesis
Eric Battenberg, Soroosh Mariooryad, Daisy Stanton, RJ Skerry-Ryan, Matt Shannon, David Kao, Tom Bagby
http://arxiv.org/abs/1906.03402v1

• [cs.CL]Embedding Imputation with Grounded Language Information
Ziyi Yang, Chenguang Zhu, Vin Sachidananda, Eric Darve
http://arxiv.org/abs/1906.03753v1

• [cs.CL]Encouraging Paragraph Embeddings to Remember Sentence Identity Improves Classification
Tu Vu, Mohit Iyyer
http://arxiv.org/abs/1906.03656v1

• [cs.CL]GLTR: Statistical Detection and Visualization of Generated Text
Sebastian Gehrmann, Hendrik Strobelt, Alexander M. Rush
http://arxiv.org/abs/1906.04043v1

• [cs.CL]Gendered Pronoun Resolution using BERT and an extractive question answering formulation
Rakesh Chada
http://arxiv.org/abs/1906.03695v1

• [cs.CL]Generalized Data Augmentation for Low-Resource Translation
Mengzhou Xia, Xiang Kong, Antonios Anastasopoulos, Graham Neubig
http://arxiv.org/abs/1906.03785v1

• [cs.CL]Happy Together: Learning and Understanding Appraisal From Natural Language
Arun Rajendran, Chiyu Zhang, Muhammad Abdul-Mageed
http://arxiv.org/abs/1906.03677v1

• [cs.CL]Hierarchical Representation in Neural Language Models: Suppression and Recovery of Expectations
Ethan Wilcox, Roger Levy, Richard Futrell
http://arxiv.org/abs/1906.04068v1

• [cs.CL]Improving Low-Resource Cross-lingual Document Retrieval by Reranking with Deep Bilingual Representations
Rui Zhang, Caitlin Westerfield, Sungrok Shim, Garrett Bingham, Alexander Fabbri, Neha Verma, William Hu, Dragomir Radev
http://arxiv.org/abs/1906.03492v1

• [cs.CL]Is Attention Interpretable?
Sofia Serrano, Noah A. Smith
http://arxiv.org/abs/1906.03731v1

• [cs.CL]LSTM Networks Can Perform Dynamic Counting
Mirac Suzgun, Sebastian Gehrmann, Yonatan Belinkov, Stuart M. Shieber
http://arxiv.org/abs/1906.03648v1

• [cs.CL]Learning to Predict Novel Noun-Noun Compounds
Prajit Dhar, Lonneke van der Plas
http://arxiv.org/abs/1906.03634v1

• [cs.CL]Learning to combine Grammatical Error Corrections
Yoav Kantor, Yoav Katz, Leshem Choshen, Edo Cohen-Karlik, Naftali Liberman, Assaf Toledo, Amir Menczel, Noam Slonim
http://arxiv.org/abs/1906.03897v1

• [cs.CL]Making Asynchronous Stochastic Gradient Descent Work for Transformers
Alham Fikri Aji, Kenneth Heafield
http://arxiv.org/abs/1906.03496v1

• [cs.CL]Modeling Noisiness to Recognize Named Entities using Multitask Neural Networks on Social Media
Gustavo Aguilar, A. Pastor López-Monroy, Fabio A. González, Thamar Solorio
http://arxiv.org/abs/1906.04129v1

• [cs.CL]Multimodal Logical Inference System for Visual-Textual Entailment
Riko Suzuki, Hitomi Yanaka, Masashi Yoshikawa, Koji Mineshima, Daisuke Bekki
http://arxiv.org/abs/1906.03952v1

• [cs.CL]Named Entity Recognition on Code-Switched Data: Overview of the CALCS 2018 Shared Task
Gustavo Aguilar, Fahad AlGhamdi, Victor Soto, Mona Diab, Julia Hirschberg, Thamar Solorio
http://arxiv.org/abs/1906.04138v1

• [cs.CL]Neural Keyphrase Generation via Reinforcement Learning with Adaptive Rewards
Hou Pong Chan, Wang Chen, Lu Wang, Irwin King
http://arxiv.org/abs/1906.04106v1

• [cs.CL]Open-Domain Targeted Sentiment Analysis via Span-Based Extraction and Classification
Minghao Hu, Yuxing Peng, Zhen Huang, Dongsheng Li, Yiwei Lv
http://arxiv.org/abs/1906.03820v1

• [cs.CL]Probing for Semantic Classes: Diagnosing the Meaning Content of Word Embeddings
Yadollah Yaghoobzadeh, Katharina Kann, Timothy J. Hazen, Eneko Agirre, Hinrich Schütze
http://arxiv.org/abs/1906.03608v1

• [cs.CL]Question Answering as Global Reasoning over Semantic Abstractions
Daniel Khashabi, Tushar Khot, Ashish Sabharwal, Dan Roth
http://arxiv.org/abs/1906.03672v1

• [cs.CL]Seeing Things from a Different Angle: Discovering Diverse Perspectives about Claims
Sihao Chen, Daniel Khashabi, Wenpeng Yin, Chris Callison-Burch, Dan Roth
http://arxiv.org/abs/1906.03538v1

• [cs.CL]Sentence Centrality Revisited for Unsupervised Summarization
Hao Zheng, Mirella Lapata
http://arxiv.org/abs/1906.03508v1

• [cs.CL]Sequence-to-Nuggets: Nested Entity Mention Detection via Anchor-Region Networks
Hongyu Lin, Yaojie Lu, Xianpei Han, Le Sun
http://arxiv.org/abs/1906.03783v1

• [cs.CL]The University of Helsinki submissions to the WMT19 news translation task
Aarne Talman, Umut Sulubacak, Raúl Vázquez, Yves Scherrer, Sami Virpioja, Alessandro Raganato, Arvi Hurskainen, Jörg Tiedemann
http://arxiv.org/abs/1906.04040v1

• [cs.CL]This Email Could Save Your Life: Introducing the Task of Email Subject Line Generation
Rui Zhang, Joel Tetreault
http://arxiv.org/abs/1906.03497v1

• [cs.CL]Topic-Aware Neural Keyphrase Generation for Social Media Language
Yue Wang, Jing Li, Hou Pong Chan, Irwin King, Michael R. Lyu, Shuming Shi
http://arxiv.org/abs/1906.03889v1

• [cs.CL]UBC-NLP at SemEval-2019 Task 6:Ensemble Learning of Offensive Content With Enhanced Training Data
Arun Rajendran, Chiyu Zhang, Muhammad Abdul-Mageed
http://arxiv.org/abs/1906.03692v1

• [cs.CV]2nd Place and 2nd Place Solution to Kaggle Landmark Recognition andRetrieval Competition 2019
Kaibing Chen, Cheng Cui, Yuning Du, Xianglong Meng, Hui Ren
http://arxiv.org/abs/1906.03990v1

• [cs.CV]A Coarse-to-Fine Framework for Learned Color Enhancement with Non-Local Attention
Chaowei Shan, Zhizheng Zhang, Zhibo Chen
http://arxiv.org/abs/1906.03404v1

• [cs.CV]A New Ratio Image Based CNN Algorithm For SAR Despeckling
Sergio Vitale, Giampaolo Ferraioli, Vito Pascazio
http://arxiv.org/abs/1906.04111v1

