cond-mat.stat-mech - 统计数学

    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.DC - 分布式、并行与集群计算 cs.DS - 数据结构与算法 cs.IR - 信息检索 cs.IT - 信息论 cs.LG - 自动学习 cs.NI - 网络和互联网体系结构 cs.RO - 机器人学 cs.SE - 软件工程 cs.SI - 社交网络与信息网络 econ.EM - 计量经济学 eess.AS - 语音处理 eess.IV - 图像与视频处理 eess.SP - 信号处理 eess.SY - 系统和控制 math.OC - 优化与控制 math.PR - 概率 math.ST - 统计理论 physics.flu-dyn - 流体动力学 physics.soc-ph - 物理学与社会 q-bio.GN - 基因组学 q-bio.QM - 定量方法 q-fin.CP -计算金融学 q-fin.RM - 风险管理 stat.AP - 应用统计 stat.ME - 统计方法论 stat.ML - (统计)机器学习

    • [cond-mat.stat-mech]Imperfect bifurcations in opinion dynamics under external fields
    • [cs.AI]DRiLLS: Deep Reinforcement Learning for Logic Synthesis
    • [cs.AI]HDDL — A Language to Describe Hierarchical Planning Problems
    • [cs.CL]A Stable Variational Autoencoder for Text Modelling
    • [cs.CL]Adapting and evaluating a deep learning language model for clinical why-question answering
    • [cs.CL]Can a Gorilla Ride a Camel? Learning Semantic Plausibility from Text
    • [cs.CL]Creating Auxiliary Representations from Charge Definitions for Criminal Charge Prediction
    • [cs.CL]How to Evaluate Word Representations of Informal Domain?
    • [cs.CL]Improving Robustness of Task Oriented Dialog Systems
    • [cs.CL]LexiPers: An ontology based sentiment lexicon for Persian
    • [cs.CL]Mark my Word: A Sequence-to-Sequence Approach to Definition Modeling
    • [cs.CL]Neural Duplicate Question Detection without Labeled Training Data
    • [cs.CL]Prevalence of code mixing in semi-formal patient communication in low resource languages of South Africa
    • [cs.CL]Robustness to Capitalization Errors in Named Entity Recognition
    • [cs.CR]Blockchain-based System Evaluation: The Effectiveness of Blockchain on E-Procurements
    • [cs.CR]Development of a Secure and Private Electronic Procurement System based on Blockchain Implementation
    • [cs.CR]IStego100K: Large-scale Image Steganalysis Dataset
    • [cs.CR]Optical Proof of Work
    • [cs.CV]CSPN++: Learning Context and Resource Aware Convolutional Spatial Propagation Networks for Depth Completion
    • [cs.CV]Cost-efficient segmentation of electron microscopy images using active learning
    • [cs.CV]Crowd Video Captioning
    • [cs.CV]Deep Multi-task Prediction of Lung Cancer and Cancer-free Progression from Censored Heterogenous Clinical Imaging
    • [cs.CV]DupNet: Towards Very Tiny Quantized CNN with Improved Accuracy for Face Detection
    • [cs.CV]Exploiting Local and Global Structure for Point Cloud Semantic Segmentation with Contextual Point Representations
    • [cs.CV]Extracting 2D weak labels from volume labels using multiple instance learning in CT hemorrhage detection
    • [cs.CV]Image Differential Invariants
    • [cs.CV]Kaolin: A PyTorch Library for Accelerating 3D Deep Learning Research
    • [cs.CV]Knowledge Representing: Efficient, Sparse Representation of Prior Knowledge for Knowledge Distillation
    • [cs.CV]Learning Motion Priors for Efficient Video Object Detection
    • [cs.CV]Location-aware Upsampling for Semantic Segmentation
    • [cs.CV]Momentum Contrast for Unsupervised Visual Representation Learning
    • [cs.CV]Multi-domain CT metal artifacts reduction using partial convolution based inpainting
    • [cs.CV]Real or Fake? Spoofing State-Of-The-Art Face Synthesis Detection Systems
    • [cs.CV]Self-labelling via simultaneous clustering and representation learning
    • [cs.CV]Statistical Deformation Reconstruction Using Multi-organ Shape Features for Pancreatic Cancer Localization
    • [cs.CV]SynSig2Vec: Learning Representations from Synthetic Dynamic Signatures for Real-world Verification
