cs.AI - 人工智能

    cs.CL - 计算与语言 cs.CR - 加密与安全 cs.CV - 机器视觉与模式识别 cs.CY - 计算与社会 cs.DC - 分布式、并行与集群计算 cs.DL - 数字图书馆 cs.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.NE - 神经与进化计算 cs.PF - 计算性能 cs.PL - 编程语言 cs.RO - 机器人学 cs.SI - 社交网络与信息网络 econ.EM - 计量经济学 econ.GN - 一般经济学 eess.AS - 语音处理 eess.IV - 图像与视频处理 eess.SP - 信号处理 eess.SY - 系统和控制 math.CO - 组合数学 math.OC - 优化与控制 math.ST - 统计理论 physics.soc-ph - 物理学与社会 q-fin.ST - 统计金融学 quant-ph - 量子物理 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]Survey of Artificial Intelligence for Card Games and Its Application to the Swiss Game Jass
    • [cs.AI]Two-step Constructive Approaches for Dungeon Generation
    • [cs.CL]A Document-grounded Matching Network for Response Selection in Retrieval-based Chatbots
    • [cs.CL]Automated Curriculum Learning for Turn-level Spoken Language Understanding with Weak Supervision
    • [cs.CL]Counterfactual Data Augmentation for Mitigating Gender Stereotypes in Languages with Rich Morphology
    • [cs.CL]DoubleTransfer at MEDIQA 2019: Multi-Source Transfer Learning for Natural Language Understanding in the Medical Domain
    • [cs.CL]Estimating Causal Effects of Tone in Online Debates
    • [cs.CL]Federated Learning for Emoji Prediction in a Mobile Keyboard
    • [cs.CL]Generating Summaries with Topic Templates and Structured Convolutional Decoders
    • [cs.CL]HEAD-QA: A Healthcare Dataset for Complex Reasoning
    • [cs.CL]Identifying Visible Actions in Lifestyle Vlogs
    • [cs.CL]Inter-sentence Relation Extraction with Document-level Graph Convolutional Neural Network
    • [cs.CL]Journal Name Extraction from Japanese Scientific News Articles
    • [cs.CL]Label-Agnostic Sequence Labeling by Copying Nearest Neighbors
    • [cs.CL]Learning a Matching Model with Co-teaching for Multi-turn Response Selection in Retrieval-based Dialogue Systems
    • [cs.CL]Lightweight and Efficient Neural Natural Language Processing with Quaternion Networks
    • [cs.CL]Modeling Sentiment Dependencies with Graph Convolutional Networks for Aspect-level Sentiment Classification
    • [cs.CL]Parallel Scheduled Sampling
    • [cs.CL]Psycholinguistics meets Continual Learning: Measuring Catastrophic Forgetting in Visual Question Answering
    • [cs.CL]Reinforcement Learning of Minimalist Numeral Grammars
    • [cs.CL]Retrieve, Read, Rerank: Towards End-to-End Multi-Document Reading Comprehension
    • [cs.CL]Self-Supervised Learning for Contextualized Extractive Summarization
    • [cs.CL]Using Structured Representation and Data: A Hybrid Model for Negation and Sentiment in Customer Service Conversations
    • [cs.CL]What Does BERT Look At? An Analysis of BERT’s Attention
    • [cs.CL]What Kind of Language Is Hard to Language-Model?
    • [cs.CL]Word-level Speech Recognition with a Dynamic Lexicon
    • [cs.CR]New dynamic and verifiable multi-secret sharing schemes based on LFSR public key cryptosystem
    • [cs.CV]3-D Surface Segmentation Meets Conditional Random Fields
    • [cs.CV]BDNet: Bengali handwritten numeral digit recognition based on densely connected convolutional neural networks
    • [cs.CV]Bag of Color Features For Color Constancy
    • [cs.CV]Band Attention Convolutional Networks For Hyperspectral Image Classification
    • [cs.CV]CVPR19 Tracking and Detection Challenge: How crowded can it get?
    • [cs.CV]Challenges in Time-Stamp Aware Anomaly Detection in Traffic Videos
    • [cs.CV]Clouds of Oriented Gradients for 3D Detection of Objects, Surfaces, and Indoor Scene Layouts
    • [cs.CV]Cross-Modal Relationship Inference for Grounding Referring Expressions
    • [cs.CV]E-LPIPS: Robust Perceptual Image Similarity via Random Transformation Ensembles
    • [cs.CV]End-to-End CAD Model Retrieval and 9DoF Alignment in 3D Scans
    • [cs.CV]FAMED-Net: A Fast and Accurate Multi-scale End-to-end Dehazing Network
    • [cs.CV]FASTER Recurrent Networks for Video Classification
    • [cs.CV]Few-Shot Point Cloud Region Annotation with Human in the Loop
    • [cs.CV]Gated CRF Loss for Weakly Supervised Semantic Image Segmentation
    • [cs.CV]Hybrid Function Sparse Representation towards Image Super Resolution
    • [cs.CV]Joint Subspace Recovery and Enhanced Locality Driven Robust Flexible Discriminative Dictionary Learning
    • [cs.CV]Large-scale Landmark Retrieval/Recognition under a Noisy and Diverse Dataset
    • [cs.CV]Learning robust visual representations using data augmentation invariance
    • [cs.CV]Mimic and Fool: A Task Agnostic Adversarial Attack
    • [cs.CV]NAS-FCOS: Fast Neural Architecture Search for Object Detection
    • [cs.CV]Object-aware Aggregation with Bidirectional Temporal Graph for Video Captioning
    • [cs.CV]On Stabilizing Generative Adversarial Training with Noise
    • [cs.CV]On the Vector Space in Photoplethysmography Imaging
    • [cs.CV]Online Object Representations with Contrastive Learning
    • [cs.CV]PAN: Projective Adversarial Network for Medical Image Segmentation
    • [cs.CV]Patch Transformer for Multi-tagging Whole Slide Histopathology Images
    • [cs.CV]Polysemous Visual-Semantic Embedding for Cross-Modal Retrieval
    • [cs.CV]Recognizing License Plates in Real-Time
    • [cs.CV]Rethinking Person Re-Identification with Confidence
    • [cs.CV]Scale Invariant Fully Convolutional Network: Detecting Hands Efficiently
    • [cs.CV]Semantic-guided Encoder Feature Learning for Blurry Boundary Delineation
    • [cs.CV]Shapes and Context: In-the-Wild Image Synthesis & Manipulation
    • [cs.CV]Simultaneously Learning Architectures and Features of Deep Neural Networks
    • [cs.CV]Single Image Blind Deblurring Using Multi-Scale Latent Structure Prior
    • [cs.CV]Subspace Attack: Exploiting Promising Subspaces for Query-Efficient Black-box Attacks
    • [cs.CV]SymNet: Symmetrical Filters in Convolutional Neural Networks
    • [cs.CV]TW-SMNet: Deep Multitask Learning of Tele-Wide Stereo Matching
    • [cs.CV]iProStruct2D: Identifying protein structural classes by deep learning via 2D representations
    • [cs.CY]Creation of User Friendly Datasets: Insights from a Case Study concerning Explanations of Loan Denials
    • [cs.DC]Anomaly Detection in High Performance Computers: A Vicinity Perspective
    • [cs.DC]Membership-based Manoeuvre Negotiation in Autonomous and Safety-critical Vehicular Systems
    • [cs.DC]Window Based BFT Blockchain Consensus
    • [cs.DL]EXmatcher: Combining Features Based on Reference Strings and Segments to Enhance Citation Matching
    • [cs.GR]Differentiable Surface Splatting for Point-based Geometry Processing
    • [cs.HC]Human-Machine Collaboration for Fast Land Cover Mapping
    • [cs.IR]Evaluation of Seed Set Selection Approaches and Active Learning Strategies in Predictive Coding
    • [cs.IR]Modeling the Past and Future Contexts for Session-based Recommendation
    • [cs.IR]The snippets taxonomy in web search engines
    • [cs.IT]Artificial Noisy MIMO Systems under Correlated Scattering Rayleigh Fading — A Physical Layer Security Approach
    • [cs.IT]Beamforming Optimization for Wireless Network Aided by Intelligent Reflecting Surface with Discrete Phase Shifts
    • [cs.IT]Dual-Band Fading Multiple Access Relay Channels
    • [cs.IT]Orthogonal Cocktail BPSK: Exceeding Shannon Capacity of QPSK Input
    • [cs.IT]Rate-Splitting Unifying SDMA, OMA, NOMA, and Multicasting in MISO Broadcast Channel: A Simple Two-User Rate Analysis
    • [cs.IT]Reinforcement Learning for Channel Coding: Learned Bit-Flipping Decoding
    • [cs.IT]Stability and Metastability of Traffic Dynamics in Uplink Random Access Networks
    • [cs.LG]A Taxonomy of Channel Pruning Signals in CNNs
    • [cs.LG]A refined primal-dual analysis of the implicit bias
    • [cs.LG]Adaptively Preconditioned Stochastic Gradient Langevin Dynamics
    • [cs.LG]An Improved Analysis of Training Over-parameterized Deep Neural Networks
    • [cs.LG]Analysis Of Momentum Methods
    • [cs.LG]Associative Convolutional Layers
    • [cs.LG]BasisConv: A method for compressed representation and learning in CNNs
    • [cs.LG]Building High-Quality Auction Fraud Dataset
    • [cs.LG]Causal Discovery with Reinforcement Learning
    • [cs.LG]Communication and Memory Efficient Testing of Discrete Distributions
    • [cs.LG]Continual Reinforcement Learning deployed in Real-life using Policy Distillation and Sim2Real Transfer
    • [cs.LG]Coupled Variational Recurrent Collaborative Filtering
    • [cs.LG]Data-Free Quantization through Weight Equalization and Bias Correction
    • [cs.LG]DropConnect Is Effective in Modeling Uncertainty of Bayesian Deep Networks