• [cs.CV]An Attention-based Recurrent Convolutional Network for Vehicle Taillight Recognition
Kuan-Hui Lee, Takaaki Tagawa, Jia-En M. Pan, Adrien Gaidon, Bertrand Douillard
http://arxiv.org/abs/1906.03683v1

• [cs.CV]An Image Clustering Auto-Encoder Based on Predefined Evenly-Distributed Class Centroids and MMD Distance
Qiuyu Zhu, Zhengyong Wang
http://arxiv.org/abs/1906.03905v1

• [cs.CV]Attending to Discriminative Certainty for Domain Adaptation
Vinod Kumar Kurmi, Shanu Kumar, Vinay P Namboodiri
http://arxiv.org/abs/1906.03502v1

• [cs.CV]BAGS: An automatic homework grading system using the pictures taken by smart phones
Xiaoshuo Li, Tiezhu Yue, Xuanping Huang, Zhe Yang, Gang Xu
http://arxiv.org/abs/1906.03767v1

• [cs.CV]BDNet: Bengali handwritten numeral digit recognition based on densely connected convolutional neural networks
A. Sufian, Anirudha Ghosh, Avijit Naskar, Farhana Sultana
http://arxiv.org/abs/1906.03786v1

• [cs.CV]Cross-view Semantic Segmentation for Sensing Surroundings
Bowen Pan, Jiankai Sun, Alex Andonian, Aude Oliva, Bolei Zhou
http://arxiv.org/abs/1906.03560v1

• [cs.CV]Defending against Adversarial Attacks through Resilient Feature Regeneration
Tejas Borkar, Felix Heide, Lina Karam
http://arxiv.org/abs/1906.03444v1

• [cs.CV]DiCENet: Dimension-wise Convolutions for Efficient Networks
Sachin Mehta, Hannaneh Hajishirzi, Mohammad Rastegari
http://arxiv.org/abs/1906.03516v1

• [cs.CV]Distilling Object Detectors with Fine-grained Feature Imitation
Tao Wang, Li Yuan, Xiaopeng Zhang, Jiashi Feng
http://arxiv.org/abs/1906.03609v1

• [cs.CV]E-LPIPS: Robust Perceptual Image Similarity via Random Transformation Ensembles
Markus Kettunen, Erik Härkönen, Jaakko Lehtinen
http://arxiv.org/abs/1906.03973v1

• [cs.CV]Embodied View-Contrastive 3D Feature Learning
Adam W. Harley, Fangyu Li, Shrinidhi K. Lakshmikanth, Xian Zhou, Hsiao-Yu Fish Tung, Katerina Fragkiadaki
http://arxiv.org/abs/1906.03764v1

• [cs.CV]Fast Hierarchical Neural Network for Feature Learning on Point Cloud
Can Chen, Luca Zanotti Fragonara, Antonios Tsourdos
http://arxiv.org/abs/1906.04117v1

• [cs.CV]Fast Spatially-Varying Indoor Lighting Estimation
Mathieu Garon, Kalyan Sunkavalli, Sunil Hadap, Nathan Carr, Jean-François Lalonde
http://arxiv.org/abs/1906.03799v1

• [cs.CV]Generation of Multimodal Justification Using Visual Word Constraint Model for Explainable Computer-Aided Diagnosis
Hyebin Lee, Seong Tae Kim, Yong Man Ro
http://arxiv.org/abs/1906.03922v1

• [cs.CV]Global Context for Convolutional Pose Machines
Daniil Osokin
http://arxiv.org/abs/1906.04104v1

• [cs.CV]Global Semantic Description of Objects based on Prototype Theory
Omar Vidal Pino, Erickson Rangel Nascimento, Mario Fernando Montenegro Campos
http://arxiv.org/abs/1906.03365v1

• [cs.CV]HGC: Hierarchical Group Convolution for Highly Efficient Neural Network
Xukai Xie, Yuan Zhou, Sun-Yuan Kung
http://arxiv.org/abs/1906.03657v1

• [cs.CV]In Situ Cane Toad Recognition
Dmitry A. Konovalov, Simindokht Jahangard, Lin Schwarzkopf
http://arxiv.org/abs/1906.03547v1

• [cs.CV]Intriguing properties of adversarial training
Cihang Xie, Alan Yuille
http://arxiv.org/abs/1906.03787v1

• [cs.CV]Large-scale Landmark Retrieval/Recognition under a Noisy and Diverse Dataset
Kohei Ozaki, Shuhei Yokoo
http://arxiv.org/abs/1906.04087v1

• [cs.CV]Learning Deep Multi-Level Similarity for Thermal Infrared Object Tracking
Qiao Liu, Xin Li, Zhenyu He, Nana Fan, Di Yuan, Hongpeng Wang
http://arxiv.org/abs/1906.03568v1

• [cs.CV]Learning Individual Styles of Conversational Gesture
Shiry Ginosar, Amir Bar, Gefen Kohavi, Caroline Chan, Andrew Owens, Jitendra Malik
http://arxiv.org/abs/1906.04160v1

• [cs.CV]Learning to Segment Skin Lesions from Noisy Annotations
Zahra Mirikharaji, Yiqi Yan, Ghassan Hamarneh
http://arxiv.org/abs/1906.03815v1

• [cs.CV]Neurogeometry of perception: isotropic and anisotropic aspects
Giovanna Citti, Alessandro Sarti
http://arxiv.org/abs/1906.03495v1

• [cs.CV]Overcoming Limitations of Mixture Density Networks: A Sampling and Fitting Framework for Multimodal Future Prediction
Osama Makansi, Eddy Ilg, Özgün Cicek, Thomas Brox
http://arxiv.org/abs/1906.03631v1

• [cs.CV]Patch Transformer for Multi-tagging Whole Slide Histopathology Images
Weijian Li, Viet-Duy Nguyen, Haofu Liao, Matt Wilder, Ke Cheng, Jiebo Luo
http://arxiv.org/abs/1906.04151v1

• [cs.CV]Pattern-Affinitive Propagation across Depth, Surface Normal and Semantic Segmentation
Zhenyu Zhang, Zhen Cui, Chunyan Xu, Yan Yan, Nicu Sebe, Jian Yang
http://arxiv.org/abs/1906.03525v1

• [cs.CV]Pixel DAG-Recurrent Neural Network for Spectral-Spatial Hyperspectral Image Classification
Xiufang Li, Qigong Sun, Lingling Li, Zhongle Ren, Fang Liu, Licheng Jiao
http://arxiv.org/abs/1906.03607v1

• [cs.CV]Progressive Cluster Purification for Transductive Few-shot Learning
Chenyang Si, Wentao Chen, Wei Wang, Liang Wang, Tieniu Tan
http://arxiv.org/abs/1906.03847v1

• [cs.CV]PyramNet: Point Cloud Pyramid Attention Network and Graph Embedding Module for Classification and Segmentation
Kang Zhiheng, Li Ning
http://arxiv.org/abs/1906.03299v1

• [cs.CV]Referring Expression Grounding by Marginalizing Scene Graph Likelihood
Daqing Liu, Hanwang Zhang, Zheng-Jun Zha, Fanglin Wang
http://arxiv.org/abs/1906.03561v1

