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
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• [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