    • [cs.LG]Dynamical Anatomy of NARMA10 Benchmark Task
    • [cs.LG]Efficient Kernel-based Subsequence Search for User Identification from Walking Activity
    • [cs.LG]Evaluating aleatoric and epistemic uncertainties of time series deep learning models for soil moisture predictions
    • [cs.LG]Evolutionary Trigger Set Generation for DNN Black-Box Watermarking
    • [cs.LG]Exploration via Hindsight Goal Generation
    • [cs.LG]Extracting Interpretable Concept-Based Decision Trees from CNNs
    • [cs.LG]Fast Rates for a kNN Classifier Robust to Unknown Asymmetric Label Noise
    • [cs.LG]Fast and Accurate Least-Mean-Squares Solvers
    • [cs.LG]Faster Algorithms for High-Dimensional Robust Covariance Estimation
    • [cs.LG]Gossip-based Actor-Learner Architectures for Deep Reinforcement Learning
    • [cs.LG]Graph Convolutional Transformer: Learning the Graphical Structure of Electronic Health Records
    • [cs.LG]Importance Resampling for Off-policy Prediction
    • [cs.LG]Large Scale Structure of Neural Network Loss Landscapes
    • [cs.LG]Learning Powerful Policies by Using Consistent Dynamics Model
    • [cs.LG]Learning Selection Masks for Deep Neural Networks
    • [cs.LG]Meta-Learning Neural Bloom Filters
    • [cs.LG]Metrics for Learning in Topological Persistence
    • [cs.LG]On Single Source Robustness in Deep Fusion Models
    • [cs.LG]Optimizing Pipelined Computation and Communication for Latency-Constrained Edge Learning
    • [cs.LG]Performance Analysis and Characterization of Training Deep Learning Models on NVIDIA TX2
    • [cs.LG]Quantifying Intrinsic Uncertainty in Classification via Deep Dirichlet Mixture Networks
    • [cs.LG]Representation Learning-Assisted Click-Through Rate Prediction
    • [cs.LG]Scaling Laws for the Principled Design, Initialization and Preconditioning of ReLU Networks
    • [cs.LG]Stochastic Neural Network with Kronecker Flow
    • [cs.LG]Topology Attack and Defense for Graph Neural Networks: An Optimization Perspective
    • [cs.LG]Towards Amortized Ranking-Critical Training for Collaborative Filtering
    • [cs.LG]Transfer Learning for Ultrasound Tongue Contour Extraction with Different Domains
    • [cs.LG]Ultra Fast Medoid Identification via Correlated Sequential Halving
    • [cs.LG]Variance-reduced $Q$-learning is minimax optimal
    • [cs.LG]Wasserstein Reinforcement Learning
    • [cs.LG]Weight Agnostic Neural Networks
    • [cs.LG]WikiDataSets : Standardized sub-graphs from WikiData
    • [cs.LG]k-Nearest Neighbor Optimization via Randomized Hyperstructure Convex Hull
    • [cs.NE]Classification of EEG Signals using Genetic Programming for Feature Construction
    • [cs.NE]Unsupervised Minimax: Adversarial Curiosity, Generative Adversarial Networks, and Predictability Minimization
    • [cs.PF]ROOT I/O compression algorithms and their performance impact within Run 3
    • [cs.PL]Write, Execute, Assess: Program Synthesis with a REPL
    • [cs.RO]Automatic Multi-Sensor Extrinsic Calibration for Mobile Robots
    • [cs.RO]Bilevel Optimization for Planning through Contact: A Semidirect Method
    • [cs.RO]Design and integration of a parallel, soft robotic end-effector for extracorporeal ultrasound
    • [cs.RO]Gait modeling and optimization for the perturbed Stokes regime
    • [cs.RO]Visual-Inertial Odometry of Aerial Robots
    • [cs.SI]Boosting Students’ Performance With The Aid Of Social Network Analysis
    • [cs.SI]Control contribution identifies top driver nodes in complex networks
    • [cs.SI]Corrected overlap weight and clustering coefficient
    • [cs.SI]Examining Untempered Social Media: Analyzing Cascades of Polarized Conversations
    • [cs.SI]Fine-grained Event Categorization with Heterogeneous Graph Convolutional Networks
    • [cs.SI]Heterogeneous network approach to predict individuals’ mental health
    • [cs.SI]Latent Channel Networks
    • [cs.SI]Network-based Fake News Detection: A Pattern-driven Approach
    • [cs.SI]StRE: Self Attentive Edit Quality Prediction in Wikipedia
    • [econ.EM]Bias-Aware Inference in Fuzzy Regression Discontinuity Designs
    • [econ.EM]The Regression Discontinuity Design
    • [econ.GN]ProPublica’s COMPAS Data Revisited
    • [eess.AS]Using generative modelling to produce varied intonation for speech synthesis
    • [eess.IV]A Hybrid Approach Between Adversarial Generative Networks and Actor-Critic Policy Gradient for Low Rate High-Resolution Image Compression
    • [eess.IV]A Novel Cost Function for Despeckling using Convolutional Neural Networks
    • [eess.IV]Alzheimer’s Disease Brain MRI Classification: Challenges and Insights
    • [eess.IV]Automatic brain tissue segmentation in fetal MRI using convolutional neural networks
    • [eess.IV]BowNet: Dilated Convolution Neural Network for Ultrasound Tongue Contour Extraction
    • [eess.IV]Deep learning analysis of cardiac CT angiography for detection of coronary arteries with functionally significant stenosis
    • [eess.IV]DeepcomplexMRI: Exploiting deep residual network for fast parallel MR imaging with complex convolution
    • [eess.IV]Generative adversarial network for segmentation of motion affected neonatal brain MRI
    • [eess.IV]Multiscale Nakagami parametric imaging for improved liver tumor localization
    • [eess.IV]`Project & Excite’ Modules for Segmentation of Volumetric Medical Scans
    • [eess.SP]Adaptive Neural Signal Detection for Massive MIMO
    • [eess.SP]Beam Learning — Using Machine Learning for Finding Beam Directions
    • [eess.SP]Transport Triggered Array Processor for Vision Applications
    • [eess.SY]Sequential Source Coding for Stochastic Systems Subject to Finite Rate Constraints
    • [math.CO]Schur ring and Codes for $S$-subgroups over $\Z_{2}^{n}$
    • [math.OC]Hybrid Nonlinear Observers for Inertial Navigation Using Landmark Measurements
    • [math.ST]Convergence of Dümbgen’s Algorithm for Estimation of Tail Inflation
    • [math.ST]Detection and estimation of parameters in high dimensional multiple change point regression models via $\ell_1/\ell_0$ regularization and discrete optimization
    • [math.ST]Mean estimation and regression under heavy-tailed distributions—a survey
    • [math.ST]Monte Carlo and Quasi-Monte Carlo Density Estimation via Conditioning
    • [math.ST]Selection consistency of Lasso-based procedures for misspecified high-dimensional binary model and random regressors
    • [physics.soc-ph]Achieving competitive advantage in academia through early career coauthorship with top scientists
    • [physics.soc-ph]Analysis of the susceptible-infected-susceptible epidemic dynamics in networks via the non-backtracking matrix
    • [physics.soc-ph]Intertemporal Community Detection in Bikeshare Networks
    • [q-fin.ST]Likelihood Evaluation of Jump-Diffusion Models Using Deterministic Nonlinear Filters
    • [quant-ph]Quantum Random Numbers generated by the Cloud Superconducting Quantum Computer
    • [stat.AP]Assessing the effects of exposure to sulfuric acid aerosol on respiratory function in adults
    • [stat.AP]Causal Inference in Higher Education: Building Better Curriculums
    • [stat.AP]Characterization and valuation of uncertainty of calibrated parameters in stochastic decision models
    • [stat.AP]Regional economic convergence and spatial quantile regression
    • [stat.AP]Statistical Species Identification
    • [stat.AP]Technical Preprint: Rationale and Design of a Planned Observational Study to Evaluate the Impact of Hydrocodone Rescheduling on Opioid Prescribing After Surgery
    • [stat.CO]Likelihood-free approximate Gibbs sampling
    • [stat.ME]Adaptative significance levels in linear regression models with known variance
    • [stat.ME]An approximate Bayesian approach to regression estimation with many auxiliary variables
    • [stat.ML]Approximate Variational Inference Based on a Finite Sample of Gaussian Latent Variables
    • [stat.ML]Asymptotic Guarantees for Learning Generative Models with the Sliced-Wasserstein Distance
    • [stat.ML]Efficient structure learning with automatic sparsity selection for causal graph processes
    • [stat.ML]Maximum Mean Discrepancy Gradient Flow
    • [stat.ML]Principled Training of Neural Networks with Direct Feedback Alignment
    • [stat.ML]Probabilistic Forecasting with Temporal Convolutional Neural Network
    • [stat.ML]SALT: Subspace Alignment as an Auxiliary Learning Task for Domain Adaptation
    • [stat.ML]Stable Rank Normalization for Improved Generalization in Neural Networks and GANs