• [cs.CV]Scale Steerable Filters for Locally Scale-Invariant Convolutional Neural Networks
Rohan Ghosh, Anupam K. Gupta
http://arxiv.org/abs/1906.03861v1

• [cs.CV]Soft-ranking Label Encoding for Robust Facial Age Estimation
Xusheng Zeng, Changxing Ding, Yonggang Wen, Dacheng Tao
http://arxiv.org/abs/1906.03625v1

• [cs.CV]Structure from Motion for Panorama-Style Videos
Chris Sweeney, Aleksander Holynski, Brian Curless, Steve M Seitz
http://arxiv.org/abs/1906.03539v1

• [cs.CV]Synthesizing 3D Shapes from Silhouette Image Collections using Multi-projection Generative Adversarial Networks
Xiao Li, Yue Dong, Pieter Peers, Xin Tong
http://arxiv.org/abs/1906.03841v1

• [cs.CV]The role of ego vision in view-invariant action recognition
Gaurvi Goyal, Nicoletta Noceti, Francesca Odone, Alessandra Sciutti
http://arxiv.org/abs/1906.03918v1

• [cs.CV]Towards Social Artificial Intelligence: Nonverbal Social Signal Prediction in A Triadic Interaction
Hanbyul Joo, Tomas Simon, Mina Cikara, Yaser Sheikh
http://arxiv.org/abs/1906.04158v1

• [cs.CV]UniDual: A Unified Model for Image and Video Understanding
Yufei Wang, Du Tran, Lorenzo Torresani
http://arxiv.org/abs/1906.03857v1

• [cs.CV]Unsupervised Primitive Discovery for Improved 3D Generative Modeling
Salman H. Khan, Yulan Guo, Munawar Hayat, Nick Barnes
http://arxiv.org/abs/1906.03650v1

• [cs.CV]What and Where to Translate: Local Mask-based Image-to-Image Translation
Wonwoong Cho, Seunghwan Choi, Junwoo Park, David Keetae Park, Tao Qin, Jaegul Choo
http://arxiv.org/abs/1906.03598v1

• [cs.CY]Accuracy Requirements for Early Estimation of Crop Production in Senegal
Damien Christophe Jacques, Pierre Defourny
http://arxiv.org/abs/1906.03627v1

• [cs.CY]Crypto art: A decentralized view
Massimo Franceschet, Giovanni Colavizza, Tai Smith, Blake Finucane, Martin Lukas Ostachowski, Sergio Scalet, Jonathan Perkins, James Morgan, Sebastian Hernandez
http://arxiv.org/abs/1906.03263v1

• [cs.CY]Identifying Data And Information Streams In Cyberspace: A Multi-Dimensional Perspective
Ruth Ikwu
http://arxiv.org/abs/1906.03757v1

• [cs.CY]Mastery Learning-Like Teaching with Achievements
Tobias Wrigstad, Elias Castegren
http://arxiv.org/abs/1906.03510v1

• [cs.DC]Analysis of parallel I/O use on the UK national supercomputing service, ARCHER using Cray LASSi and EPCC SAFE
Andrew Turner, Dominic Sloan-Murphy, Karthee Sivalingam, Harvey Richardson, Julian Kunkel
http://arxiv.org/abs/1906.03891v1

• [cs.DC]FairLedger: A Fair Blockchain Protocol for Financial Institutions
Kfir Lev-Ari, Alexander Spiegelman, Idit Keidar, Dahlia Malkhi
http://arxiv.org/abs/1906.03819v1

• [cs.DC]LASSi: Metric based I/O analytics for HPC
Karthee Sivalingam, Harvey Richardson, Adrian Tate, Martin Lafferty
http://arxiv.org/abs/1906.03884v1

• [cs.DC]On the performance of various parallel GMRES implementations on CPU and GPU clusters
E. I. Ioannidis, N. Cheimarios, A. N. Spyropoulos, A. G. Boudouvis
http://arxiv.org/abs/1906.04051v1

• [cs.GT]FaRM: Fair Reward Mechanism for Information Aggregation in Spontaneous Localized Settings (Extended Version)
Moin Hussain Moti, Dimitris Chatzopoulos, Pan Hui, Sujit Gujar
http://arxiv.org/abs/1906.03963v1

• [cs.HC]Detecting Clues for Skill Levels and Machine Operation Difficulty from Egocentric Vision
Longfei Chen, Yuichi Nakamura, Kazuaki Kondo
http://arxiv.org/abs/1906.04002v1

• [cs.IR]Adversarial Mahalanobis Distance-based Attentive Song Recommender for Automatic Playlist Continuation
Thanh Tran, Renee Sweeney, Kyumin Lee
http://arxiv.org/abs/1906.03450v1

• [cs.IR]Deep Learning-Based Automatic Downbeat Tracking: A Brief Review
Bijue Jia, Jiancheng Lv, Dayiheng Liu
http://arxiv.org/abs/1906.03870v1

• [cs.IR]Variance Reduction in Gradient Exploration for Online Learning to Rank
Huazheng Wang, Sonwoo Kim, Eric McCord-Snook, Qingyun Wu, Hongning Wang
http://arxiv.org/abs/1906.03766v1

• [cs.IT]A Characterization of $q$-binomials and its Application to Coding Theory
Manabu Hagiwara, Justin Kong
http://arxiv.org/abs/1906.03385v1

• [cs.IT]A Regression Approach to Certain Information Transmission Problems
Wenyi Zhang, Yizhu Wang, Cong Shen, Ning Liang
http://arxiv.org/abs/1906.03777v1

• [cs.IT]Finding a Generator Matrix of a Multidimensional Cyclic Code
R. Andriamifidisoa, R. M. Lalasoa, T. J. Rabeherimanana
http://arxiv.org/abs/1906.03491v1

• [cs.IT]Intelligent Reflecting Surface vs. Decode-and-Forward: How Large Surfaces Are Needed to Beat Relaying?
Emil Björnson, Özgecan Özdogan, Erik G. Larsson
http://arxiv.org/abs/1906.03949v1

• [cs.IT]Non-Coherent Rate Splitting for the MISO BC with Magnitude CSIT
Carlos Mosquera, Tomás Ramírez, Màrius Caus, Nele Noels, Adriano Pastore
http://arxiv.org/abs/1906.03863v1

• [cs.IT]On the Secrecy Performance of NOMA Systems with both External and Internal Eavesdroppers
Milad Abolpour, Mahtab Mirmohseni, Mohammad Reza Aref
http://arxiv.org/abs/1906.03929v1

• [cs.IT]Random Access for Massive Machine-Type Communications
Zhuo Sun
http://arxiv.org/abs/1906.03817v1

• [cs.IT]Secure Beamforming in MISO NOMA Backscatter Device Aided Symbiotic Radio Networks
Yiqing Li, Miao Jiang, Qi Zhang, Jiayin Qin
http://arxiv.org/abs/1906.03410v1

• [cs.IT]Sum-Rate Maximization of Uplink Rate Splitting Multiple Access (RSMA) Communication
Zhaohui Yang, Mingzhe Chen, Walid Saad, Wei Xu, Mohammad Shikh-Bahaei
http://arxiv.org/abs/1906.04092v1

