astro-ph.IM - 仪器仪表和天体物理学方法

    cond-mat.dis-nn - 无序系统与神经网络 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.DM - 离散数学 cs.DS - 数据结构与算法 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.RO - 机器人学 cs.SD - 声音处理 cs.SI - 社交网络与信息网络 eess.AS - 语音处理 eess.IV - 图像与视频处理 eess.SP - 信号处理 math.DS - 动力系统 math.NT - 数论 math.OC - 优化与控制 math.PR - 概率 math.ST - 统计理论 physics.data-an - 数据分析、 统计和概率 physics.soc-ph - 物理学与社会 q-bio.BM - 生物分子 q-bio.QM - 定量方法 q-fin.CP -计算金融学 q-fin.PM - 投资组合管理 q-fin.TR - 贸易与市场微观结构 stat.AP - 应用统计 stat.CO - 统计计算 stat.ME - 统计方法论 stat.ML - (统计)机器学习

    • [astro-ph.IM]Algorithms and Statistical Models for Scientific Discovery in the Petabyte Era
    • [astro-ph.IM]An Information Theory Approach on Deciding Spectroscopic Follow Ups
    • [cond-mat.dis-nn]Statistical physics of unsupervised learning with prior knowledge in neural networks
    • [cs.AI]A Deep Reinforcement Learning based Approach to Learning Transferable Proof Guidance Strategies
    • [cs.AI]A Latent Feelings-aware RNN Model for User Churn Prediction with Behavioral Data
    • [cs.AI]A Spoken Dialogue System for Spatial Question Answering in a Physical Blocks World
    • [cs.AI]CoKE: Contextualized Knowledge Graph Embedding
    • [cs.AI]Heuristics for Interpretable Knowledge Graph Contextualization
    • [cs.AI]Robot navigation and target capturing using nature-inspired approaches in a dynamic environment
    • [cs.AI]Wearable Affective Life-Log System for Understanding Emotion Dynamics in Daily Life
    • [cs.CL]Enriching Conversation Context in Retrieval-based Chatbots
    • [cs.CL]Guiding Non-Autoregressive Neural Machine Translation Decoding with Reordering Information
    • [cs.CL]Guiding Variational Response Generator to Exploit Persona
    • [cs.CL]Hierarchical Contextualized Representation for Named Entity Recognition
    • [cs.CL]Infusing Knowledge into the Textual Entailment Task Using Graph Convolutional Networks
    • [cs.CL]Learning to Answer by Learning to Ask: Getting the Best of GPT-2 and BERT Worlds
    • [cs.CL]Multi-Paragraph Reasoning with Knowledge-enhanced Graph Neural Network
    • [cs.CL]Optimizing the Factual Correctness of a Summary: A Study of Summarizing Radiology Reports
    • [cs.CL]SentiLR: Linguistic Knowledge Enhanced Language Representation for Sentiment Analysis
    • [cs.CL]Seq2Emo for Multi-label Emotion Classification Based on Latent Variable Chains Transformation
    • [cs.CL]Toward Dimensional Emotion Detection from Categorical Emotion Annotations
    • [cs.CL]Unsupervised Cross-lingual Representation Learning at Scale
    • [cs.CL]Unsupervised Multi-Document Opinion Summarization as Copycat-Review Generation
    • [cs.CR]A Survey of Blockchain Applications in Different Domains
    • [cs.CR]Data Poisoning Attacks to Local Differential Privacy Protocols
    • [cs.CR]Intriguing Properties of Adversarial ML Attacks in the Problem Space
    • [cs.CR]Privacy Preserving Threat Hunting in Smart Home Environments
    • [cs.CR]The Naked Sun: Malicious Cooperation Between Benign-Looking Processes
    • [cs.CV]AIM 2019 Challenge on Image Demoireing: Dataset and Study
    • [cs.CV]Architectural Tricks for Deep Learning in Remote Photoplethysmography
    • [cs.CV]Boosting Object Recognition in Point Clouds by Saliency Detection
    • [cs.CV]Contextual Grounding of Natural Language Entities in Images
    • [cs.CV]Federated Adversarial Domain Adaptation
    • [cs.CV]Interpretable Self-Attention Temporal Reasoning for Driving Behavior Understanding
    • [cs.CV]Localization-aware Channel Pruning for Object Detection
    • [cs.CV]Melanoma detection with electrical impedance spectroscopy and dermoscopy using joint deep learning models
    • [cs.CV]Predicting Long-Term Skeletal Motions by a Spatio-Temporal Hierarchical Recurrent Network
    • [cs.CV]Predictive modeling of brain tumor: A Deep learning approach
    • [cs.CV]Recurrent Instance Segmentation using Sequences of Referring Expressions
    • [cs.CV]SCL: Towards Accurate Domain Adaptive Object Detection via Gradient Detach Based Stacked Complementary Losses
    • [cs.CV]SRINet: Learning Strictly Rotation-Invariant Representations for Point Cloud Classification and Segmentation
    • [cs.CV]Satellite Pose Estimation Challenge: Dataset, Competition Design and Results
    • [cs.CV]Spatial Feature Extraction in Airborne Hyperspectral Images Using Local Spectral Similarity
    • [cs.CV]Towards Large yet Imperceptible Adversarial Image Perturbations with Perceptual Color Distance
    • [cs.CV]Uninformed Students: Student-Teacher Anomaly Detection with Discriminative Latent Embeddings
    • [cs.CV]Where is the Fake? Patch-Wise Supervised GANs for Texture Inpainting
    • [cs.CY]Adaptively selecting occupations to detect skill shortages from online job ads
    • [cs.CY]Investigating Ortega Hypothesis in Q&A portals: An Analysis of StackOverflow
    • [cs.DC]A Language-based Serverless Function Accelerator
    • [cs.DC]Asynchronous Online Federated Learning for Edge Devices
    • [cs.DC]DeLottery: A Novel Decentralized Lottery System Based on Blockchain Technology
    • [cs.DC]Developing a Process in Architecting Microservice Infrastructure with Docker, Kubernetes, and Istio
    • [cs.DC]Failure Analysis and Quantification for Contemporary and Future Supercomputers
    • [cs.DC]KLARAPTOR: A Tool for Dynamically Finding Optimal Kernel Launch Parameters Targeting CUDA Programs
    • [cs.DC]Performance Evaluation of VDI Environment
    • [cs.DC]Soft Error Resilience and Failure Recovery for Continuum Dynamics Applications
    • [cs.DC]uqSim: Scalable and Validated Simulation of Cloud Microservices
    • [cs.DM]Optimal non-adaptive group testing
    • [cs.DS]Efficiently Learning Structured Distributions from Untrusted Batches
    • [cs.GT]Liability Design for Autonomous Vehicles and Human-Driven Vehicles: A Hierarchical Game-Theoretic Approach
    • [cs.GT]Multi-Item Mechanisms without Item-Independence: Learnability via Robustness
    • [cs.HC]An Affective Situation Labeling System from Psychological Behaviors in Emotion Recognition
    • [cs.IR]MBCAL: A Simple and Efficient Reinforcement Learning Method for Recommendation Systems
    • [cs.IT]Active Status Update Packet Drop Control in an Energy Harvesting Node
    • [cs.IT]Analysis and Optimization of Tail-Biting Spatially Coupled Protograph LDPC Codes for BICM-ID Systems
    • [cs.IT]Average Age-of-Information with a Backup Information Source
    • [cs.IT]Channel Estimation for Wireless Communication Systems Assisted by Large Intelligent Surfaces
    • [cs.IT]Computable Upper Bounds on the Capacity of Finite-State Channels
    • [cs.IT]Conditional Mutual Information Neural Estimator
    • [cs.IT]Energy Efficient Federated Learning Over Wireless Communication Networks
    • [cs.IT]Information Update: TDMA or FDMA?
    • [cs.IT]On Data-Processing and Majorization Inequalities for $f$-Divergences with Applications
    • [cs.IT]Symbol-pair Weight Distributions of Some Linear Codes
    • [cs.LG]A Divergence Minimization Perspective on Imitation Learning Methods
    • [cs.LG]A Method to Model Conditional Distributions with Normalizing Flows
    • [cs.LG]A Programmable Approach to Model Compression
    • [cs.LG]A Scalable Multilabel Classification to Deploy Deep Learning Architectures For Edge Devices
    • [cs.LG]Alleviating Label Switching with Optimal Transport
    • [cs.LG]An Algorithm for Routing Capsules in All Domains
    • [cs.LG]Auptimizer — an Extensible, Open-Source Framework for Hyperparameter Tuning
    • [cs.LG]Computational Separations between Sampling and Optimization
    • [cs.LG]DC-S3GD: Delay-Compensated Stale-Synchronous SGD for Large-Scale Decentralized Neural Network Training