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

    • [cs.AI]“Did You Hear That?” Learning to Play Video Games from Audio Cues
    Raluca D. Gaina, Matthew Stephenson
    http://arxiv.org/abs/1906.04027v2

    • [cs.AI]Survey of Artificial Intelligence for Card Games and Its Application to the Swiss Game Jass
    Joel Niklaus, Michele Alberti, Vinaychandran Pondenkandath, Rolf Ingold, Marcus Liwicki
    http://arxiv.org/abs/1906.04439v1

    • [cs.AI]Two-step Constructive Approaches for Dungeon Generation
    Michael Cerny Green, Ahmed Khalifa, Athoug Alsoughayer, Divyesh Surana, Antonios Liapis, Julian Togelius
    http://arxiv.org/abs/1906.04660v1

    • [cs.CL]A Document-grounded Matching Network for Response Selection in Retrieval-based Chatbots
    Xueliang Zhao, Chongyang Tao, Wei Wu, Can Xu, Dongyan Zhao, Rui Yan
    http://arxiv.org/abs/1906.04362v1

    • [cs.CL]Automated Curriculum Learning for Turn-level Spoken Language Understanding with Weak Supervision
    Hao Lang, Wen Wang
    http://arxiv.org/abs/1906.04291v1

    • [cs.CL]Counterfactual Data Augmentation for Mitigating Gender Stereotypes in Languages with Rich Morphology
    Ran Zmigrod, Sebastian J. Mielke, Hanna Wallach, Ryan Cotterell
    http://arxiv.org/abs/1906.04571v1

    • [cs.CL]DoubleTransfer at MEDIQA 2019: Multi-Source Transfer Learning for Natural Language Understanding in the Medical Domain
    Yichong Xu, Xiaodong Liu, Chunyuan Li, Hoifung Poon, Jianfeng Gao
    http://arxiv.org/abs/1906.04382v1

    • [cs.CL]Estimating Causal Effects of Tone in Online Debates
    Dhanya Sridhar, Lise Getoor
    http://arxiv.org/abs/1906.04177v1

    • [cs.CL]Federated Learning for Emoji Prediction in a Mobile Keyboard
    Swaroop Ramaswamy, Rajiv Mathews, Kanishka Rao, Françoise Beaufays
    http://arxiv.org/abs/1906.04329v1

    • [cs.CL]Generating Summaries with Topic Templates and Structured Convolutional Decoders
    Laura Perez-Beltrachini, Yang Liu, Mirella Lapata
    http://arxiv.org/abs/1906.04687v1

    • [cs.CL]HEAD-QA: A Healthcare Dataset for Complex Reasoning
    David Vilares, Carlos Gómez-Rodríguez
    http://arxiv.org/abs/1906.04701v1

    • [cs.CL]Identifying Visible Actions in Lifestyle Vlogs
    Oana Ignat, Laura Burdick, Jia Deng, Rada Mihalcea
    http://arxiv.org/abs/1906.04236v1

    • [cs.CL]Inter-sentence Relation Extraction with Document-level Graph Convolutional Neural Network
    Sunil Kumar Sahu, Fenia Christopoulou, Makoto Miwa, Sophia Ananiadou
    http://arxiv.org/abs/1906.04684v1

    • [cs.CL]Journal Name Extraction from Japanese Scientific News Articles
    Masato Kikuchi, Mitsuo Yoshida, Kyoji Umemura
    http://arxiv.org/abs/1906.04655v1

    • [cs.CL]Label-Agnostic Sequence Labeling by Copying Nearest Neighbors
    Sam Wiseman, Karl Stratos
    http://arxiv.org/abs/1906.04225v1

    • [cs.CL]Learning a Matching Model with Co-teaching for Multi-turn Response Selection in Retrieval-based Dialogue Systems
    Jiazhan Feng, Chongyang Tao, Wei Wu, Yansong Feng, Dongyan Zhao, Rui Yan
    http://arxiv.org/abs/1906.04413v1

    • [cs.CL]Lightweight and Efficient Neural Natural Language Processing with Quaternion Networks
    Yi Tay, Aston Zhang, Luu Anh Tuan, Jinfeng Rao, Shuai Zhang, Shuohang Wang, Jie Fu, Siu Cheung Hui
    http://arxiv.org/abs/1906.04393v1

    • [cs.CL]Modeling Sentiment Dependencies with Graph Convolutional Networks for Aspect-level Sentiment Classification
    Pinlong Zhaoa, Linlin Houb, Ou Wua
    http://arxiv.org/abs/1906.04501v1

    • [cs.CL]Parallel Scheduled Sampling
    Daniel Duckworth, Arvind Neelakantan, Ben Goodrich, Lukasz Kaiser, Samy Bengio
    http://arxiv.org/abs/1906.04331v1

    • [cs.CL]Psycholinguistics meets Continual Learning: Measuring Catastrophic Forgetting in Visual Question Answering
    Claudio Greco, Barbara Plank, Raquel Fernández, Raffaella Bernardi
    http://arxiv.org/abs/1906.04229v1