• [cs.LG]A Closed-Form Learned Pooling for Deep Classification Networks
Vighnesh Birodkar, Hossein Mobahi, Dilip Krishnan, Samy Bengio
http://arxiv.org/abs/1906.03808v1

• [cs.LG]A Survey of Reinforcement Learning Informed by Natural Language
Jelena Luketina, Nantas Nardelli, Gregory Farquhar, Jakob Foerster, Jacob Andreas, Edward Grefenstette, Shimon Whiteson, Tim Rocktäschel
http://arxiv.org/abs/1906.03926v1

• [cs.LG]A Two-Step Graph Convolutional Decoder for Molecule Generation
Xavier Bresson, Thomas Laurent
http://arxiv.org/abs/1906.03412v1

• [cs.LG]A general solver to the elliptical mixture model through an approximate Wasserstein manifold
Shengxi Li, Zeyang Yu, Min Xiang, Danilo Mandic
http://arxiv.org/abs/1906.03700v1

• [cs.LG]A gradual, semi-discrete approach to generative network training via explicit wasserstein minimization
Yucheng Chen, Matus Telgarsky, Chao Zhang, Bolton Bailey, Daniel Hsu, Jian Peng
http://arxiv.org/abs/1906.03471v1

• [cs.LG]Aggregation of pairwise comparisons with reduction of biases
Nadezhda Bugakova, Valentina Fedorova, Gleb Gusev, Alexey Drutsa
http://arxiv.org/abs/1906.03711v1

• [cs.LG]Analyzing the Role of Model Uncertainty for Electronic Health Records
Michael W. Dusenberry, Dustin Tran, Edward Choi, Jonas Kemp, Jeremy Nixon, Ghassen Jerfel, Katherine Heller, Andrew M. Dai
http://arxiv.org/abs/1906.03842v1

• [cs.LG]Attacking Graph Convolutional Networks via Rewiring
Yao Ma, Suhang Wang, Lingfei Wu, Jiliang Tang
http://arxiv.org/abs/1906.03750v1

• [cs.LG]Attention-based Conditioning Methods for External Knowledge Integration
Katerina Margatina, Christos Baziotis, Alexandros Potamianos
http://arxiv.org/abs/1906.03674v1

• [cs.LG]Autonomous Goal Exploration using Learned Goal Spaces for Visuomotor Skill Acquisition in Robots
Adrien Laversanne-Finot, Alexandre Péré, Pierre-Yves Oudeyer
http://arxiv.org/abs/1906.03967v1

• [cs.LG]Balanced Off-Policy Evaluation General Action Spaces
Arjun Sondhi, David Arbour, Drew Dimmery
http://arxiv.org/abs/1906.03694v1

• [cs.LG]Bayesian experimental design using regularized determinantal point processes
Michał Dereziński, Feynman Liang, Michael W. Mahoney
http://arxiv.org/abs/1906.04133v1

• [cs.LG]Beyond Adversarial Training: Min-Max Optimization in Adversarial Attack and Defense
Jingkang Wang, Tianyun Zhang, Sijia Liu, Pin-Yu Chen, Jiacen Xu, Makan Fardad, Bo Li
http://arxiv.org/abs/1906.03563v1

• [cs.LG]BlockSwap: Fisher-guided Block Substitution for Network Compression
Jack Turner, Elliot J. Crowley, Gavin Gray, Amos Storkey, Michael O’Boyle
http://arxiv.org/abs/1906.04113v1

• [cs.LG]Boosting Soft Actor-Critic: Emphasizing Recent Experience without Forgetting the Past
Che Wang, Keith Ross
http://arxiv.org/abs/1906.04009v1

• [cs.LG]CRCEN: A Generalized Cost-sensitive Neural Network Approach for Imbalanced Classification
Xiangrui Li, Dongxiao Zhu
http://arxiv.org/abs/1906.04026v1

• [cs.LG]Commuting Conditional GANs for Robust Multi-Modal Fusion
Siddharth Roheda, Hamid Krim, Benjamin S. Riggan
http://arxiv.org/abs/1906.04115v1

• [cs.LG]Convolutional Bipartite Attractor Networks
Michael Iuzzolino, Yoram Singer, Michael C. Mozer
http://arxiv.org/abs/1906.03504v1

• [cs.LG]Curiosity-Driven Multi-Criteria Hindsight Experience Replay
John B. Lanier, Stephen McAleer, Pierre Baldi
http://arxiv.org/abs/1906.03710v1

• [cs.LG]DataLearner: A Data Mining and Knowledge Discovery Tool for Android Smartphones and Tablets
Darren Yates, Md Zahidul Islam, Junbin Gao
http://arxiv.org/abs/1906.03773v1

• [cs.LG]Deep Batch Active Learning by Diverse, Uncertain Gradient Lower Bounds
Jordan T. Ash, Chicheng Zhang, Akshay Krishnamurthy, John Langford, Alekh Agarwal
http://arxiv.org/abs/1906.03671v1

• [cs.LG]Deep Spatio-Temporal Neural Networks for Click-Through Rate Prediction
Wentao Ouyang, Xiuwu Zhang, Li Li, Heng Zou, Xin Xing, Zhaojie Liu, Yanlong Du
http://arxiv.org/abs/1906.03776v1

• [cs.LG]Degrees of Freedom Analysis of Unrolled Neural Networks
Morteza Mardani, Qingyun Sun, Vardan Papyan, Shreyas Vasanawala, John Pauly, David Donoho
http://arxiv.org/abs/1906.03742v1

• [cs.LG]Distributed Learning with Random Features
Jian Li, Yong Liu, Weiping Wang
http://arxiv.org/abs/1906.03155v2

• [cs.LG]Dynamic Network Embedding via Incremental Skip-gram with Negative Sampling
Hao Peng, Jianxin Li, Hao Yan, Qiran Gong, Senzhang Wang, Lin Liu, Lihong Wang, Xiang Ren
http://arxiv.org/abs/1906.03586v1

• [cs.LG]Efficient Project Gradient Descent for Ensemble Adversarial Attack
Fanyou Wu, Rado Gazo, Eva Haviarova, Bedrich Benes
http://arxiv.org/abs/1906.03333v1

• [cs.LG]Errors-in-variables Modeling of Personalized Treatment-Response Trajectories
Guangyi Zhang, Reza Ashrafi, Anne Juuti, Kirsi Pietiläinen, Pekka Marttinen
http://arxiv.org/abs/1906.03989v1

• [cs.LG]Evaluating the Robustness of Nearest Neighbor Classifiers: A Primal-Dual Perspective
Lu Wang, Xuanqing Liu, Jinfeng Yi, Zhi-Hua Zhou, Cho-Jui Hsieh
http://arxiv.org/abs/1906.03972v1

• [cs.LG]Factorization Bandits for Online Influence Maximization
Qingyun Wu, Zhige Li, Huazheng Wang, Wei Chen, Hongning Wang
http://arxiv.org/abs/1906.03737v1

• [cs.LG]Few-Shot Learning with Per-Sample Rich Supervision
Roman Visotsky, Yuval Atzmon, Gal Chechik
http://arxiv.org/abs/1906.03859v1

• [cs.LG]Four Things Everyone Should Know to Improve Batch Normalization
Cecilia Summers, Michael J. Dinneen
http://arxiv.org/abs/1906.03548v1