    • [cs.LG]Deep least-squares methods: an unsupervised learning-based numerical method for solving elliptic PDEs
    • [cs.LG]Designing Evaluations of Machine Learning Models for Subjective Inference: The Case of Sentence Toxicity
    • [cs.LG]Distributional Reward Decomposition for Reinforcement Learning
    • [cs.LG]Don’t Blame the ELBO! A Linear VAE Perspective on Posterior Collapse
    • [cs.LG]E.T.-RNN: Applying Deep Learning to Credit Loan Applications
    • [cs.LG]Exact Partitioning of High-order Models with a Novel Convex Tensor Cone Relaxation
    • [cs.LG]Experience Sharing Between Cooperative Reinforcement Learning Agents
    • [cs.LG]Feedback-Based Self-Learning in Large-Scale Conversational AI Agents
    • [cs.LG]Fully Parameterized Quantile Function for Distributional Reinforcement Learning
    • [cs.LG]Guided Layer-wise Learning for Deep Models using Side Information
    • [cs.LG]Hierarchical Mixtures of Generators for Adversarial Learning
    • [cs.LG]How can we fool LIME and SHAP? Adversarial Attacks on Post hoc Explanation Methods
    • [cs.LG]Improving reinforcement learning algorithms: towards optimal learning rate policies
    • [cs.LG]Learning-based estimation of dielectric properties and tissue density in head models for personalized radio-frequency dosimetry
    • [cs.LG]MLPerf Inference Benchmark
    • [cs.LG]Machine Learning using the Variational Predictive Information Bottleneck with a Validation Set
    • [cs.LG]Online matrix factorization for Markovian data and applications to Network Dictionary Learning
    • [cs.LG]OpenML-Python: an extensible Python API for OpenML
    • [cs.LG]Post-Training 4-bit Quantization on Embedding Tables
    • [cs.LG]Practical Compositional Fairness: Understanding Fairness in Multi-Task ML Systems
    • [cs.LG]Safe Linear Thompson Sampling
    • [cs.LG]Searching to Exploit Memorization Effect in Learning from Corrupted Labels
    • [cs.LG]Secure Federated Submodel Learning
    • [cs.LG]Spatially regularized active diffusion learning for high-dimensional images
    • [cs.LG]The gradient complexity of linear regression
    • [cs.LG]Unfairness towards subjective opinions in Machine Learning
    • [cs.LG]Unsupervised Hierarchy Matching with Optimal Transport over Hyperbolic Spaces
    • [cs.LG]Why X rather than Y? Explaining Neural Model’ Predictions by Generating Intervention Counterfactual Samples
    • [cs.NE]Fast Transformer Decoding: One Write-Head is All You Need
    • [cs.RO]Cognitive and motor compliance in intentional human-robot interaction
    • [cs.RO]Effects of Haptic Feedback on the Wristduring Virtual Manipulation
    • [cs.RO]Nonverbal Robot Feedback for Human Teachers
    • [cs.RO]Rapid Uncertainty Propagation and Chance-Constrained Path Planning for Small Unmanned Aerial Vehicles
    • [cs.SD]Finding Strength in Weakness: Learning to Separate Sounds with Weak Supervision
    • [cs.SD]OtoMechanic: Auditory Automobile Diagnostics via Query-by-Example
    • [cs.SI]Click Maximization in Online Social Networks Using Optimal Choice of Targeted Interests
    • [eess.AS]A comparison of end-to-end models for long-form speech recognition
    • [eess.AS]Addressing Ambiguity of Emotion Labels Through Meta-learning
    • [eess.AS]Small-Footprint Keyword Spotting on Raw Audio Data with Sinc-Convolutions
    • [eess.AS]The Speed Submission to DIHARD II: Contributions & Lessons Learned
    • [eess.IV]Automated Left Ventricle Dimension Measurement in 2D Cardiac Ultrasound via an Anatomically Meaningful CNN Approach
    • [eess.IV]Deep Compressed Pneumonia Detection for Low-Power Embedded Devices
    • [eess.IV]GAN-enhanced Conditional Echocardiogram Generation
    • [eess.IV]Lesson Learnt: Modularization of Deep Networks Allow Cross-Modality Reuse
    • [eess.IV]Machine Learning Techniques for Biomedical Image Segmentation: An Overview of Technical Aspects and Introduction to State-of-Art Applications
    • [eess.IV]Optimization with soft Dice can lead to a volumetric bias
    • [eess.IV]Scribble-based Hierarchical Weakly Supervised Learning for Brain Tumor Segmentation
    • [eess.IV]Semantic Image Completion and Enhancement using Deep Learning
    • [eess.IV]Unimodal-uniform Constrained Wasserstein Training for Medical Diagnosis
    • [eess.IV]User-Intended Doppler Measurement Type Prediction Combining CNNs With Smart Post-Processing
    • [eess.IV]Weakly Supervised Fine Tuning Approach for Brain Tumor Segmentation Problem
    • [eess.SP]Convolutional Neural Network for Multipath Detection in GNSS Receivers
    • [math.DS]Permutations With Restricted Movement
    • [math.NT]Machine Learning meets Number Theory: The Data Science of Birch-Swinnerton-Dyer
    • [math.OC]Generalized Self-concordant Hessian-barrier algorithms
    • [math.OC]High-dimensional Black-box Optimization Under Uncertainty
    • [math.OC]Linear Support Vector Regression with Linear Constraints
    • [math.OC]Resilient Load Restoration in Microgrids Considering Mobile Energy Storage Fleets: A Deep Reinforcement Learning Approach
    • [math.PR]Weak convergence of empirical Wasserstein type distances
    • [math.ST]A Fourier Analytical Approach to Estimation of Smooth Functions in Gaussian Shift Model
    • [math.ST]Optimal Design of Experiments on Riemannian Manifolds
    • [math.ST]Simultaneous estimation of complementary moment independent sensitivity measures for reliability analysis
    • [math.ST]The Fourier Transform Method for Volatility Functional Inference by Asynchronous Observations
    • [physics.data-an]Randomized Computer Vision Approaches for Pattern Recognition in Timepix and Timepix3 Detectors
    • [physics.soc-ph]weg2vec: Event embedding for temporal networks
    • [q-bio.BM]Using Residual Dipolar Couplings from Two Alignment Media to Detect Structural Homology
    • [q-bio.QM]Fetal cardiovascular decompensation during labor predicted from the individual heart rate: a prospective study in fetal sheep near term and the impact of low sampling rate
    • [q-fin.CP]Deep Learning for Stock Selection Based on High Frequency Price-Volume Data
    • [q-fin.PM]Robo-advising: Learning Investor’s Risk Preferences via Portfolio Choices
    • [q-fin.TR]Modelling bid-ask spread conditional distributions using hierarchical correlation reconstruction
    • [stat.AP]Modelling extreme claims via composite models and threshold selection methods
    • [stat.AP]The design and statistical aspects of VIETNARMS: a strategic post-licensing trial of multiple oral direct acting antiviral Hepatitis C treatment strategies in Vietnam
    • [stat.CO]A step further towards automatic and efficient reversible jump algorithms
    • [stat.ME]A Comparison of Methods of Inference in Randomized Experiments from a Restricted Set of Allocations
    • [stat.ME]A Conway-Maxwell-Multinomial Distribution for Flexible Modeling of Clustered Categorical Data
    • [stat.ME]Bias-aware model selection for machine learning of doubly robust functionals
    • [stat.ME]Estimation of Spatial Deformation for Nonstationary Processes via Variogram Alignment
    • [stat.ME]Minimax Nonparametric Parallelism Test
    • [stat.ME]Regularization of Bayesian shrinkage priors and inference via geometrically / uniformly ergodic Gibbs sampler
    • [stat.ME]Semiparametric Estimation of Cross-covariance Functions for Multivariate Random Fields
    • [stat.ML]An Alternative Probabilistic Interpretation of the Huber Loss
    • [stat.ML]Designing over uncertain outcomes with stochastic sampling Bayesian optimization
    • [stat.ML]Information-Theoretic Generalization Bounds for SGLD via Data-Dependent Estimates
    • [stat.ML]Integrating Markov processes with structural causal modeling enables counterfactual inference in complex systems