    • [cs.CL]Reinforcement Learning of Minimalist Numeral Grammars
    Peter beim Graben, Ronald Römer, Werner Meyer, Markus Huber, Matthias Wolff
    http://arxiv.org/abs/1906.04447v1

    • [cs.CL]Retrieve, Read, Rerank: Towards End-to-End Multi-Document Reading Comprehension
    Minghao Hu, Yuxing Peng, Zhen Huang, Dongsheng Li
    http://arxiv.org/abs/1906.04618v1

    • [cs.CL]Self-Supervised Learning for Contextualized Extractive Summarization
    Hong Wang, Xin Wang, Wenhan Xiong, Mo Yu, Xiaoxiao Guo, Shiyu Chang, William Yang Wang
    http://arxiv.org/abs/1906.04466v1

    • [cs.CL]Using Structured Representation and Data: A Hybrid Model for Negation and Sentiment in Customer Service Conversations
    Amita Misra, Mansurul Bhuiyan, Jalal Mahmud, Saurabh Tripathy
    http://arxiv.org/abs/1906.04706v1

    • [cs.CL]What Does BERT Look At? An Analysis of BERT’s Attention
    Kevin Clark, Urvashi Khandelwal, Omer Levy, Christopher D. Manning
    http://arxiv.org/abs/1906.04341v1

    • [cs.CL]What Kind of Language Is Hard to Language-Model?
    Sebastian J. Mielke, Ryan Cotterell, Kyle Gorman, Brian Roark, Jason Eisner
    http://arxiv.org/abs/1906.04726v1

    • [cs.CL]Word-level Speech Recognition with a Dynamic Lexicon
    Ronan Collobert, Awni Hannun, Gabriel Synnaeve
    http://arxiv.org/abs/1906.04323v1

    • [cs.CR]New dynamic and verifiable multi-secret sharing schemes based on LFSR public key cryptosystem
    Jing Yang, Fang-Wei Fu
    http://arxiv.org/abs/1906.04611v1

    • [cs.CV]3-D Surface Segmentation Meets Conditional Random Fields
    Leixin Zhou, Zisha Zhong, Abhay Shah, Xiaodong Wu
    http://arxiv.org/abs/1906.04714v1

    • [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/1906.03786v2

    • [cs.CV]Bag of Color Features For Color Constancy
    Firas Laakom, Nikolaos Passalis, Jenni Raitoharju, Jarno Nikkanen, Anastasios Tefas, Alexandros Iosifidis, Moncef Gabbouj
    http://arxiv.org/abs/1906.04445v1

    • [cs.CV]Band Attention Convolutional Networks For Hyperspectral Image Classification
    Hongwei Dong, Lamei Zhang, Bin Zou
    http://arxiv.org/abs/1906.04379v1

    • [cs.CV]CVPR19 Tracking and Detection Challenge: How crowded can it get?
    Patrick Dendorfer, Hamid Rezatofighi, Anton Milan, Javen Shi, Daniel Cremers, Ian Reid, Stefan Roth, Konrad Schindler, Laura Leal-Taixe
    http://arxiv.org/abs/1906.04567v1

    • [cs.CV]Challenges in Time-Stamp Aware Anomaly Detection in Traffic Videos
    Kuldeep Marotirao Biradar, Ayushi Gupta, Murari Mandal, Santosh Kumar Vipparthi
    http://arxiv.org/abs/1906.04574v1

    • [cs.CV]Clouds of Oriented Gradients for 3D Detection of Objects, Surfaces, and Indoor Scene Layouts
    Zhile Ren, Erik B. Sudderth
    http://arxiv.org/abs/1906.04725v1

    • [cs.CV]Cross-Modal Relationship Inference for Grounding Referring Expressions
    Sibei Yang, Guanbin Li, Yizhou Yu
    http://arxiv.org/abs/1906.04464v1

    • [cs.CV]E-LPIPS: Robust Perceptual Image Similarity via Random Transformation Ensembles
    Markus Kettunen, Erik Härkönen, Jaakko Lehtinen
    http://arxiv.org/abs/1906.03973v2

    • [cs.CV]End-to-End CAD Model Retrieval and 9DoF Alignment in 3D Scans
    Armen Avetisyan, Angela Dai, Matthias Nießner
    http://arxiv.org/abs/1906.04201v1

    • [cs.CV]FAMED-Net: A Fast and Accurate Multi-scale End-to-end Dehazing Network
    Jing Zhang, Dacheng Tao
    http://arxiv.org/abs/1906.04334v1

    • [cs.CV]FASTER Recurrent Networks for Video Classification
    Linchao Zhu, Laura Sevilla-Lara, Du Tran, Matt Feiszli, Yi Yang, Heng Wang
    http://arxiv.org/abs/1906.04226v1

    • [cs.CV]Few-Shot Point Cloud Region Annotation with Human in the Loop
    Siddhant Jain, Sowmya Munukutla, David Held
    http://arxiv.org/abs/1906.04409v1

    • [cs.CV]Gated CRF Loss for Weakly Supervised Semantic Image Segmentation
    Anton Obukhov, Stamatios Georgoulis, Dengxin Dai, Luc Van Gool
    http://arxiv.org/abs/1906.04651v1

    • [cs.CV]Hybrid Function Sparse Representation towards Image Super Resolution
    Junyi Bian, Baojun Lin, Ke Zhang
    http://arxiv.org/abs/1906.04363v1

    • [cs.CV]Joint Subspace Recovery and Enhanced Locality Driven Robust Flexible Discriminative Dictionary Learning
    Zhao Zhang, Jiahuan Ren, Weiming Jiang, Zheng Zhang, Richang Hong, Shuicheng Yan, Meng Wang
    http://arxiv.org/abs/1906.04598v1

    • [cs.CV]Large-scale Landmark Retrieval/Recognition under a Noisy and Diverse Dataset
    Kohei Ozaki, Shuhei Yokoo
    http://arxiv.org/abs/1906.04087v2

    • [cs.CV]Learning robust visual representations using data augmentation invariance
    Alex Hernández-García, Peter König, Tim C. Kietzmann
    http://arxiv.org/abs/1906.04547v1

    • [cs.CV]Mimic and Fool: A Task Agnostic Adversarial Attack
    Akshay Chaturvedi, Utpal Garain
    http://arxiv.org/abs/1906.04606v1

    • [cs.CV]NAS-FCOS: Fast Neural Architecture Search for Object Detection
    Ning Wang, Yang Gao, Hao Chen, Peng Wang, Zhi Tian, Chunhua Shen
    http://arxiv.org/abs/1906.04423v1

    • [cs.CV]Object-aware Aggregation with Bidirectional Temporal Graph for Video Captioning
    Junchao Zhang, Yuxin Peng
    http://arxiv.org/abs/1906.04375v1

    • [cs.CV]On Stabilizing Generative Adversarial Training with Noise
    Simon Jenni, Paolo Favaro
    http://arxiv.org/abs/1906.04612v1

    • [cs.CV]On the Vector Space in Photoplethysmography Imaging
    Christian S. Pilz, Vladimir Blazek, Steffen Leonhardt
    http://arxiv.org/abs/1906.04431v1

    • [cs.CV]Online Object Representations with Contrastive Learning
    Sören Pirk, Mohi Khansari, Yunfei Bai, Corey Lynch, Pierre Sermanet
    http://arxiv.org/abs/1906.04312v1

    • [cs.CV]PAN: Projective Adversarial Network for Medical Image Segmentation
    Naji Khosravan, Aliasghar Mortazi, Michael Wallace, Ulas Bagci
    http://arxiv.org/abs/1906.04378v1

    • [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/1906.04151v2

    • [cs.CV]Polysemous Visual-Semantic Embedding for Cross-Modal Retrieval
    Yale Song, Mohammad Soleymani
    http://arxiv.org/abs/1906.04402v1

    • [cs.CV]Recognizing License Plates in Real-Time
    Xuewen Yang, Xin Wang
    http://arxiv.org/abs/1906.04376v1