• [cs.LG]Generative Continual Concept Learning
Mohammad Rostami, Soheil Kolouri, James McClelland, Praveen Pilly
http://arxiv.org/abs/1906.03744v1

• [cs.LG]Improved Adversarial Robustness via Logit Regularization Methods
Cecilia Summers, Michael J. Dinneen
http://arxiv.org/abs/1906.03749v1

• [cs.LG]Improving Neural Language Modeling via Adversarial Training
Dilin Wang, Chengyue Gong, Qiang Liu
http://arxiv.org/abs/1906.03805v1

• [cs.LG]Intrinsically Efficient, Stable, and Bounded Off-Policy Evaluation for Reinforcement Learning
Nathan Kallus, Masatoshi Uehara
http://arxiv.org/abs/1906.03735v1

• [cs.LG]Joint Semantic Domain Alignment and Target Classifier Learning for Unsupervised Domain Adaptation
Dong-Dong Chen, Yisen Wang, Jinfeng Yi, Zaiyi Chen, Zhi-Hua Zhou
http://arxiv.org/abs/1906.04053v1

• [cs.LG]Learning Fair Naive Bayes Classifiers by Discovering and Eliminating Discrimination Patterns
YooJung Choi, Golnoosh Farnadi, Behrouz Babaki, Guy Van den Broeck
http://arxiv.org/abs/1906.03843v1

• [cs.LG]Learning Radiative Transfer Models for Climate Change Applications in Imaging Spectroscopy
Shubhankar Deshpande, Brian D. Bue, David R. Thompson, Vijay Natraj, Mario Parente
http://arxiv.org/abs/1906.03479v1

• [cs.LG]ML-LOO: Detecting Adversarial Examples with Feature Attribution
Puyudi Yang, Jianbo Chen, Cho-Jui Hsieh, Jane-Ling Wang, Michael I. Jordan
http://arxiv.org/abs/1906.03499v1

• [cs.LG]Making Classical Machine Learning Pipelines Differentiable: A Neural Translation Approach
Gyeong-In Yu, Saeed Amizadeh, Artidoro Pagnoni, Byung-Gon Chun, Markus Weimer, Matteo Interlandi
http://arxiv.org/abs/1906.03822v1

• [cs.LG]Making targeted black-box evasion attacks effective and efficient
Mika Juuti, Buse Gul Atli, N. Asokan
http://arxiv.org/abs/1906.03397v1

• [cs.LG]Maximum Weighted Loss Discrepancy
Fereshte Khani, Aditi Raghunathan, Percy Liang
http://arxiv.org/abs/1906.03518v1

• [cs.LG]Multi-objects Generation with Amortized Structural Regularization
Kun Xu, Chongxuan Li, Jun Zhu, Bo Zhang
http://arxiv.org/abs/1906.03923v1

• [cs.LG]Network Implosion: Effective Model Compression for ResNets via Static Layer Pruning and Retraining
Yasutoshi Ida, Yasuhiro Fujiwara
http://arxiv.org/abs/1906.03826v1

• [cs.LG]Note on the bias and variance of variational inference
Chin-Wei Huang, Aaron Courville
http://arxiv.org/abs/1906.03708v1

• [cs.LG]Novelty Detection via Network Saliency in Visual-based Deep Learning
Valerie Chen, Man-Ki Yoon, Zhong Shao
http://arxiv.org/abs/1906.03685v1

• [cs.LG]On the Optimality of Sparse Model-Based Planning for Markov Decision Processes
Alekh Agarwal, Sham Kakade, Lin F. Yang
http://arxiv.org/abs/1906.03804v1

• [cs.LG]On the Vulnerability of Capsule Networks to Adversarial Attacks
Felix Michels, Tobias Uelwer, Eric Upschulte, Stefan Harmeling
http://arxiv.org/abs/1906.03612v1

• [cs.LG]Online Forecasting of Total-Variation-bounded Sequences
Dheeraj Baby, Yu-Xiang Wang
http://arxiv.org/abs/1906.03364v1

• [cs.LG]Optimal Off-Policy Evaluation for Reinforcement Learning with Marginalized Importance Sampling
Tengyang Xie, Yifei Ma, Yu-Xiang Wang
http://arxiv.org/abs/1906.03393v1

• [cs.LG]Partially Linear Additive Gaussian Graphical Models
Sinong Geng, Minhao Yan, Mladen Kolar, Oluwasanmi Koyejo
http://arxiv.org/abs/1906.03362v1

• [cs.LG]Provably Robust Boosted Decision Stumps and Trees against Adversarial Attacks
Maksym Andriushchenko, Matthias Hein
http://arxiv.org/abs/1906.03526v1

• [cs.LG]Quadratic Suffices for Over-parametrization via Matrix Chernoff Bound
Zhao Song, Xin Yang
http://arxiv.org/abs/1906.03593v1

• [cs.LG]Quantifying Layerwise Information Discarding of Neural Networks
Haotian Ma, Yinqing Zhang, Fan Zhou, Quanshi Zhang
http://arxiv.org/abs/1906.04109v1

• [cs.LG]Real or Fake? Learning to Discriminate Machine from Human Generated Text
Anton Bakhtin, Sam Gross, Myle Ott, Yuntian Deng, Marc’Aurelio Ranzato, Arthur Szlam
http://arxiv.org/abs/1906.03351v1

• [cs.LG]Reducing the variance in online optimization by transporting past gradients
Sébastien M. R. Arnold, Pierre-Antoine Manzagol, Reza Babanezhad, Ioannis Mitliagkas, Nicolas Le Roux
http://arxiv.org/abs/1906.03532v1

• [cs.LG]Redundancy-Free Computation Graphs for Graph Neural Networks
Zhihao Jia, Sina Lin, Rex Ying, Jiaxuan You, Jure Leskovec, Alex Aiken
http://arxiv.org/abs/1906.03707v1

• [cs.LG]Robust Bi-Tempered Logistic Loss Based on Bregman Divergences
Ehsan Amid, Manfred K. Warmuth, Rohan Anil, Tomer Koren
http://arxiv.org/abs/1906.03361v1

• [cs.LG]RobustTrend: A Huber Loss with a Combined First and Second Order Difference Regularization for Time Series Trend Filtering
Qingsong Wen, Jingkun Gao, Xiaomin Song, Liang Sun, Jian Tan
http://arxiv.org/abs/1906.03751v1

• [cs.LG]Robustness Verification of Tree-based Models
Hongge Chen, Huan Zhang, Si Si, Yang Li, Duane Boing, Cho-Jui Hsieh
http://arxiv.org/abs/1906.03849v1

• [cs.LG]SAR: Learning Cross-Language API Mappings with Little Knowledge
Nghi D. Q. Bui, Yijun Yu, Lingxiao Jiang
http://arxiv.org/abs/1906.03835v1

• [cs.LG]SVRG for Policy Evaluation with Fewer Gradient Evaluations
Zilun Peng, Ahmed Touati, Pascal Vincent, Doina Precup
http://arxiv.org/abs/1906.03704v1

• [cs.LG]Self-Supervised Exploration via Disagreement
Deepak Pathak, Dhiraj Gandhi, Abhinav Gupta
http://arxiv.org/abs/1906.04161v1