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

    • [astro-ph.IM]Algorithms and Statistical Models for Scientific Discovery in the Petabyte Era
    Brian Nord, Andrew J. Connolly, Jamie Kinney, Jeremy Kubica, Gautaum Narayan, Joshua E. G. Peek, Chad Schafer, Erik J. Tollerud, Camille Avestruz, G. Jogesh Babu, Simon Birrer, Douglas Burke, João Caldeira, Douglas A. Caldwell, Joleen K. Carlberg, Yen-Chi Chen, Chuanfei Dong, Eric D. Feigelson, V. Zach Golkhou, Vinay Kashyap, T. S. Li, Thomas Loredo, Luisa Lucie-Smith, Kaisey S. Mandel, J. R. Martínez-Galarza, Adam A. Miller, Priyamvada Natarajan, Michelle Ntampaka, Andy Ptak, David Rapetti, Lior Shamir, Aneta Siemiginowska, Brigitta M. Sipőcz, Arfon M. Smith, Nhan Tran, Ricardo Vilalta, Lucianne M. Walkowicz, John ZuHone
    http://arxiv.org/abs/1911.02479v1

    • [astro-ph.IM]An Information Theory Approach on Deciding Spectroscopic Follow Ups
    Javiera Astudillo, Pavlos Protopapas, Karim Pichara, Pablo Huijse
    http://arxiv.org/abs/1911.02444v1

    • [cond-mat.dis-nn]Statistical physics of unsupervised learning with prior knowledge in neural networks
    Tianqi Hou, Haiping Huang
    http://arxiv.org/abs/1911.02344v1

    • [cs.AI]A Deep Reinforcement Learning based Approach to Learning Transferable Proof Guidance Strategies
    Maxwell Crouse, Spencer Whitehead, Ibrahim Abdelaziz, Bassem Makni, Cristina Cornelio, Pavan Kapanipathi, Edwin Pell, Kavitha Srinivas, Veronika Thost, Michael Witbrock, Achille Fokoue
    http://arxiv.org/abs/1911.02065v1

    • [cs.AI]A Latent Feelings-aware RNN Model for User Churn Prediction with Behavioral Data
    Meng Xi, Zhiling Luo, Naibo Wang, Jianwei Yin
    http://arxiv.org/abs/1911.02224v1

    • [cs.AI]A Spoken Dialogue System for Spatial Question Answering in a Physical Blocks World
    Georgiy Platonov, Benjamin Kane, Aaron Gindi, Lenhart K. Schubert
    http://arxiv.org/abs/1911.02524v1

    • [cs.AI]CoKE: Contextualized Knowledge Graph Embedding
    Quan Wang, Pingping Huang, Haifeng Wang, Songtai Dai, Wenbin Jiang, Jing Liu, Yajuan Lyu, Yong Zhu, Hua Wu
    http://arxiv.org/abs/1911.02168v1

    • [cs.AI]Heuristics for Interpretable Knowledge Graph Contextualization
    Kshitij Fadnis, Kartik Talamadupula, Pavan Kapanipathi, Haque Ishfaq, Salim Roukos, Achille Fokoue
    http://arxiv.org/abs/1911.02085v1

    • [cs.AI]Robot navigation and target capturing using nature-inspired approaches in a dynamic environment
    Devansh Verma, Priyansh Saxena, Ritu Tiwari
    http://arxiv.org/abs/1911.02268v1

    • [cs.AI]Wearable Affective Life-Log System for Understanding Emotion Dynamics in Daily Life
    Byung Hyung Kim, Sungho Jo
    http://arxiv.org/abs/1911.01072v2

    • [cs.CL]Enriching Conversation Context in Retrieval-based Chatbots
    Amir Vakili Tahami, Azadeh Shakery
    http://arxiv.org/abs/1911.02290v1

    • [cs.CL]Guiding Non-Autoregressive Neural Machine Translation Decoding with Reordering Information
    Qiu Ran, Yankai Lin, Peng Li, Jie Zhou
    http://arxiv.org/abs/1911.02215v1

    • [cs.CL]Guiding Variational Response Generator to Exploit Persona
    Bowen Wu, Mengyuan Li, Zongsheng Wang, Yifu Chen, Derek Wong, Qihang Feng, Junhong Huang, Baoxun Wang
    http://arxiv.org/abs/1911.02390v1

    • [cs.CL]Hierarchical Contextualized Representation for Named Entity Recognition
    Ying Luo, Fengshun Xiao, Hai Zhao
    http://arxiv.org/abs/1911.02257v1

    • [cs.CL]Infusing Knowledge into the Textual Entailment Task Using Graph Convolutional Networks
    Pavan Kapanipathi, Veronika Thost, Siva Sankalp Patel, Spencer Whitehead, Ibrahim Abdelaziz, Avinash Balakrishnan, Maria Chang, Kshitij Fadnis, Chulaka Gunasekara, Bassem Makni, Nicholas Mattei, Kartik Talamadupula, Achille Fokoue
    http://arxiv.org/abs/1911.02060v1

    • [cs.CL]Learning to Answer by Learning to Ask: Getting the Best of GPT-2 and BERT Worlds
    Tassilo Klein, Moin Nabi
    http://arxiv.org/abs/1911.02365v1