    • [cs.CV]Rethinking Person Re-Identification with Confidence
    George Adaimi, Sven Kreiss, Alexandre Alahi
    http://arxiv.org/abs/1906.04692v1

    • [cs.CV]Scale Invariant Fully Convolutional Network: Detecting Hands Efficiently
    Dan Liu, Dawei Du, Libo Zhang, Tiejian Luo, Yanjun Wu, Feiyue Huang, Siwei Lyu
    http://arxiv.org/abs/1906.04634v1

    • [cs.CV]Semantic-guided Encoder Feature Learning for Blurry Boundary Delineation
    Dong Nie, Dinggang Shen
    http://arxiv.org/abs/1906.04306v1

    • [cs.CV]Shapes and Context: In-the-Wild Image Synthesis & Manipulation
    Aayush Bansal, Yaser Sheikh, Deva Ramanan
    http://arxiv.org/abs/1906.04728v1

    • [cs.CV]Simultaneously Learning Architectures and Features of Deep Neural Networks
    Tinghuai Wang, Lixin Fan, Huiling Wang
    http://arxiv.org/abs/1906.04505v1

    • [cs.CV]Single Image Blind Deblurring Using Multi-Scale Latent Structure Prior
    Yuanchao Bai, Huizhu Jia, Ming Jiang, Xianming Liu, Xiaodong Xie, Wen Gao
    http://arxiv.org/abs/1906.04442v1

    • [cs.CV]Subspace Attack: Exploiting Promising Subspaces for Query-Efficient Black-box Attacks
    Ziang Yan, Yiwen Guo, Changshui Zhang
    http://arxiv.org/abs/1906.04392v1

    • [cs.CV]SymNet: Symmetrical Filters in Convolutional Neural Networks
    Gregory Dzhezyan, Hubert Cecotti
    http://arxiv.org/abs/1906.04252v1

    • [cs.CV]TW-SMNet: Deep Multitask Learning of Tele-Wide Stereo Matching
    Mostafa El-Khamy, Haoyu Ren, Xianzhi Du, Jungwon Lee
    http://arxiv.org/abs/1906.04463v1

    • [cs.CV]iProStruct2D: Identifying protein structural classes by deep learning via 2D representations
    Loris Nanni, Alessandra Lumini, Federica Pasquali, Sheryl Brahnam
    http://arxiv.org/abs/1906.04407v1

    • [cs.CY]Creation of User Friendly Datasets: Insights from a Case Study concerning Explanations of Loan Denials
    Ajay Chander, Ramya Srinivasan
    http://arxiv.org/abs/1906.04643v1

    • [cs.DC]Anomaly Detection in High Performance Computers: A Vicinity Perspective
    Siavash Ghiasvand, Florina M. Ciorba
    http://arxiv.org/abs/1906.04550v1

    • [cs.DC]Membership-based Manoeuvre Negotiation in Autonomous and Safety-critical Vehicular Systems
    Antonio Casimiro, Emelie Ekenstedt, Elad Michael Schiller
    http://arxiv.org/abs/1906.04703v1

    • [cs.DC]Window Based BFT Blockchain Consensus
    Mohammad M. Jalalzai, Costas Busch
    http://arxiv.org/abs/1906.04381v1

    • [cs.DL]EXmatcher: Combining Features Based on Reference Strings and Segments to Enhance Citation Matching
    Behnam Ghavimi, Wolfgang Otto, Philipp Mayr
    http://arxiv.org/abs/1906.04484v1

    • [cs.GR]Differentiable Surface Splatting for Point-based Geometry Processing
    Wang Yifan, Felice Serena, Shihao Wu, Cengiz Öztireli, Olga Sorkine-Hornung
    http://arxiv.org/abs/1906.04173v1

    • [cs.HC]Human-Machine Collaboration for Fast Land Cover Mapping
    Caleb Robinson, Anthony Ortiz, Kolya Malkin, Blake Elias, Andi Peng, Dan Morris, Bistra Dilkina, Nebojsa Jojic
    http://arxiv.org/abs/1906.04176v1

    • [cs.IR]Evaluation of Seed Set Selection Approaches and Active Learning Strategies in Predictive Coding
    Christian J. Mahoney, Nathaniel Huber-Fliflet, Haozhen Zhao, Jianping Zhang, Peter Gronvall, Shi Ye
    http://arxiv.org/abs/1906.04367v1

    • [cs.IR]Modeling the Past and Future Contexts for Session-based Recommendation
    Yuan Fajie, He Xiangnan, Guo Guibing, Xu Zhezhao, Xiong Jian, He Xiuqiang
    http://arxiv.org/abs/1906.04473v1

    • [cs.IR]The snippets taxonomy in web search engines
    Artur Strzelecki, Paulina Rutecka
    http://arxiv.org/abs/1906.04497v1

    • [cs.IT]Artificial Noisy MIMO Systems under Correlated Scattering Rayleigh Fading — A Physical Layer Security Approach
    Yiliang Liu, Hsiao-Hwa Chen, Liangmin Wang, Weixiao Meng
    http://arxiv.org/abs/1906.04289v1

    • [cs.IT]Beamforming Optimization for Wireless Network Aided by Intelligent Reflecting Surface with Discrete Phase Shifts
    Qingqing Wu, Rui Zhang
    http://arxiv.org/abs/1906.03165v2

    • [cs.IT]Dual-Band Fading Multiple Access Relay Channels
    Subhajit Majhi, Patrick Mitran
    http://arxiv.org/abs/1906.04609v1

    • [cs.IT]Orthogonal Cocktail BPSK: Exceeding Shannon Capacity of QPSK Input
    Bingli Jiao
    http://arxiv.org/abs/1906.04440v1

    • [cs.IT]Rate-Splitting Unifying SDMA, OMA, NOMA, and Multicasting in MISO Broadcast Channel: A Simple Two-User Rate Analysis
    Bruno Clerckx, Yijie Mao, Robert Schober, H. Vincent Poor
    http://arxiv.org/abs/1906.04474v1

    • [cs.IT]Reinforcement Learning for Channel Coding: Learned Bit-Flipping Decoding
    Fabrizio Carpi, Christian Häger, Marco Martalò, Riccardo Raheli, Henry D. Pfister
    http://arxiv.org/abs/1906.04448v1

    • [cs.IT]Stability and Metastability of Traffic Dynamics in Uplink Random Access Networks
    Ahmad AlAmmouri, Jeffrey G. Andrews, Francois Baccelli
    http://arxiv.org/abs/1906.04683v1

    • [cs.LG]A Taxonomy of Channel Pruning Signals in CNNs
    Kaveena Persand, Andrew Anderson, David Gregg
    http://arxiv.org/abs/1906.04675v1

    • [cs.LG]A refined primal-dual analysis of the implicit bias
    Ziwei Ji, Matus Telgarsky
    http://arxiv.org/abs/1906.04540v1

    • [cs.LG]Adaptively Preconditioned Stochastic Gradient Langevin Dynamics
    Chandrasekaran Anirudh Bhardwaj
    http://arxiv.org/abs/1906.04324v1

    • [cs.LG]An Improved Analysis of Training Over-parameterized Deep Neural Networks
    Difan Zou, Quanquan Gu
    http://arxiv.org/abs/1906.04688v1

    • [cs.LG]Analysis Of Momentum Methods
    Nikola B. Kovachki, Andrew M. Stuart
    http://arxiv.org/abs/1906.04285v1

    • [cs.LG]Associative Convolutional Layers
    Hamed Omidvar, Vahideh Akhlaghi, Massimo Franceschetti, Rajesh K. Gupta
    http://arxiv.org/abs/1906.04309v1

    • [cs.LG]BasisConv: A method for compressed representation and learning in CNNs
    Muhammad Tayyab, Abhijit Mahalanobis
    http://arxiv.org/abs/1906.04509v1

    • [cs.LG]Building High-Quality Auction Fraud Dataset
    Sulaf Elshaar, Samira Sadaoui
    http://arxiv.org/abs/1906.04272v1