• [cs.LG]Sensitivity of Deep Convolutional Networks to Gabor Noise
Kenneth T. Co, Luis Muñoz-González, Emil C. Lupu
http://arxiv.org/abs/1906.03455v1

• [cs.LG]Simultaneous Classification and Novelty Detection Using Deep Neural Networks
Aristotelis-Angelos Papadopoulos, Mohammad Reza Rajati
http://arxiv.org/abs/1906.03509v1

• [cs.LG]Stochastic Mirror Descent on Overparameterized Nonlinear Models: Convergence, Implicit Regularization, and Generalization
Navid Azizan, Sahin Lale, Babak Hassibi
http://arxiv.org/abs/1906.03830v1

• [cs.LG]Stretching the Effectiveness of MLE from Accuracy to Bias for Pairwise Comparisons
Jingyan Wang, Nihar B. Shah, R. Ravi
http://arxiv.org/abs/1906.04066v1

• [cs.LG]The Generalization-Stability Tradeoff in Neural Network Pruning
Brian R. Bartoldson, Ari S. Morcos, Adrian Barbu, Gordon Erlebacher
http://arxiv.org/abs/1906.03728v1

• [cs.LG]Time-Series Anomaly Detection Service at Microsoft
Hansheng Ren, Bixiong Xu, Yujing Wang, Chao Yi, Congrui Huang, Xiaoyu Kou, Tony Xing, Mao Yang, Jie Tong, Qi Zhang
http://arxiv.org/abs/1906.03821v1

• [cs.LG]Transfer Learning by Modeling a Distribution over Policies
Disha Shrivastava, Eeshan Gunesh Dhekane, Riashat Islam
http://arxiv.org/abs/1906.03574v1

• [cs.LG]Understanding Generalization through Visualizations
W. Ronny Huang, Zeyad Emam, Micah Goldblum, Liam Fowl, Justin K. Terry, Furong Huang, Tom Goldstein
http://arxiv.org/abs/1906.03291v1

• [cs.LG]Understanding overfitting peaks in generalization error: Analytical risk curves for $l_2$ and $l_1$ penalized interpolation
Partha P Mitra
http://arxiv.org/abs/1906.03667v1

• [cs.LG]Unit Impulse Response as an Explainer of Redundancy in a Deep Convolutional Neural Network
Rachana Sathish, Debdoot Sheet
http://arxiv.org/abs/1906.03986v1

• [cs.LG]Using learned optimizers to make models robust to input noise
Luke Metz, Niru Maheswaranathan, Jonathon Shlens, Jascha Sohl-Dickstein, Ekin D. Cubuk
http://arxiv.org/abs/1906.03367v1

• [cs.LG]Watch, Try, Learn: Meta-Learning from Demonstrations and Reward
Allan Zhou, Eric Jang, Daniel Kappler, Alex Herzog, Mohi Khansari, Paul Wohlhart, Yunfei Bai, Mrinal Kalakrishnan, Sergey Levine, Chelsea Finn
http://arxiv.org/abs/1906.03352v1

• [cs.LG]apricot: Submodular selection for data summarization in Python
Jacob Schreiber, Jeffrey Bilmes, William Stafford Noble
http://arxiv.org/abs/1906.03543v1

• [cs.NE]Class-specific Differential Detection in Diffractive Optical Neural Networks Improves Inference Accuracy
Jingxi Li, Deniz Mengu, Yi Luo, Yair Rivenson, Aydogan Ozcan
http://arxiv.org/abs/1906.03417v1

• [cs.NE]Data-driven Reconstruction of Nonlinear Dynamics from Sparse Observation
Kyongmin Yeo
http://arxiv.org/abs/1906.04059v1

• [cs.NE]Enhanced Optimization with Composite Objectives and Novelty Pulsation
Hormoz Shahrzad, Babak Hodjat, Camille Dollé, Andrei Denissov, Simon Lau, Donn Goodhew, Justin Dyer, Risto Miikkulainen
http://arxiv.org/abs/1906.04050v1

• [cs.NE]Exploration and Exploitation in Symbolic Regression using Quality-Diversity and Evolutionary Strategies Algorithms
J. -P. Bruneton, L. Cazenille, A. Douin, V. Reverdy
http://arxiv.org/abs/1906.03959v1

• [cs.NE]When and Why Metaheuristics Researchers Can Ignore “No Free Lunch” Theorems
James McDermott
http://arxiv.org/abs/1906.03280v1

• [cs.NI]Optimal Task Offloading and Resource Allocation for Fog Computing
Thai T. Vu, Diep N. Nguyen, Dinh Thai Hoang, Eryk Dutkiewicz
http://arxiv.org/abs/1906.03567v1

• [cs.RO]A Hierarchical Network for Diverse Trajectory Proposals
Sriram N. N., Gourav Kumar, Abhay Singh, M. Siva Karthik, Saket Saurav Brojeshwar Bhowmick, K. Madhava Krishna
http://arxiv.org/abs/1906.03584v1

• [cs.RO]Composition of Safety Constraints With Applications to Decentralized Fixed-Wing Collision Avoidance
Eric Squires, Pietro Pierpaoli, Rohit Konda, Sam Coogan, Magnus Egerstedt
http://arxiv.org/abs/1906.03771v1

• [cs.RO]Control of A High Performance Bipedal Robot using Viscoelastic Liquid Cooled Actuators
Junhyeok Ahn, Donghyun Kim, SeungHyeon Bang, Nick Paine, Luis Sentis
http://arxiv.org/abs/1906.03811v1

• [cs.RO]Data Efficient and Safe Learning for Locomotion via Simplified Model
Junhyeok Ahn, Jaemin Lee, Luis Sentis
http://arxiv.org/abs/1906.03812v1

• [cs.RO]DensePhysNet: Learning Dense Physical Object Representations via Multi-step Dynamic Interactions
Zhenjia Xu, Jiajun Wu, Andy Zeng, Joshua B. Tenenbaum, Shuran Song
http://arxiv.org/abs/1906.03853v1

• [cs.RO]Enabling Robust State Estimation through Measurement Error Covariance Adaptation
Ryan M. Watson, Jason N. Gross, Clark N. Taylor, Robert C. Leishman
http://arxiv.org/abs/1906.04055v1

• [cs.RO]Movable-Object-Aware Visual SLAM via Weakly Supervised Semantic Segmentation
Ting Sun, Yuxiang Sun, Ming Liu, Dit-Yan Yeung
http://arxiv.org/abs/1906.03629v1

• [cs.RO]Peristaltic locomotion without digital controllers: Exploiting the origami multi-stability to coordinate robotic motions
Priyanka Bhovad, Joshua Kaufmann, Suyi Li
http://arxiv.org/abs/1906.04091v1

• [cs.RO]Rethinking Trajectory Evaluation for SLAM: a Probabilistic, Continuous-Time Approach
Zichao Zhang, Davide Scaramuzza
http://arxiv.org/abs/1906.03996v1

• [cs.RO]Simplified Kinematics of Continuum Robot Equilibrium Modulation via Moment Coupling Effects and Model Calibration
Long Wang, Giuseppe Del Giudice, Nabil Simaan
http://arxiv.org/abs/1906.03582v1