    • [cs.CL]Multi-Paragraph Reasoning with Knowledge-enhanced Graph Neural Network
    Deming Ye, Yankai Lin, Zhenghao Liu, Zhiyuan Liu, Maosong Sun
    http://arxiv.org/abs/1911.02170v1

    • [cs.CL]Optimizing the Factual Correctness of a Summary: A Study of Summarizing Radiology Reports
    Yuhao Zhang, Derek Merck, Emily Bao Tsai, Christopher D. Manning, Curtis P. Langlotz
    http://arxiv.org/abs/1911.02541v1

    • [cs.CL]SentiLR: Linguistic Knowledge Enhanced Language Representation for Sentiment Analysis
    Pei Ke, Haozhe Ji, Siyang Liu, Xiaoyan Zhu, Minlie Huang
    http://arxiv.org/abs/1911.02493v1

    • [cs.CL]Seq2Emo for Multi-label Emotion Classification Based on Latent Variable Chains Transformation
    Chenyang Huang, Amine Trabelsi, Osmar R. Zaïane
    http://arxiv.org/abs/1911.02147v1

    • [cs.CL]Toward Dimensional Emotion Detection from Categorical Emotion Annotations
    Sungjoon Park, Jiseon Kim, Jaeyeol Jeon, Heeyoung Park, Alice Oh
    http://arxiv.org/abs/1911.02499v1

    • [cs.CL]Unsupervised Cross-lingual Representation Learning at Scale
    Alexis Conneau, Kartikay Khandelwal, Naman Goyal, Vishrav Chaudhary, Guillaume Wenzek, Francisco Guzmán, Edouard Grave, Myle Ott, Luke Zettlemoyer, Veselin Stoyanov
    http://arxiv.org/abs/1911.02116v1

    • [cs.CL]Unsupervised Multi-Document Opinion Summarization as Copycat-Review Generation
    Arthur Bražinskas, Mirella Lapata, Ivan Titov
    http://arxiv.org/abs/1911.02247v1

    • [cs.CR]A Survey of Blockchain Applications in Different Domains
    Wubing Chen, Zhiying Xu, Shuyu Shi, Yang Zhao, Jun Zhao
    http://arxiv.org/abs/1911.02013v1

    • [cs.CR]Data Poisoning Attacks to Local Differential Privacy Protocols
    Xiaoyu Cao, Jinyuan Jia, Neil Zhenqiang Gong
    http://arxiv.org/abs/1911.02046v1

    • [cs.CR]Intriguing Properties of Adversarial ML Attacks in the Problem Space
    Fabio Pierazzi, Feargus Pendlebury, Jacopo Cortellazzi, Lorenzo Cavallaro
    http://arxiv.org/abs/1911.02142v1

    • [cs.CR]Privacy Preserving Threat Hunting in Smart Home Environments
    Ahmed M. Elmisery, Mirela Sertovic
    http://arxiv.org/abs/1911.02174v1

    • [cs.CR]The Naked Sun: Malicious Cooperation Between Benign-Looking Processes
    Fabio De Gaspari, Dorjan Hitaj, Giulio Pagnotta, Lorenzo De Carli, Luigi V. Mancini
    http://arxiv.org/abs/1911.02423v1

    • [cs.CV]AIM 2019 Challenge on Image Demoireing: Dataset and Study
    Shanxin Yuan, Radu Timofte, Gregory Slabaugh, Ales Leonardis
    http://arxiv.org/abs/1911.02498v1

    • [cs.CV]Architectural Tricks for Deep Learning in Remote Photoplethysmography
    Mikhail Kopeliovich, Yuriy Mironenko, Mikhail Petrushan
    http://arxiv.org/abs/1911.02202v1

    • [cs.CV]Boosting Object Recognition in Point Clouds by Saliency Detection
    Marlon Marcon, Riccardo Spezialetti, Samuele Salti, Luciano Silva, Luigi Di Stefano
    http://arxiv.org/abs/1911.02286v1

    • [cs.CV]Contextual Grounding of Natural Language Entities in Images
    Farley Lai, Ning Xie, Derek Doran, Asim Kadav
    http://arxiv.org/abs/1911.02133v1

    • [cs.CV]Federated Adversarial Domain Adaptation
    Xingchao Peng, Zijun Huang, Yizhe Zhu, Kate Saenko
    http://arxiv.org/abs/1911.02054v1

    • [cs.CV]Interpretable Self-Attention Temporal Reasoning for Driving Behavior Understanding
    Yi-Chieh Liu, Yung-An Hsieh, Min-Hung Chen, Chao-Han Huck Yang, Jesper Tegner, Yi-Chang James Tsai
    http://arxiv.org/abs/1911.02172v1

    • [cs.CV]Localization-aware Channel Pruning for Object Detection
    Zihao Xie, Wenbing Tao, Zhu Li, Lin Zhao
    http://arxiv.org/abs/1911.02237v1

    • [cs.CV]Melanoma detection with electrical impedance spectroscopy and dermoscopy using joint deep learning models
    Nils Gessert, Marcel Bengs, Alexander Schlaefer
    http://arxiv.org/abs/1911.02322v1

    • [cs.CV]Predicting Long-Term Skeletal Motions by a Spatio-Temporal Hierarchical Recurrent Network
    Junfeng Hu, Zhencheng Fan, Jun Liao, Li Liu
    http://arxiv.org/abs/1911.02404v1

    • [cs.CV]Predictive modeling of brain tumor: A Deep learning approach
    Priyansh Saxena, Akshat Maheshwari, Saumil Maheshwari
    http://arxiv.org/abs/1911.02265v1

    • [cs.CV]Recurrent Instance Segmentation using Sequences of Referring Expressions
    Alba Herrera-Palacio, Carles Ventura, Carina Silberer, Ionut-Teodor Sorodoc, Gemma Boleda, Xavier Giro-i-Nieto
    http://arxiv.org/abs/1911.02103v1

    • [cs.CV]SCL: Towards Accurate Domain Adaptive Object Detection via Gradient Detach Based Stacked Complementary Losses
    Zhiqiang Shen, Harsh Maheshwari, Weichen Yao, Marios Savvides
    http://arxiv.org/abs/1911.02559v1

    • [cs.CV]SRINet: Learning Strictly Rotation-Invariant Representations for Point Cloud Classification and Segmentation
    Xiao Sun, Zhouhui Lian, Jianguo Xiao
    http://arxiv.org/abs/1911.02163v1

    • [cs.CV]Satellite Pose Estimation Challenge: Dataset, Competition Design and Results
    Mate Kisantal, Sumant Sharma, Tae Ha Park, Dario Izzo, Marcus Märtens, Simone D’Amico
    http://arxiv.org/abs/1911.02050v1

    • [cs.CV]Spatial Feature Extraction in Airborne Hyperspectral Images Using Local Spectral Similarity
    Anand S Sahadevan, Arundhati Misra, Praveen Gupta
    http://arxiv.org/abs/1911.02285v1

    • [cs.CV]Towards Large yet Imperceptible Adversarial Image Perturbations with Perceptual Color Distance
    Zhengyu Zhao, Zhuoran Liu, Martha Larson
    http://arxiv.org/abs/1911.02466v1

    • [cs.CV]Uninformed Students: Student-Teacher Anomaly Detection with Discriminative Latent Embeddings
    Paul Bergmann, Michael Fauser, David Sattlegger, Carsten Steger
    http://arxiv.org/abs/1911.02357v1