    • [cs.LG]Causal Discovery with Reinforcement Learning
    Shengyu Zhu, Zhitang Chen
    http://arxiv.org/abs/1906.04477v1

    • [cs.LG]Communication and Memory Efficient Testing of Discrete Distributions
    Ilias Diakonikolas, Themis Gouleakis, Daniel M. Kane, Sankeerth Rao
    http://arxiv.org/abs/1906.04709v1

    • [cs.LG]Continual Reinforcement Learning deployed in Real-life using Policy Distillation and Sim2Real Transfer
    René Traoré, Hugo Caselles-Dupré, Timothée Lesort, Te Sun, Natalia Díaz-Rodríguez, David Filliat
    http://arxiv.org/abs/1906.04452v1

    • [cs.LG]Coupled Variational Recurrent Collaborative Filtering
    Qingquan Song, Shiyu Chang, Xia Hu
    http://arxiv.org/abs/1906.04386v1

    • [cs.LG]Data-Free Quantization through Weight Equalization and Bias Correction
    Markus Nagel, Mart van Baalen, Tijmen Blankevoort, Max Welling
    http://arxiv.org/abs/1906.04721v1

    • [cs.LG]DropConnect Is Effective in Modeling Uncertainty of Bayesian Deep Networks
    Aryan Mobiny, Hien V. Nguyen, Supratik Moulik, Naveen Garg, Carol C. Wu
    http://arxiv.org/abs/1906.04569v1

    • [cs.LG]Dynamical Anatomy of NARMA10 Benchmark Task
    Tomoyuki Kubota, Kohei Nakajima, Hirokazu Takahashi
    http://arxiv.org/abs/1906.04608v1

    • [cs.LG]Efficient Kernel-based Subsequence Search for User Identification from Walking Activity
    Candelieri Antonio, Fedorov Stanislav, Messina Vincenzina
    http://arxiv.org/abs/1906.04680v1

    • [cs.LG]Evaluating aleatoric and epistemic uncertainties of time series deep learning models for soil moisture predictions
    Kuai Fang, Chaopeng Shen, Daniel Kifer
    http://arxiv.org/abs/1906.04595v1

    • [cs.LG]Evolutionary Trigger Set Generation for DNN Black-Box Watermarking
    Jia Guo, Miodrag Potkonjak
    http://arxiv.org/abs/1906.04411v1

    • [cs.LG]Exploration via Hindsight Goal Generation
    Zhizhou Ren, Kefan Dong, Yuan Zhou, Qiang Liu, Jian Peng
    http://arxiv.org/abs/1906.04279v1

    • [cs.LG]Extracting Interpretable Concept-Based Decision Trees from CNNs
    Conner Chyung, Michael Tsang, Yan Liu
    http://arxiv.org/abs/1906.04664v1

    • [cs.LG]Fast Rates for a kNN Classifier Robust to Unknown Asymmetric Label Noise
    Henry W. J. Reeve, Ata Kaban
    http://arxiv.org/abs/1906.04542v1

    • [cs.LG]Fast and Accurate Least-Mean-Squares Solvers
    Alaa Maalouf, Ibrahim Jubran, Dan Feldman
    http://arxiv.org/abs/1906.04705v1

    • [cs.LG]Faster Algorithms for High-Dimensional Robust Covariance Estimation
    Yu Cheng, Ilias Diakonikolas, Rong Ge, David Woodruff
    http://arxiv.org/abs/1906.04661v1

    • [cs.LG]Gossip-based Actor-Learner Architectures for Deep Reinforcement Learning
    Mahmoud Assran, Joshua Romoff, Nicolas Ballas, Joelle Pineau, Mike Rabbat
    http://arxiv.org/abs/1906.04585v1

    • [cs.LG]Graph Convolutional Transformer: Learning the Graphical Structure of Electronic Health Records
    Edward Choi, Zhen Xu, Yujia Li, Michael W. Dusenberry, Gerardo Flores, Yuan Xue, Andrew M. Dai
    http://arxiv.org/abs/1906.04716v1

    • [cs.LG]Importance Resampling for Off-policy Prediction
    Matthew Schlegel, Wesley Chung, Daniel Graves, Jian Qian, Martha White
    http://arxiv.org/abs/1906.04328v1

    • [cs.LG]Large Scale Structure of Neural Network Loss Landscapes
    Stanislav Fort, Stanislaw Jastrzebski
    http://arxiv.org/abs/1906.04724v1

    • [cs.LG]Learning Powerful Policies by Using Consistent Dynamics Model
    Shagun Sodhani, Anirudh Goyal, Tristan Deleu, Yoshua Bengio, Sergey Levine, Jian Tang
    http://arxiv.org/abs/1906.04355v1

    • [cs.LG]Learning Selection Masks for Deep Neural Networks
    Stefan Oehmcke, Fabian Gieseke
    http://arxiv.org/abs/1906.04673v1

    • [cs.LG]Meta-Learning Neural Bloom Filters
    Jack W Rae, Sergey Bartunov, Timothy P Lillicrap
    http://arxiv.org/abs/1906.04304v1

    • [cs.LG]Metrics for Learning in Topological Persistence
    Henri Riihimäki, José Licón-Saláiz
    http://arxiv.org/abs/1906.04436v1

    • [cs.LG]On Single Source Robustness in Deep Fusion Models
    Taewan Kim, Joydeep Ghosh
    http://arxiv.org/abs/1906.04691v1

    • [cs.LG]Optimizing Pipelined Computation and Communication for Latency-Constrained Edge Learning
    Nicolas Skatchkovsky, Osvaldo Simeone
    http://arxiv.org/abs/1906.04488v1

    • [cs.LG]Performance Analysis and Characterization of Training Deep Learning Models on NVIDIA TX2
    Jie Liu, Jiawen Liu, Wan Du, Dong Li
    http://arxiv.org/abs/1906.04278v1

    • [cs.LG]Quantifying Intrinsic Uncertainty in Classification via Deep Dirichlet Mixture Networks
    Qingyang Wu, He Li, Weijie Su, Lexin Li, Zhou Yu
    http://arxiv.org/abs/1906.04450v1

    • [cs.LG]Representation Learning-Assisted Click-Through Rate Prediction
    Wentao Ouyang, Xiuwu Zhang, Shukui Ren, Chao Qi, Zhaojie Liu, Yanlong Du
    http://arxiv.org/abs/1906.04365v1

    • [cs.LG]Scaling Laws for the Principled Design, Initialization and Preconditioning of ReLU Networks
    Aaron Defazio, Léon Bottou
    http://arxiv.org/abs/1906.04267v1

    • [cs.LG]Stochastic Neural Network with Kronecker Flow
    Chin-Wei Huang, Ahmed Touati, Pascal Vincent, Gintare Karolina Dziugaite, Alexandre Lacoste, Aaron Courville
    http://arxiv.org/abs/1906.04282v1

    • [cs.LG]Topology Attack and Defense for Graph Neural Networks: An Optimization Perspective
    Kaidi Xu, Hongge Chen, Sijia Liu, Pin-Yu Chen, Tsui-Wei Weng, Mingyi Hong, Xue Lin
    http://arxiv.org/abs/1906.04214v1

    • [cs.LG]Towards Amortized Ranking-Critical Training for Collaborative Filtering
    Sam Lobel, Chunyuan Li, Jianfeng Gao, Lawrence Carin
    http://arxiv.org/abs/1906.04281v1

    • [cs.LG]Transfer Learning for Ultrasound Tongue Contour Extraction with Different Domains
    M. Hamed Mozaffari, Won-Sook Lee
    http://arxiv.org/abs/1906.04301v1

    • [cs.LG]Ultra Fast Medoid Identification via Correlated Sequential Halving
    Tavor Z. Baharav, David N. Tse
    http://arxiv.org/abs/1906.04356v1

    • [cs.LG]Variance-reduced $Q$-learning is minimax optimal
    Martin J. Wainwright
    http://arxiv.org/abs/1906.04697v1