• [cs.RO]Trajectory Optimization for Robust Humanoid Locomotion with Sample-Efficient Learning
Majid Khadiv, Mohammad Hasan Yeganegi, S. Ali A. Moosavian, Jia-Jie Zhu, Ludovic Righetti
http://arxiv.org/abs/1906.03684v1

• [cs.SD]Deep Music Analogy Via Latent Representation Disentanglement
Ruihan Yang, Dingsu Wang, Ziyu Wang, Tianyao Chen, Junyan Jiang, Gus Xia
http://arxiv.org/abs/1906.03626v1

• [cs.SD]rVAD: An Unsupervised Segment-Based Robust Voice Activity Detection Method
Zheng-Hua Tan, Achintya kr. Sarkar, Najim Dehak
http://arxiv.org/abs/1906.03588v1

• [cs.SE]Recovering Variable Names for Minified Code with Usage Contexts
Hieu Tran, Ngoc Tran, Son Nguyen, Hoan Nguyen, Tien Nguyen
http://arxiv.org/abs/1906.03488v1

• [cs.SI]Approximate Identification of the Optimal Epidemic Source in Complex Networks
S. Jalil Kazemitabar, Arash A. Amini
http://arxiv.org/abs/1906.03052v2

• [cs.SI]Detection and Prediction of Users Attitude Based on Real-Time and Batch Sentiment Analysis of Facebook Comments
Hieu Tran, Maxim Shcherbakov
http://arxiv.org/abs/1906.03392v1

• [cs.SI]Having the Last Word: Understanding How to Sample Discussions Online
Gioia Boschi, Anthony P. Young, Sagar Joglekar, Chiara Cammarota, Nishanth Sastry
http://arxiv.org/abs/1906.04148v1

• [cs.SI]Learning Individual Treatment Effects from Networked Observational Data
Ruocheng Guo, Jundong Li, Huan Liu
http://arxiv.org/abs/1906.03485v1

• [cs.SI]News Labeling as Early as Possible: Real or Fake?
Maryam Ramezani, Mina Rafiei, Soroush Omranpour, Hamid R. Rabiee
http://arxiv.org/abs/1906.03423v1

• [cs.SI]Transfer Learning for Hate Speech Detection in Social Media
Marian-Andrei Rizoiu, Tianyu Wang, Gabriela Ferraro, Hanna Suominen
http://arxiv.org/abs/1906.03829v1

• [cs.SY]Region of Attraction for Power Systems using Gaussian Process and Converse Lyapunov Function — Part I: Theoretical Framework and Off-line Study
Chao Zhai, Hung D. Nguyen
http://arxiv.org/abs/1906.03590v1

• [eess.IV]3DFPN-HS$^2$: 3D Feature Pyramid Network Based High Sensitivity and Specificity Pulmonary Nodule Detection
Jingya Liu, Liangliang Cao, Oguz Akin, Yingli Tian
http://arxiv.org/abs/1906.03467v1

• [eess.IV]Association of genomic subtypes of lower-grade gliomas with shape features automatically extracted by a deep learning algorithm
Mateusz Buda, Ashirbani Saha, Maciej A Mazurowski
http://arxiv.org/abs/1906.03720v1

• [eess.IV]Automatic Segmentation of Vestibular Schwannoma from T2-Weighted MRI by Deep Spatial Attention with Hardness-Weighted Loss
Guotai Wang, Jonathan Shapey, Wenqi Li, Reuben Dorent, Alex Demitriadis, Sotirios Bisdas, Ian Paddick, Robert Bradford, Sebastien Ourselin, Tom Vercauteren
http://arxiv.org/abs/1906.03906v1

• [eess.IV]Consensus Neural Network for Medical Imaging Denoising with Only Noisy Training Samples
Dufan Wu, Kuang Gong, Kyungsang Kim, Quanzheng Li
http://arxiv.org/abs/1906.03639v1

• [eess.IV]Interpreting Age Effects of Human Fetal Brain from Spontaneous fMRI using Deep 3D Convolutional Neural Networks
Xiangrui Li, Jasmine Hect, Moriah Thomason, Dongxiao Zhu
http://arxiv.org/abs/1906.03691v1

• [eess.IV]Multiparametric Deep Learning and Radiomics for Tumor Grading and Treatment Response Assessment of Brain Cancer: Preliminary Results
Vishwa S. Parekh, John Laterra, Chetan Bettegowda, Alex E. Bocchieri, Jay J. Pillai, Michael A. Jacobs
http://arxiv.org/abs/1906.04049v1

• [eess.IV]Semi-supervised Complex-valued GAN for Polarimetric SAR Image Classification
Qigong Sun, Xiufang Li, Lingling Li, Xu Liu, Fang Liu, Licheng Jiao
http://arxiv.org/abs/1906.03605v1

• [eess.SP]A Distributed Event-Triggered Control Strategy for DC Microgrids Based on Publish-Subscribe Model Over Industrial Wireless Sensor Networks
Seyed Amir Alavi, Kamyar Mehran, Yang Hao, Ardavan Rahimian, Hamed Mirsaeedi, Vahid Vahidinasab
http://arxiv.org/abs/1906.03623v1

• [eess.SP]Learned Conjugate Gradient Descent Network for Massive MIMO Detection
Yi Wei, Ming-Min Zhao, Mingyi Hong, Min-jian Zhao, Ming Lei
http://arxiv.org/abs/1906.03814v1

• [eess.SP]Resource Management optimally in Non-Orthogonal Multiple Access Networks for fifth-generation by using game-theoretic
Mahsa Khodakhah, Nahid Ardalani
http://arxiv.org/abs/1906.03465v1

• [eess.SP]Supervised and Semi-Supervised Learning for MIMO Blind Detection with Low-Resolution ADCs
Ly V. Nguyen, Duy T. Ngo, Nghi H. Tran, A. Lee Swindlehurst, Duy H. N. Nguyen
http://arxiv.org/abs/1906.04090v1

• [math.DS]Physics-Informed Probabilistic Learning of Linear Embeddings of Non-linear Dynamics With Guaranteed Stability
Shaowu Pan, Karthik Duraisamy
http://arxiv.org/abs/1906.03663v1

• [math.OC]A Non-Asymptotic Analysis of Network Independence for Distributed Stochastic Gradient Descent
Alex Olshevsky, Ioannis Ch. Paschalidis, Shi Pu
http://arxiv.org/abs/1906.02702v2

• [math.OC]Stochastic In-Face Frank-Wolfe Methods for Non-Convex Optimization and Sparse Neural Network Training
Paul Grigas, Alfonso Lobos, Nathan Vermeersch
http://arxiv.org/abs/1906.03580v1

• [math.ST]A New One Parameter Bimodal Skew Logistic Distribution and its Applications
S. Shah, S. Chakraborty, P. J. Hazarika
http://arxiv.org/abs/1906.04125v1

• [math.ST]Estimation Rates for Sparse Linear Cyclic Causal Models
Jan-Christian Hütter, Philippe Rigollet
http://arxiv.org/abs/1906.03371v1

• [math.ST]Nonparametric Independence Testing for Right-Censored Data using Optimal Transport
David Rindt, Dino Sejdinovic, David Steinsaltz
http://arxiv.org/abs/1906.03866v1

• [math.ST]On statistical Calderón problems
Kweku Abraham, Richard Nickl
http://arxiv.org/abs/1906.03486v1