    • [cs.CV]Where is the Fake? Patch-Wise Supervised GANs for Texture Inpainting
    Ahmed Ben Saad, Youssef Tamaazousti, Josselin Kherroubi, Alexis He
    http://arxiv.org/abs/1911.02274v1

    • [cs.CY]Adaptively selecting occupations to detect skill shortages from online job ads
    Nik Dawson, Marian-Andrei Rizoiu, Benjamin Johnston, Mary-Anne Williams
    http://arxiv.org/abs/1911.02302v1

    • [cs.CY]Investigating Ortega Hypothesis in Q&A portals: An Analysis of StackOverflow
    Anamika Chhabra, S. R. S. Iyengar
    http://arxiv.org/abs/1911.02376v1

    • [cs.DC]A Language-based Serverless Function Accelerator
    Emily Herbert, Arjun Guha
    http://arxiv.org/abs/1911.02178v1

    • [cs.DC]Asynchronous Online Federated Learning for Edge Devices
    Yujing Chen, Yue Ning, Huzefa Rangwala
    http://arxiv.org/abs/1911.02134v1

    • [cs.DC]DeLottery: A Novel Decentralized Lottery System Based on Blockchain Technology
    Zhifeng Jia, Rui Chen, Jie Li
    http://arxiv.org/abs/1911.02392v1

    • [cs.DC]Developing a Process in Architecting Microservice Infrastructure with Docker, Kubernetes, and Istio
    Yujing Wang, Darrel Ma
    http://arxiv.org/abs/1911.02275v1

    • [cs.DC]Failure Analysis and Quantification for Contemporary and Future Supercomputers
    Li Tan, Nathan DeBardeleben
    http://arxiv.org/abs/1911.02118v1

    • [cs.DC]KLARAPTOR: A Tool for Dynamically Finding Optimal Kernel Launch Parameters Targeting CUDA Programs
    Alexander Brandt, Davood Mohajerani, Marc Moreno Maza, Jeeva Paudel, Linxiao Wang
    http://arxiv.org/abs/1911.02373v1

    • [cs.DC]Performance Evaluation of VDI Environment
    Hafiz ur Rahman, Farag Azzedin, Ahmad Shawahna, Faisal Sajjad, Alyahya Saleh Abdulrahman
    http://arxiv.org/abs/1911.01923v1

    • [cs.DC]Soft Error Resilience and Failure Recovery for Continuum Dynamics Applications
    Li Tan, Marc Charest, Nathan DeBardeleben, Qiang Guan, Ben Bergen
    http://arxiv.org/abs/1911.02114v1

    • [cs.DC]uqSim: Scalable and Validated Simulation of Cloud Microservices
    Yanqi Zhang, Yu Gan, Christina Delimitrou
    http://arxiv.org/abs/1911.02122v1

    • [cs.DM]Optimal non-adaptive group testing
    Amin Coja-Oghlan, Oliver Gebhard, Max Hahn-Klimroth, Philipp Loick
    http://arxiv.org/abs/1911.02287v1

    • [cs.DS]Efficiently Learning Structured Distributions from Untrusted Batches
    Sitan Chen, Jerry Li, Ankur Moitra
    http://arxiv.org/abs/1911.02035v1

    • [cs.GT]Liability Design for Autonomous Vehicles and Human-Driven Vehicles: A Hierarchical Game-Theoretic Approach
    Xuan Di, Xu Chen, Eric Talley
    http://arxiv.org/abs/1911.02405v1

    • [cs.GT]Multi-Item Mechanisms without Item-Independence: Learnability via Robustness
    Johaness Brustle, Yang Cai, Constantinos Daskalakis
    http://arxiv.org/abs/1911.02146v1

    • [cs.HC]An Affective Situation Labeling System from Psychological Behaviors in Emotion Recognition
    Byung Hyung Kim, Sungho Jo
    http://arxiv.org/abs/1911.01158v2

    • [cs.IR]MBCAL: A Simple and Efficient Reinforcement Learning Method for Recommendation Systems
    Fan Wang, Xiaomin Fang, Lihang Liu, Hao Tian, Zhiming Peng
    http://arxiv.org/abs/1911.02248v1

    • [cs.IT]Active Status Update Packet Drop Control in an Energy Harvesting Node
    Parisa Rafiee, Omur Ozel
    http://arxiv.org/abs/1911.01407v2

    • [cs.IT]Analysis and Optimization of Tail-Biting Spatially Coupled Protograph LDPC Codes for BICM-ID Systems
    Zhaojie Yang, Yi Fang, Guohua Zhang, Francis C. M. Lau, Shahid Mumtaz, Daniel B. da Costa
    http://arxiv.org/abs/1911.02227v1

    • [cs.IT]Average Age-of-Information with a Backup Information Source
    Elvina Gindullina, Leonardo Badia, Deniz Gündüz
    http://arxiv.org/abs/1911.02462v1

    • [cs.IT]Channel Estimation for Wireless Communication Systems Assisted by Large Intelligent Surfaces
    Junliang Lin, Gongpu Wang, Rongfei Fan, Theodoros A. Tsiftsis, Chintha Tellambura
    http://arxiv.org/abs/1911.02158v1

    • [cs.IT]Computable Upper Bounds on the Capacity of Finite-State Channels
    Bashar Huleihel, Oron Sabag, Haim H. Permuter, Navin Kashyap, Shlomo Shamai
    http://arxiv.org/abs/1911.02113v1

    • [cs.IT]Conditional Mutual Information Neural Estimator
    Sina Molavipour, Germán Bassi, Mikael Skoglund
    http://arxiv.org/abs/1911.02277v1

    • [cs.IT]Energy Efficient Federated Learning Over Wireless Communication Networks
    Zhaohui Yang, Mingzhe Chen, Walid Saad, Choong Seon Hong, Mohammad Shikh-Bahaei
    http://arxiv.org/abs/1911.02417v1

    • [cs.IT]Information Update: TDMA or FDMA?
    Haoyuan Pan, Soung Chang Liew
    http://arxiv.org/abs/1911.02241v1

    • [cs.IT]On Data-Processing and Majorization Inequalities for $f$-Divergences with Applications
    Igal Sason
    http://arxiv.org/abs/1911.02436v1

    • [cs.IT]Symbol-pair Weight Distributions of Some Linear Codes
    Junru Ma, Jinquan Luo
    http://arxiv.org/abs/1911.02184v1

    • [cs.LG]A Divergence Minimization Perspective on Imitation Learning Methods
    Seyed Kamyar Seyed Ghasemipour, Richard Zemel, Shixiang Gu
    http://arxiv.org/abs/1911.02256v1

    • [cs.LG]A Method to Model Conditional Distributions with Normalizing Flows
    Zhisheng Xiao, Qing Yan, Yali Amit
    http://arxiv.org/abs/1911.02052v1

    • [cs.LG]A Programmable Approach to Model Compression
    Vinu Joseph, Saurav Muralidharan, Animesh Garg, Michael Garland, Ganesh Gopalakrishnan
    http://arxiv.org/abs/1911.02497v1

    • [cs.LG]A Scalable Multilabel Classification to Deploy Deep Learning Architectures For Edge Devices
    Tolulope A. Odetola, Ogheneuriri Oderhohwo, Syed Rafay Hasan
    http://arxiv.org/abs/1911.02098v1

    • [cs.LG]Alleviating Label Switching with Optimal Transport
    Pierre Monteiller, Sebastian Claici, Edward Chien, Farzaneh Mirzazadeh, Justin Solomon, Mikhail Yurochkin
    http://arxiv.org/abs/1911.02053v1