    • [cs.CV]Time-Dynamic Estimates of the Reliability of Deep Semantic Segmentation Networks
    • [cs.CV]Trainable Spectrally Initializable Matrix Transformations in Convolutional Neural Networks
    • [cs.CV]Vehicle Re-identification: exploring feature fusion using multi-stream convolutional networks
    • [cs.DC]Deconstructing Stellar Consensus (Extended Version)
    • [cs.DC]Enhancing Programmability, Portability, and Performance with Rich Cross-Layer Abstractions
    • [cs.DC]HyPar-Flow: Exploiting MPI and Keras for Scalable Hybrid-Parallel DNN Training using TensorFlow
    • [cs.DC]Improving the Space-Time Efficiency of Processor-Oblivious Matrix Multiplication Algorithms
    • [cs.DC]Modeling Constrained Preemption Dynamics Of Transient Cloud Servers
    • [cs.DC]Oblivious Permutations on the Plane
    • [cs.DS]Enumerative Data Compression with Non-Uniquely Decodable Codes
    • [cs.DS]Nested Dataflow Algorithms for Dynamic Programming Recurrences with more than O(1) Dependency
    • [cs.IR]All It Takes is 20 Questions!: A Knowledge Graph Based Approach
    • [cs.IR]Allowing for equal opportunities for artists in music recommendation
    • [cs.IR]Identification of Rhetorical Roles of Sentences in Indian Legal Judgments
    • [cs.IT]Buffer-aware Wireless Scheduling based on Deep Reinforcement Learning
    • [cs.IT]Coalition Formation Game for Delay Reduction in Instantly Decodable Network Coding Assisted D2D Communications
    • [cs.IT]Energy-Efficient UAV Backscatter Communication with Joint Trajectory Design and Resource Optimization
    • [cs.IT]High Reliability Downlink MU-MIMO: New OSTBC Approach and Superposition Modulated Side Information
    • [cs.IT]Multi-Purpose Aerial Drones for Network Coverage and Package Delivery
    • [cs.IT]On the Constructions of MDS Self-dual Codes via Cyclotomy
    • [cs.IT]On the Degrees of Freedom of the MISO Interference Broadcast Channel with Delayed CSIT
    • [cs.IT]Proofs of conservation inequalities for Levin’s notion of mutual information of 1974
    • [cs.IT]Searching for Anomalies over Composite Hypotheses
    • [cs.IT]Single-Error Detection and Correction for Duplication and Substitution Channels
    • [cs.IT]The Strength of Connectivity of Random Graphs induced by Pairwise Key Predistribution Schemes: Implications on Security and Reliability of Heterogeneous Sensor Networks
    • [cs.LG]92c/MFlops/s, Ultra-Large-Scale Neural-Network Training on a PIII Cluster
    • [cs.LG]A Convergent Off-Policy Temporal Difference Algorithm
    • [cs.LG]A Hierarchy of Graph Neural Networks Based on Learnable Local Features
    • [cs.LG]Accelerating Training in Pommerman with Imitation and Reinforcement Learning
    • [cs.LG]Adversarial Examples in Modern Machine Learning: A Review
    • [cs.LG]Asynchronous Distributed Learning from Constraints
    • [cs.LG]Avoiding hashing and encouraging visual semantics in referential emergent language games
    • [cs.LG]CHEETAH: An Ultra-Fast, Approximation-Free, and Privacy-Preserved Neural Network Framework based on Joint Obscure Linear and Nonlinear Computations
    • [cs.LG]Clustering by Directly Disentangling Latent Space
    • [cs.LG]Collaborative Distillation for Top-N Recommendation
    • [cs.LG]Compressive Transformers for Long-Range Sequence Modelling
    • [cs.LG]Constant Curvature Graph Convolutional Networks
    • [cs.LG]Context-aware Dynamic Assets Selection for Online Portfolio Selection based on Contextual Bandit
    • [cs.LG]Detecting Patterns of Physiological Response to Hemodynamic Stress via Unsupervised Deep Learning