    • [cs.LG]Wasserstein Reinforcement Learning
    Aldo Pacchiano, Jack Parker-Holder, Yunhao Tang, Anna Choromanska, Krzysztof Choromanski, Michael Jordan
    http://arxiv.org/abs/1906.04349v1

    • [cs.LG]Weight Agnostic Neural Networks
    Adam Gaier, David Ha
    http://arxiv.org/abs/1906.04358v1

    • [cs.LG]WikiDataSets : Standardized sub-graphs from WikiData
    Armand Boschin
    http://arxiv.org/abs/1906.04536v1

    • [cs.LG]k-Nearest Neighbor Optimization via Randomized Hyperstructure Convex Hull
    Jasper Kyle Catapang
    http://arxiv.org/abs/1906.04559v1

    • [cs.NE]Classification of EEG Signals using Genetic Programming for Feature Construction
    Icaro Marcelino Miranda, Claus Aranha, Marcelo Ladeira
    http://arxiv.org/abs/1906.04403v1

    • [cs.NE]Unsupervised Minimax: Adversarial Curiosity, Generative Adversarial Networks, and Predictability Minimization
    Juergen Schmidhuber
    http://arxiv.org/abs/1906.04493v1

    • [cs.PF]ROOT I/O compression algorithms and their performance impact within Run 3
    Oksana Shadura, Brian Paul Bockelman
    http://arxiv.org/abs/1906.04624v1

    • [cs.PL]Write, Execute, Assess: Program Synthesis with a REPL
    Kevin Ellis, Maxwell Nye, Yewen Pu, Felix Sosa, Josh Tenenbaum, Armando Solar-Lezama
    http://arxiv.org/abs/1906.04604v1

    • [cs.RO]Automatic Multi-Sensor Extrinsic Calibration for Mobile Robots
    David Zuñiga-Noël, Jose-Raul Ruiz-Sarmiento, Ruben Gomez-Ojeda, Javier Gonzalez-Jimenez
    http://arxiv.org/abs/1906.04670v1

    • [cs.RO]Bilevel Optimization for Planning through Contact: A Semidirect Method
    Benoit Landry, Joseph Lorenzetti, Zachary Manchester, Marco Pavone
    http://arxiv.org/abs/1906.04292v1

    • [cs.RO]Design and integration of a parallel, soft robotic end-effector for extracorporeal ultrasound
    Lukas Lindenroth, Richard James Housden, Shuangyi Wang, Junghwan Back, Kawal Rhode, Hongbin Liu
    http://arxiv.org/abs/1906.04526v1

    • [cs.RO]Gait modeling and optimization for the perturbed Stokes regime
    Matthew D. Kvalheim, Brian Bittner, Shai Revzen
    http://arxiv.org/abs/1906.04384v1

    • [cs.RO]Visual-Inertial Odometry of Aerial Robots
    Davide Scaramuzza, Zichao Zhang
    http://arxiv.org/abs/1906.03289v1

    • [cs.SI]Boosting Students’ Performance With The Aid Of Social Network Analysis
    R. U. Gobithaasan, Nurul Syaheera Din, Lingeswaran Ramachandra, Roslan Hasni
    http://arxiv.org/abs/1906.04352v1

    • [cs.SI]Control contribution identifies top driver nodes in complex networks
    Yan Zhang, Antonios Garas, Frank Schweitzer
    http://arxiv.org/abs/1906.04663v1

    • [cs.SI]Corrected overlap weight and clustering coefficient
    Vladimir Batagelj
    http://arxiv.org/abs/1906.04581v1

    • [cs.SI]Examining Untempered Social Media: Analyzing Cascades of Polarized Conversations
    Arunkumar Bagavathi, Pedram Bashiri, Shannon Reid, Matthew Phillips, Siddharth Krishnan
    http://arxiv.org/abs/1906.04261v1

    • [cs.SI]Fine-grained Event Categorization with Heterogeneous Graph Convolutional Networks
    Hao Peng, Jianxin Li, Qiran Gong, Yangqiu Song, Yuanxing Ning, Kunfeng Lai, Philip S. Yu
    http://arxiv.org/abs/1906.04580v1

    • [cs.SI]Heterogeneous network approach to predict individuals’ mental health
    Shikang Liu, Fatemeh Vahedian, David Hachen, Omar Lizardo, Christian Poellabauer, Aaron Striegel, Tijana Milenkovic
    http://arxiv.org/abs/1906.04346v1

    • [cs.SI]Latent Channel Networks
    Clifford Anderson-Bergman
    http://arxiv.org/abs/1906.04563v1

    • [cs.SI]Network-based Fake News Detection: A Pattern-driven Approach
    Xinyi Zhou, Reza Zafarani
    http://arxiv.org/abs/1906.04210v1

    • [cs.SI]StRE: Self Attentive Edit Quality Prediction in Wikipedia
    Soumya Sarkar, Bhanu Prakash Reddy, Sandipan Sikdar, Animesh Mukherjee
    http://arxiv.org/abs/1906.04678v1

    • [econ.EM]Bias-Aware Inference in Fuzzy Regression Discontinuity Designs
    Claudia Noack, Christoph Rothe
    http://arxiv.org/abs/1906.04631v1

    • [econ.EM]The Regression Discontinuity Design
    Matias D. Cattaneo, Rocio Titiunik, Gonzalo Vazquez-Bare
    http://arxiv.org/abs/1906.04242v1

    • [econ.GN]ProPublica’s COMPAS Data Revisited
    Matias Barenstein
    http://arxiv.org/abs/1906.04711v1

    • [eess.AS]Using generative modelling to produce varied intonation for speech synthesis
    Zack Hodari, Oliver Watts, Simon King
    http://arxiv.org/abs/1906.04233v1

    • [eess.IV]A Hybrid Approach Between Adversarial Generative Networks and Actor-Critic Policy Gradient for Low Rate High-Resolution Image Compression
    Nicoló Savioli
    http://arxiv.org/abs/1906.04681v1

    • [eess.IV]A Novel Cost Function for Despeckling using Convolutional Neural Networks
    Giampaolo Ferraioli, Vito Pascazio, Sergio Vitale
    http://arxiv.org/abs/1906.04441v1

    • [eess.IV]Alzheimer’s Disease Brain MRI Classification: Challenges and Insights
    Yi Ren Fung, Ziqiang Guan, Ritesh Kumar, Joie Yeahuay Wu, Madalina Fiterau
    http://arxiv.org/abs/1906.04231v1

    • [eess.IV]Automatic brain tissue segmentation in fetal MRI using convolutional neural networks
    N. Khalili, N. Lessmann, E. Turk, N. Claessens, R. de Heus, T. Kolk, M. A. Viergever, M. J. N. L. Benders, I. Isgum
    http://arxiv.org/abs/1906.04713v1

    • [eess.IV]BowNet: Dilated Convolution Neural Network for Ultrasound Tongue Contour Extraction
    M. Hamed Mozaffari, Won-Sook Lee
    http://arxiv.org/abs/1906.04232v1

    • [eess.IV]Deep learning analysis of cardiac CT angiography for detection of coronary arteries with functionally significant stenosis
    Majd Zreik, Robbert W. van Hamersvelt, Nadieh Khalili, Jelmer M. Wolterink, Michiel Voskuil, Max A. Viergever, Tim Leiner, Ivana Išgum
    http://arxiv.org/abs/1906.04419v1

    • [eess.IV]DeepcomplexMRI: Exploiting deep residual network for fast parallel MR imaging with complex convolution
    Shanshan Wang, Huitao Cheng, Leslie Ying, Taohui Xiao, Ziwen Ke, Xin Liu, Hairong Zheng, Dong Liang
    http://arxiv.org/abs/1906.04359v1

    • [eess.IV]Generative adversarial network for segmentation of motion affected neonatal brain MRI
    N. Khalili, E. Turk, M. Zreik, M. A. Viergever, M. J. N. L. Benders, I. Isgum
    http://arxiv.org/abs/1906.04704v1