    • [cs.LG]An Algorithm for Routing Capsules in All Domains
    Franz A. Heinsen
    http://arxiv.org/abs/1911.00792v2

    • [cs.LG]Auptimizer — an Extensible, Open-Source Framework for Hyperparameter Tuning
    Jiayi Liu, Samarth Tripathi, Unmesh Kurup, Mohak Shah
    http://arxiv.org/abs/1911.02522v1

    • [cs.LG]Computational Separations between Sampling and Optimization
    Kunal Talwar
    http://arxiv.org/abs/1911.02074v1

    • [cs.LG]DC-S3GD: Delay-Compensated Stale-Synchronous SGD for Large-Scale Decentralized Neural Network Training
    Alessandro Rigazzi
    http://arxiv.org/abs/1911.02516v1

    • [cs.LG]Deep least-squares methods: an unsupervised learning-based numerical method for solving elliptic PDEs
    Zhiqiang Cai, Jingshuang Chen, Min Liu, Xinyu Liu
    http://arxiv.org/abs/1911.02109v1

    • [cs.LG]Designing Evaluations of Machine Learning Models for Subjective Inference: The Case of Sentence Toxicity
    Agathe Balayn, Alessandro Bozzon
    http://arxiv.org/abs/1911.02471v1

    • [cs.LG]Distributional Reward Decomposition for Reinforcement Learning
    Zichuan Lin, Li Zhao, Derek Yang, Tao Qin, Guangwen Yang, Tie-Yan Liu
    http://arxiv.org/abs/1911.02166v1

    • [cs.LG]Don’t Blame the ELBO! A Linear VAE Perspective on Posterior Collapse
    James Lucas, George Tucker, Roger Grosse, Mohammad Norouzi
    http://arxiv.org/abs/1911.02469v1

    • [cs.LG]E.T.-RNN: Applying Deep Learning to Credit Loan Applications
    Dmitrii Babaev, Maxim Savchenko, Alexander Tuzhilin, Dmitrii Umerenkov
    http://arxiv.org/abs/1911.02496v1

    • [cs.LG]Exact Partitioning of High-order Models with a Novel Convex Tensor Cone Relaxation
    Chuyang Ke, Jean Honorio
    http://arxiv.org/abs/1911.02161v1

    • [cs.LG]Experience Sharing Between Cooperative Reinforcement Learning Agents
    Lucas Oliveira Souza, Gabriel de Oliveira Ramos, Celia Ghedini Ralha
    http://arxiv.org/abs/1911.02191v1

    • [cs.LG]Feedback-Based Self-Learning in Large-Scale Conversational AI Agents
    Pragaash Ponnusamy, Alireza Roshan Ghias, Chenlei Guo, Ruhi Sarikaya
    http://arxiv.org/abs/1911.02557v1

    • [cs.LG]Fully Parameterized Quantile Function for Distributional Reinforcement Learning
    Derek Yang, Li Zhao, Zichuan Lin, Tao Qin, Jiang Bian, Tieyan Liu
    http://arxiv.org/abs/1911.02140v1

    • [cs.LG]Guided Layer-wise Learning for Deep Models using Side Information
    Pavel Sulimov, Elena Sukmanova, Roman Chereshnev, Attila Kertesz-Farkas
    http://arxiv.org/abs/1911.02048v1

    • [cs.LG]Hierarchical Mixtures of Generators for Adversarial Learning
    Alper Ahmetoğlu, Ethem Alpaydın
    http://arxiv.org/abs/1911.02069v1

    • [cs.LG]How can we fool LIME and SHAP? Adversarial Attacks on Post hoc Explanation Methods
    Dylan Slack, Sophie Hilgard, Emily Jia, Sameer Singh, Himabindu Lakkaraju
    http://arxiv.org/abs/1911.02508v1

    • [cs.LG]Improving reinforcement learning algorithms: towards optimal learning rate policies
    Othmane Mounjid, Charles-Albert Lehalle
    http://arxiv.org/abs/1911.02319v1

    • [cs.LG]Learning-based estimation of dielectric properties and tissue density in head models for personalized radio-frequency dosimetry
    Essam A. Rashed, Yinliang Diao, Akimasa Hirata
    http://arxiv.org/abs/1911.01220v2

    • [cs.LG]MLPerf Inference Benchmark
    Vijay Janapa Reddi, Christine Cheng, David Kanter, Peter Mattson, Guenther Schmuelling, Carole-Jean Wu, Brian Anderson, Maximilien Breughe, Mark Charlebois, William Chou, Ramesh Chukka, Cody Coleman, Sam Davis, Pan Deng, Greg Diamos, Jared Duke, Dave Fick, J. Scott Gardner, Itay Hubara, Sachin Idgunji, Thomas B. Jablin, Jeff Jiao, Tom St. John, Pankaj Kanwar, David Lee, Jeffery Liao, Anton Lokhmotov, Francisco Massa, Peng Meng, Paulius Micikevicius, Colin Osborne, Gennady Pekhimenko, Arun Tejusve Raghunath Rajan, Dilip Sequeira, Ashish Sirasao, Fei Sun, Hanlin Tang, Michael Thomson, Frank Wei, Ephrem Wu, Lingjie Xu, Koichi Yamada, Bing Yu, George Yuan, Aaron Zhong, Peizhao Zhang, Yuchen Zhou
    http://arxiv.org/abs/1911.02549v1

    • [cs.LG]Machine Learning using the Variational Predictive Information Bottleneck with a Validation Set
    Sayandev Mukherjee
    http://arxiv.org/abs/1911.02210v1

    • [cs.LG]Online matrix factorization for Markovian data and applications to Network Dictionary Learning
    Hanbaek Lyu, Deanna Needell, Laura Balzano
    http://arxiv.org/abs/1911.01931v2

    • [cs.LG]OpenML-Python: an extensible Python API for OpenML
    Matthias Feurer, Jan N. van Rijn, Arlind Kadra, Pieter Gijsbers, Neeratyoy Mallik, Sahithya Ravi, Andreas Müller, Joaquin Vanschoren, Frank Hutter
    http://arxiv.org/abs/1911.02490v1

    • [cs.LG]Post-Training 4-bit Quantization on Embedding Tables
    Hui Guan, Andrey Malevich, Jiyan Yang, Jongsoo Park, Hector Yuen
    http://arxiv.org/abs/1911.02079v1

    • [cs.LG]Practical Compositional Fairness: Understanding Fairness in Multi-Task ML Systems
    Xuezhi Wang, Nithum Thain, Anu Sinha, Ed H. Chi, Jilin Chen, Alex Beutel
    http://arxiv.org/abs/1911.01916v2

    • [cs.LG]Safe Linear Thompson Sampling
    Ahmadreza Moradipari, Sanae Amani, Mahnoosh Alizadeh, Christos Thrampoulidis
    http://arxiv.org/abs/1911.02156v1

    • [cs.LG]Searching to Exploit Memorization Effect in Learning from Corrupted Labels
    Hansi Yang, Quanming Yao, Bo Han, Gang Niu
    http://arxiv.org/abs/1911.02377v1

    • [cs.LG]Secure Federated Submodel Learning
    Chaoyue Niu, Fan Wu, Shaojie Tang, Lifeng Hua, Rongfei Jia, Chengfei Lv, Zhihua Wu, Guihai Chen
    http://arxiv.org/abs/1911.02254v1