• [math.ST]Optimal Convergence for Stochastic Optimization with Multiple Expectation Constraints
Kinjal Basu, Preetam Nandy
http://arxiv.org/abs/1906.03401v1

• [physics.soc-ph]The key to the weak-ties phenomenon
Ke-ke Shang, Michael Small, Di Yin, Yan Wang, Tong-chen Li
http://arxiv.org/abs/1906.03662v1

• [stat.AP]A Comprehensive Hidden Markov Model for Hourly Rainfall Time Series
Oliver Stoner, Theo Economou
http://arxiv.org/abs/1906.03846v1

• [stat.AP]A Naive Bayes Approach for NFL Passing Evaluation using Tracking Data Extracted from Images
Sarah Mallepalle, Ron Yurko, Konstantinos Pelechrinis, Samuel L. Ventura
http://arxiv.org/abs/1906.03339v1

• [stat.AP]Big Variates: Visualizing and identifying key variables in a multivariate world
S. J. Watts, L. Crow
http://arxiv.org/abs/1906.04116v1

• [stat.AP]Efficient Bayesian estimation for GARCH-type models via Sequential Monte Carlo
Dan Li, Adam Clements, Christopher Drovandi
http://arxiv.org/abs/1906.03828v1

• [stat.AP]Incorporating Open Data into Introductory Courses in Statistics
Roberto Rivera, Mario Marazzi, Pedro Torres
http://arxiv.org/abs/1906.03762v1

• [stat.AP]Modeling Excess Deaths After a Natural Disaster with Application to Hurricane Maria
Roberto Rivera, Wolfgang Rolke
http://arxiv.org/abs/1906.03714v1

• [stat.AP]On Copula-based Collective Risk Models
Rosy Oh, Jae Youn Ahn, Woojoo Lee
http://arxiv.org/abs/1906.03604v1

• [stat.AP]Pitfalls and Protocols in Practice of Manufacturing Data Science
Chia-Yen Lee
http://arxiv.org/abs/1906.04025v1

• [stat.CO]A Low Rank Gaussian Process Prediction Model for Very Large Datasets
Roberto Rivera
http://arxiv.org/abs/1906.03564v1

• [stat.ME]An Approximate Restricted Likelihood Ratio Test for Variance Components in Generalized Linear Mixed Models
Stephanie T. Chen, Luo Xiao, Ana-Maria Staicu
http://arxiv.org/abs/1906.03320v1

• [stat.ME]Graph Independence Testing
Junhao Xiong, Cencheng Shen, Jesüs Arroyo, Joshua T. Vogelstein
http://arxiv.org/abs/1906.03661v1

• [stat.ME]Multimodal Data Fusion of Non-Gaussian Spatial Fields in Sensor Networks
Pengfei Zhang, Gareth W. Peters, Ido Nevat, Keng Boon Teo, Yixin Wang
http://arxiv.org/abs/1906.03772v1

• [stat.ME]On the Structure of Ordered Latent Trait Models
Gerhard Tutz
http://arxiv.org/abs/1906.03851v1

• [stat.ML]A Variant of Gaussian Process Dynamical Systems
Jing Zhao, Jingjing Fei, Shiliang Sun
http://arxiv.org/abs/1906.03647v1

• [stat.ML]A cost-reducing partial labeling estimator in text classification problem
Jiangning Chen, Zhibo Dai, Juntao Duan, Qianli Hu, Ruilin Li, Heinrich Matzinger, Ionel Popescu, Haoyan Zhai
http://arxiv.org/abs/1906.03768v1

• [stat.ML]Bayesian Automatic Relevance Determination for Utility Function Specification in Discrete Choice Models
Filipe Rodrigues, Nicola Ortelli, Michel Bierlaire, Francisco Pereira
http://arxiv.org/abs/1906.03855v1

• [stat.ML]Bayesian Tensor Filtering: Smooth, Locally-Adaptive Factorization of Functional Matrices
Wesley Tansey, Christopher Tosh, David M. Blei
http://arxiv.org/abs/1906.04072v1

• [stat.ML]Benchmarking Minimax Linkage
Xiao Hui Tai, Kayla Frisoli
http://arxiv.org/abs/1906.03336v1

• [stat.ML]Confidence intervals for class prevalences under prior probability shift
Dirk Tasche
http://arxiv.org/abs/1906.04119v1

• [stat.ML]Goodness-of-fit Test for Latent Block Models
Chihiro Watanabe, Taiji Suzuki
http://arxiv.org/abs/1906.03886v1

• [stat.ML]Guidelines for Responsible and Human-Centered Use of Explainable Machine Learning
Patrick Hall
http://arxiv.org/abs/1906.03533v1

• [stat.ML]Inference and Uncertainty Quantification for Noisy Matrix Completion
Yuxin Chen, Jianqing Fan, Cong Ma, Yuling Yan
http://arxiv.org/abs/1906.04159v1

• [stat.ML]Integrative Factorization of Bidimensionally Linked Matrices
Jun Young Park, Eric F. Lock
http://arxiv.org/abs/1906.03722v1

• [stat.ML]Lift Up and Act! Classifier Performance in Resource-Constrained Applications
Galit Shmueli
http://arxiv.org/abs/1906.03374v1

• [stat.ML]Multiway clustering via tensor block models
Yuchen Zeng, Miaoyan Wang
http://arxiv.org/abs/1906.03807v1

• [stat.ML]Neural Spline Flows
Conor Durkan, Artur Bekasov, Iain Murray, George Papamakarios
http://arxiv.org/abs/1906.04032v1

• [stat.ML]On the Insufficiency of the Large Margins Theory in Explaining the Performance of Ensemble Methods
Waldyn Martinez, J. Brian Gray
http://arxiv.org/abs/1906.04063v1

• [stat.ML]Optimal Transport Relaxations with Application to Wasserstein GANs
Saied Mahdian, Jose Blanchet, Peter Glynn
http://arxiv.org/abs/1906.03317v1

• [stat.ML]Robust conditional GANs under missing or uncertain labels
Kiran Koshy Thekumparampil, Sewoong Oh, Ashish Khetan
http://arxiv.org/abs/1906.03579v1

• [stat.ML]Sampling Humans for Optimizing Preferences in Coloring Artwork
Michael McCourt, Ian Dewancker
http://arxiv.org/abs/1906.03813v1

• [stat.ML]Sparse Variational Inference: Bayesian Coresets from Scratch
Trevor Campbell, Boyan Beronov
http://arxiv.org/abs/1906.03329v1

• [stat.ML]The Broad Optimality of Profile Maximum Likelihood
Yi Hao, Alon Orlitsky
http://arxiv.org/abs/1906.03794v1

• [stat.ML]The Impact of Regularization on High-dimensional Logistic Regression
Fariborz Salehi, Ehsan Abbasi, Babak Hassibi
http://arxiv.org/abs/1906.03761v1

• [stat.ML]The Implicit Bias of AdaGrad on Separable Data
Qian Qian, Xiaoyuan Qian
http://arxiv.org/abs/1906.03559v1

• [stat.ML]The Implicit Metropolis-Hastings Algorithm
Kirill Neklyudov, Evgenii Egorov, Dmitry Vetrov
http://arxiv.org/abs/1906.03644v1