    • [eess.IV]Multiscale Nakagami parametric imaging for improved liver tumor localization
    Omar S. Al-Kadi
    http://arxiv.org/abs/1906.04333v1

    • [eess.IV]`Project & Excite’ Modules for Segmentation of Volumetric Medical Scans
    Anne-Marie Rickmann, Abhijit Guha Roy, Ignacio Sarasua, Nassir Navab, Christian Wachinger
    http://arxiv.org/abs/1906.04649v1

    • [eess.SP]Adaptive Neural Signal Detection for Massive MIMO
    Mehrdad Khani, Mohammad Alizadeh, Jakob Hoydis, Phil Fleming
    http://arxiv.org/abs/1906.04610v1

    • [eess.SP]Beam Learning — Using Machine Learning for Finding Beam Directions
    Saidhiraj Amuru
    http://arxiv.org/abs/1906.04368v1

    • [eess.SP]Transport Triggered Array Processor for Vision Applications
    Mehdi Safarpour, Ilkka Hautala, Miguel Bordallo Lopez, Olli Silven
    http://arxiv.org/abs/1906.04258v1

    • [eess.SY]Sequential Source Coding for Stochastic Systems Subject to Finite Rate Constraints
    Photios A. Stavrou, Mikael Skoglund, Takashi Tanaka
    http://arxiv.org/abs/1906.04217v1

    • [math.CO]Schur ring and Codes for $S$-subgroups over $\Z_{2}^{n}$
    Ronald Orozco López
    http://arxiv.org/abs/1906.04250v1

    • [math.OC]Hybrid Nonlinear Observers for Inertial Navigation Using Landmark Measurements
    Miaomiao Wang, Abdelhamid Tayebi
    http://arxiv.org/abs/1906.04689v1

    • [math.ST]Convergence of Dümbgen’s Algorithm for Estimation of Tail Inflation
    Jasha Sommer-Simpson
    http://arxiv.org/abs/1906.04544v1

    • [math.ST]Detection and estimation of parameters in high dimensional multiple change point regression models via $\ell_1/\ell_0$ regularization and discrete optimization
    Abhishek Kaul, Venkata K Jandhyala, Stergios B Fotopoulos
    http://arxiv.org/abs/1906.04396v1

    • [math.ST]Mean estimation and regression under heavy-tailed distributions—a survey
    Gabor Lugosi, Shahar Mendelson
    http://arxiv.org/abs/1906.04280v1

    • [math.ST]Monte Carlo and Quasi-Monte Carlo Density Estimation via Conditioning
    Pierre L’Ecuyer, Florian Puchhammer, Amal Ben Abdellah
    http://arxiv.org/abs/1906.04607v1

    • [math.ST]Selection consistency of Lasso-based procedures for misspecified high-dimensional binary model and random regressors
    Mariusz Kubkowski, Jan Mielniczuk
    http://arxiv.org/abs/1906.04175v1

    • [physics.soc-ph]Achieving competitive advantage in academia through early career coauthorship with top scientists
    Weihua Li, Tomaso Aste, Fabio Caccioli, Giacomo Livan
    http://arxiv.org/abs/1906.04619v1

    • [physics.soc-ph]Analysis of the susceptible-infected-susceptible epidemic dynamics in networks via the non-backtracking matrix
    Naoki Masuda, Masaki Ogura, Victor M. Preciado
    http://arxiv.org/abs/1906.04269v1

    • [physics.soc-ph]Intertemporal Community Detection in Bikeshare Networks
    Mark He, Joseph Glasser, Shankar Bhamidi, Nikhil Kaza
    http://arxiv.org/abs/1906.04582v1

    • [q-fin.ST]Likelihood Evaluation of Jump-Diffusion Models Using Deterministic Nonlinear Filters
    Jean-François Bégin, Mathieu Boudreault
    http://arxiv.org/abs/1906.04322v1

    • [quant-ph]Quantum Random Numbers generated by the Cloud Superconducting Quantum Computer
    Kentaro Tamura, Yutaka Shikano
    http://arxiv.org/abs/1906.04410v1

    • [stat.AP]Assessing the effects of exposure to sulfuric acid aerosol on respiratory function in adults
    Lamin Juwara, Jennifer Boateng
    http://arxiv.org/abs/1906.04296v1

    • [stat.AP]Causal Inference in Higher Education: Building Better Curriculums
    Prableen Kaur, Agoritsa Polyzou, George Karypis
    http://arxiv.org/abs/1906.04698v1

    • [stat.AP]Characterization and valuation of uncertainty of calibrated parameters in stochastic decision models
    Fernando Alarid-Escudero, Amy B. Knudsen, Jonathan Ozik, Nicholson Collier, Karen M. Kuntz
    http://arxiv.org/abs/1906.04668v1

    • [stat.AP]Regional economic convergence and spatial quantile regression
    Alfredo Cartone, Geoffrey JD Hewings, Paolo Postiglione
    http://arxiv.org/abs/1906.04613v1

    • [stat.AP]Statistical Species Identification
    Måns Karlsson, Ola Hössjer
    http://arxiv.org/abs/1906.04538v1

    • [stat.AP]Technical Preprint: Rationale and Design of a Planned Observational Study to Evaluate the Impact of Hydrocodone Rescheduling on Opioid Prescribing After Surgery
    Mark D. Neuman, Sean Hennessy, Dylan Small, Colleen Brensinger, Craig Newcomb, Lakisha Gaskins, Duminda Wijeysundera, Brian T. Bateman, Hannah Wunsch
    http://arxiv.org/abs/1906.04246v1

    • [stat.CO]Likelihood-free approximate Gibbs sampling
    G. S. Rodrigues, D. J. Nott, S. A. Sisson
    http://arxiv.org/abs/1906.04347v1

    • [stat.ME]Adaptative significance levels in linear regression models with known variance
    Alejandra Estefanía Patiño Hoyos, Victor Fossaluza
    http://arxiv.org/abs/1906.04222v1

    • [stat.ME]An approximate Bayesian approach to regression estimation with many auxiliary variables
    Shonosuke Sugasawa, Jae Kwang Kim
    http://arxiv.org/abs/1906.04398v1

    • [stat.ML]Approximate Variational Inference Based on a Finite Sample of Gaussian Latent Variables
    Nikolaos Gianniotis, Christoph Schnörr, Christian Molkenthin, Sanjay Singh Bora
    http://arxiv.org/abs/1906.04507v1

    • [stat.ML]Asymptotic Guarantees for Learning Generative Models with the Sliced-Wasserstein Distance
    Kimia Nadjahi, Alain Durmus, Umut Şimşekli, Roland Badeau
    http://arxiv.org/abs/1906.04516v1

    • [stat.ML]Efficient structure learning with automatic sparsity selection for causal graph processes
    Théophile Griveau-Billion, Ben Calderhead
    http://arxiv.org/abs/1906.04479v1

    • [stat.ML]Maximum Mean Discrepancy Gradient Flow
    Michael Arbel, Anna Korba, Adil Salim, Arthur Gretton
    http://arxiv.org/abs/1906.04370v1

    • [stat.ML]Principled Training of Neural Networks with Direct Feedback Alignment
    Julien Launay, Iacopo Poli, Florent Krzakala
    http://arxiv.org/abs/1906.04554v1

    • [stat.ML]Probabilistic Forecasting with Temporal Convolutional Neural Network
    Yitian Chen, Yanfei Kang, Yixiong Chen, Zizhuo Wang
    http://arxiv.org/abs/1906.04397v1

    • [stat.ML]SALT: Subspace Alignment as an Auxiliary Learning Task for Domain Adaptation
    Kowshik Thopalli, Jayaraman J. Thiagarajan, Rushil Anirudh, Pavan Turaga
    http://arxiv.org/abs/1906.04338v1

    • [stat.ML]Stable Rank Normalization for Improved Generalization in Neural Networks and GANs
    Amartya Sanyal, Philip H. S. Torr, Puneet K. Dokania
    http://arxiv.org/abs/1906.04659v1