    • [cs.LG]Spatially regularized active diffusion learning for high-dimensional images
    James M. Murphy
    http://arxiv.org/abs/1911.02155v1

    • [cs.LG]The gradient complexity of linear regression
    Mark Braverman, Elad Hazan, Max Simchowitz, Blake Woodworth
    http://arxiv.org/abs/1911.02212v1

    • [cs.LG]Unfairness towards subjective opinions in Machine Learning
    Agathe Balayn, Alessandro Bozzon, Zoltan Szlavik
    http://arxiv.org/abs/1911.02455v1

    • [cs.LG]Unsupervised Hierarchy Matching with Optimal Transport over Hyperbolic Spaces
    David Alvarez-Melis, Youssef Mroueh, Tommi S. Jaakkola
    http://arxiv.org/abs/1911.02536v1

    • [cs.LG]Why X rather than Y? Explaining Neural Model’ Predictions by Generating Intervention Counterfactual Samples
    Thai Le, Suhang Wang, Dongwon Lee
    http://arxiv.org/abs/1911.02042v1

    • [cs.NE]Fast Transformer Decoding: One Write-Head is All You Need
    Noam Shazeer
    http://arxiv.org/abs/1911.02150v1

    • [cs.RO]Cognitive and motor compliance in intentional human-robot interaction
    Hendry Ferreira Chame, Jun Tani
    http://arxiv.org/abs/1911.01753v2

    • [cs.RO]Effects of Haptic Feedback on the Wristduring Virtual Manipulation
    Mine Sarac, Allison M. Okamura, Massimiliano Di Luca
    http://arxiv.org/abs/1911.02104v1

    • [cs.RO]Nonverbal Robot Feedback for Human Teachers
    Sandy H. Huang, Isabella Huang, Ravi Pandya, Anca D. Dragan
    http://arxiv.org/abs/1911.02320v1

    • [cs.RO]Rapid Uncertainty Propagation and Chance-Constrained Path Planning for Small Unmanned Aerial Vehicles
    Andrew W. Berning Jr., Anouck Girard, Ilya Kolmanovsky, Sarah N. D’Souza
    http://arxiv.org/abs/1911.02543v1

    • [cs.SD]Finding Strength in Weakness: Learning to Separate Sounds with Weak Supervision
    Fatemeh Pishdadian, Gordon Wichern, Jonathan Le Roux
    http://arxiv.org/abs/1911.02182v1

    • [cs.SD]OtoMechanic: Auditory Automobile Diagnostics via Query-by-Example
    Max Morrison, Bryan Pardo
    http://arxiv.org/abs/1911.02073v1

    • [cs.SI]Click Maximization in Online Social Networks Using Optimal Choice of Targeted Interests
    Nathaniel Hudson, Hana Khamfroush, Brent Harrison, Adam Craig
    http://arxiv.org/abs/1911.02061v1

    • [eess.AS]A comparison of end-to-end models for long-form speech recognition
    Chung-Cheng Chiu, Wei Han, Yu Zhang, Ruoming Pang, Sergey Kishchenko, Patrick Nguyen, Arun Narayanan, Hank Liao, Shuyuan Zhang, Anjuli Kannan, Rohit Prabhavalkar, Zhifeng Chen, Tara Sainath, Yonghui Wu
    http://arxiv.org/abs/1911.02242v1

    • [eess.AS]Addressing Ambiguity of Emotion Labels Through Meta-learning
    Takuya Fujioka, Dario Bertero, Takeshi Homma, Kenji Nagamatsu
    http://arxiv.org/abs/1911.02216v1

    • [eess.AS]Small-Footprint Keyword Spotting on Raw Audio Data with Sinc-Convolutions
    Simon Mittermaier, Ludwig Kürzinger, Bernd Waschneck, Gerhard Rigoll
    http://arxiv.org/abs/1911.02086v1

    • [eess.AS]The Speed Submission to DIHARD II: Contributions & Lessons Learned
    Md Sahidullah, Jose Patino, Samuele Cornell, Ruiqing Yin, Sunit Sivasankaran, Hervé Bredin, Pavel Korshunov, Alessio Brutti, Romain Serizel, Emmanuel Vincent, Nicholas Evans, Sébastien Marcel, Stefano Squartini, Claude Barras
    http://arxiv.org/abs/1911.02388v1

    • [eess.IV]Automated Left Ventricle Dimension Measurement in 2D Cardiac Ultrasound via an Anatomically Meaningful CNN Approach
    Andrew Gilbert, Marit Holden, Line Eikvil, Svein Arne Aase, Eigil Samset, Kristin McLeod
    http://arxiv.org/abs/1911.02448v1

    • [eess.IV]Deep Compressed Pneumonia Detection for Low-Power Embedded Devices
    Hongjia Li, Sheng Lin, Ning Liu, Caiwen Ding, Yanzhi Wang
    http://arxiv.org/abs/1911.02007v1

    • [eess.IV]GAN-enhanced Conditional Echocardiogram Generation
    Amir H. Abdi, Teresa Tsang, Purang Abolmaesumi
    http://arxiv.org/abs/1911.02121v1

    • [eess.IV]Lesson Learnt: Modularization of Deep Networks Allow Cross-Modality Reuse
    Weilin Fu, Lennart Husvogt, Stefan Ploner James G. Fujimoto Andreas Maier
    http://arxiv.org/abs/1911.02080v1

    • [eess.IV]Machine Learning Techniques for Biomedical Image Segmentation: An Overview of Technical Aspects and Introduction to State-of-Art Applications
    Hyunseok Seo, Masoud Badiei Khuzani, Varun Vasudevan, Charles Huang, Hongyi Ren, Ruoxiu Xiao, Xiao Jia, Lei Xing
    http://arxiv.org/abs/1911.02521v1

    • [eess.IV]Optimization with soft Dice can lead to a volumetric bias
    Jeroen Bertels, David Robben, Dirk Vandermeulen, Paul Suetens
    http://arxiv.org/abs/1911.02278v1

    • [eess.IV]Scribble-based Hierarchical Weakly Supervised Learning for Brain Tumor Segmentation
    Zhanghexuan Ji, Yan Shen, Chunwei Ma, Mingchen Gao
    http://arxiv.org/abs/1911.02014v1

    • [eess.IV]Semantic Image Completion and Enhancement using Deep Learning
    Vaishnav Chandak, Priyansh Saxena, Manisha Pattanaik, Gaurav Kaushal
    http://arxiv.org/abs/1911.02222v1

    • [eess.IV]Unimodal-uniform Constrained Wasserstein Training for Medical Diagnosis
    Xiaofeng Liu, Xu Han, Yukai Qiao, Yi Ge, Lu Jun
    http://arxiv.org/abs/1911.02475v1

    • [eess.IV]User-Intended Doppler Measurement Type Prediction Combining CNNs With Smart Post-Processing
    Andrew Gilbert, Marit Holden, Line Eikvil, Mariia Rakhmail, Aleksandar Babic, Svein Arne Aase, Eigil Samset, Kristin McLeod
    http://arxiv.org/abs/1911.02407v1

    • [eess.IV]Weakly Supervised Fine Tuning Approach for Brain Tumor Segmentation Problem
    Sergey Pavlov, Alexey Artemov, Maksim Sharaev, Alexander Bernstein, Evgeny Burnaev
    http://arxiv.org/abs/1911.01738v2

    • [eess.SP]Convolutional Neural Network for Multipath Detection in GNSS Receivers
    Evgenii Munin, Antoine Blais, Nicolas Couellan
    http://arxiv.org/abs/1911.02347v1

    • [math.DS]Permutations With Restricted Movement
    Dor Elimelech, Tom Meyerovitch, Moshe Schwartz
    http://arxiv.org/abs/1911.02233v1

    • [math.NT]Machine Learning meets Number Theory: The Data Science of Birch-Swinnerton-Dyer
    Laura Alessandretti, Andrea Baronchelli, Yang-Hui He
    http://arxiv.org/abs/1911.02008v1

    • [math.OC]Generalized Self-concordant Hessian-barrier algorithms
    Pavel Dvurechensky, Mathias Staudigl, César A. Uribe
    http://arxiv.org/abs/1911.01522v2

    • [math.OC]High-dimensional Black-box Optimization Under Uncertainty
    Hadis Anahideh, Jay Rosenberger, Victoria Chen
    http://arxiv.org/abs/1911.02457v1

    • [math.OC]Linear Support Vector Regression with Linear Constraints
    Quentin Klopfenstein, Samuel Vaiter
    http://arxiv.org/abs/1911.02306v1

    • [math.OC]Resilient Load Restoration in Microgrids Considering Mobile Energy Storage Fleets: A Deep Reinforcement Learning Approach
    Shuhan Yao, Jiuxiang Gu, Peng Wang, Tianyang Zhao, Huajun Zhang, Xiaochuan Liu
    http://arxiv.org/abs/1911.02206v1

    • [math.PR]Weak convergence of empirical Wasserstein type distances
    Philippe Berthet, Jean-Claude Fort
    http://arxiv.org/abs/1911.02389v1

    • [math.ST]A Fourier Analytical Approach to Estimation of Smooth Functions in Gaussian Shift Model
    Fan Zhou, Ping Li
    http://arxiv.org/abs/1911.02010v1

    • [math.ST]Optimal Design of Experiments on Riemannian Manifolds
    Hang Li, Enrique Del Castillo, George Runger
    http://arxiv.org/abs/1911.02192v1

    • [math.ST]Simultaneous estimation of complementary moment independent sensitivity measures for reliability analysis
    Pierre Derennes, Jerome Morio, Florian Simatos
    http://arxiv.org/abs/1911.02488v1

    • [math.ST]The Fourier Transform Method for Volatility Functional Inference by Asynchronous Observations
    Richard Y. Chen
    http://arxiv.org/abs/1911.02205v1

    • [physics.data-an]Randomized Computer Vision Approaches for Pattern Recognition in Timepix and Timepix3 Detectors
    Petr Mánek, Benedikt Bergmann, Petr Burian, Lukáš Meduna, Stanislav Pospíšil, Michal Suk
    http://arxiv.org/abs/1911.02367v1

    • [physics.soc-ph]weg2vec: Event embedding for temporal networks
    Maddalena Torricelli, Márton Karsai, Laetitia Gauvin
    http://arxiv.org/abs/1911.02425v1

    • [q-bio.BM]Using Residual Dipolar Couplings from Two Alignment Media to Detect Structural Homology
    Ryan Yandle, Rishi Mukhopadhyay, Homayoun Valafar
    http://arxiv.org/abs/1911.02396v1

    • [q-bio.QM]Fetal cardiovascular decompensation during labor predicted from the individual heart rate: a prospective study in fetal sheep near term and the impact of low sampling rate
    Nathan Gold, Christophe L. Herry, Xiaogang Wang, Martin G. Frasch
    http://arxiv.org/abs/1911.01304v2

    • [q-fin.CP]Deep Learning for Stock Selection Based on High Frequency Price-Volume Data
    Junming Yang, Yaoqi Li, Xuanyu Chen, Jiahang Cao, Kangkang Jiang
    http://arxiv.org/abs/1911.02502v1

    • [q-fin.PM]Robo-advising: Learning Investor’s Risk Preferences via Portfolio Choices
    Humoud Alsabah, Agostino Capponi, Octavio Ruiz Lacedelli, Matt Stern
    http://arxiv.org/abs/1911.02067v1

    • [q-fin.TR]Modelling bid-ask spread conditional distributions using hierarchical correlation reconstruction
    Jarosław Duda, Robert Syrek, Henryk Gurgul
    http://arxiv.org/abs/1911.02361v1

    • [stat.AP]Modelling extreme claims via composite models and threshold selection methods
    Yinzhi Wang, Ingrid Hobæk Haff, Arne Huseby
    http://arxiv.org/abs/1911.02418v1

    • [stat.AP]The design and statistical aspects of VIETNARMS: a strategic post-licensing trial of multiple oral direct acting antiviral Hepatitis C treatment strategies in Vietnam
    L. McCabe, I. R. White, N. V. Vinh Chau, E. Barnes, S. L. Pett, G. S. Cooke, A. S. Walker
    http://arxiv.org/abs/1911.02272v1

    • [stat.CO]A step further towards automatic and efficient reversible jump algorithms
    Philippe Gagnon
    http://arxiv.org/abs/1911.02089v1

    • [stat.ME]A Comparison of Methods of Inference in Randomized Experiments from a Restricted Set of Allocations
    Junni L. Zhang, Per Johansson
    http://arxiv.org/abs/1911.02197v1

    • [stat.ME]A Conway-Maxwell-Multinomial Distribution for Flexible Modeling of Clustered Categorical Data
    Darcy Steeg Morris, Andrew M. Raim, Kimberly F. Sellers
    http://arxiv.org/abs/1911.02131v1

    • [stat.ME]Bias-aware model selection for machine learning of doubly robust functionals
    Yifan Cui, Eric Tchetgen Tchetgen
    http://arxiv.org/abs/1911.02029v1

    • [stat.ME]Estimation of Spatial Deformation for Nonstationary Processes via Variogram Alignment
    Ghulam A. Qadir, Ying Sun, Sebastian Kurtek
    http://arxiv.org/abs/1911.02249v1

    • [stat.ME]Minimax Nonparametric Parallelism Test
    Xin Xing, Meimei Liu, Ping Ma, Wenxuan Zhong
    http://arxiv.org/abs/1911.02171v1

    • [stat.ME]Regularization of Bayesian shrinkage priors and inference via geometrically / uniformly ergodic Gibbs sampler
    Akihiko Nishimura, Marc A. Suchard
    http://arxiv.org/abs/1911.02160v1

    • [stat.ME]Semiparametric Estimation of Cross-covariance Functions for Multivariate Random Fields
    Ghulam A. Qadir, Ying Sun
    http://arxiv.org/abs/1911.02258v1

    • [stat.ML]An Alternative Probabilistic Interpretation of the Huber Loss
    Gregory P. Meyer
    http://arxiv.org/abs/1911.02088v1

    • [stat.ML]Designing over uncertain outcomes with stochastic sampling Bayesian optimization
    Peter D. Tonner, Daniel V. Samarov, A. Gilad Kusne
    http://arxiv.org/abs/1911.02106v1

    • [stat.ML]Information-Theoretic Generalization Bounds for SGLD via Data-Dependent Estimates
    Jeffrey Negrea, Mahdi Haghifam, Gintare K. Dziugaite, Ashish Khisti, Daniel M. Roy
    http://arxiv.org/abs/1911.02151v1

    • [stat.ML]Integrating Markov processes with structural causal modeling enables counterfactual inference in complex systems
    Robert Osazuwa Ness, Kaushal Paneri, Olga Vitek
    http://arxiv.org/abs/1911.02175v1