    • [cs.LG]Dynamic Connected Neural Decision Classifier and Regressor with Dynamic Softing Pruning
    • [cs.LG]Fair Adversarial Gradient Tree Boosting
    • [cs.LG]Finding Social Media Trolls: Dynamic Keyword Selection Methods for Rapidly-Evolving Online Debates
    • [cs.LG]Graph Representation Learning via Multi-task Knowledge Distillation
    • [cs.LG]Incentivized Exploration for Multi-Armed Bandits under Reward Drift
    • [cs.LG]Learning From Brains How to Regularize Machines
    • [cs.LG]Learning Non-Parametric Invariances from Data with Permanent Random Connectomes
    • [cs.LG]Learning Relationships between Text, Audio, and Video via Deep Canonical Correlation for Multimodal Language Analysis
    • [cs.LG]Learning Representations in Reinforcement Learning:An Information Bottleneck Approach
    • [cs.LG]Learning from a Teacher using Unlabeled Data
    • [cs.LG]Learning to Communicate in Multi-Agent Reinforcement Learning : A Review
    • [cs.LG]MIDAS: Microcluster-Based Detector of Anomalies in Edge Streams
    • [cs.LG]Model-Based Reinforcement Learning with Adversarial Training for Online Recommendation
    • [cs.LG]Modeling patterns of smartphone usage and their relationship to cognitive health
    • [cs.LG]Negative sampling in semi-supervised learning
    • [cs.LG]On the Shattering Coefficient of Supervised Learning Algorithms
    • [cs.LG]Predicting microRNA-disease associations from knowledge graph using tensor decomposition with relational constraints
    • [cs.LG]Predictive Multi-level Patient Representations from Electronic Health Records
    • [cs.LG]Regression via Arbitrary Quantile Modeling
    • [cs.LG]SAVEHR: Self Attention Vector Representations for EHR based Personalized Chronic Disease Onset Prediction and Interpretability
    • [cs.LG]Schedule Earth Observation satellites with Deep Reinforcement Learning
    • [cs.LG]Selective Brain Damage: Measuring the Disparate Impact of Model Pruning
    • [cs.LG]Self-supervised representation learning from electroencephalography signals
    • [cs.LG]Streaming Bayesian Inference for Crowdsourced Classification
    • [cs.LG]Structured Sparsification of Gated Recurrent Neural Networks
    • [cs.LG]The Deep Learning Revolution and Its Implications for Computer Architecture and Chip Design
    • [cs.LG]Time-Aware Prospective Modeling of Users for Online Display Advertising
    • [cs.LG]Topological Stability: Guided Determination of the Nearest Neighbors in Non-Linear Dimensionality Reduction Techniques
    • [cs.LG]Transfer Value Iteration Networks
    • [cs.LG]Uncertainty on Asynchronous Time Event Prediction
    • [cs.LG]ZiMM: a deep learning model for long term adverse events with non-clinical claims data
    • [cs.NI]Age-Delay Tradeoffs in Queueing Systems
    • [cs.NI]MOTH- Mobility-induced Outages in THz: A Beyond 5G (B5G) application
    • [cs.NI]MSDF: A Deep Reinforcement Learning Framework for Service Function Chain Migration
    • [cs.RO]Are We Ready for Service Robots? The OpenLORIS-Scene Datasets for Lifelong SLAM
    • [cs.RO]Deep Reinforcement Learning Attitude Control of Fixed-Wing UAVs Using Proximal Policy Optimization
    • [cs.RO]IRIS: Implicit Reinforcement without Interaction at Scale for Learning Control from Offline Robot Manipulation Data
    • [cs.RO]Numerical and experimental realization of analytical SLAM
    • [cs.RO]Pose estimation and bin picking for deformable products
    • [cs.SE]Reinforcement Learning-Driven Test Generation for Android GUI Applications using Formal Specifications
    • [cs.SI]Classifying Relevant Social Media Posts During Disasters Using Ensemble of Domain-agnostic and Domain-specific Word Embeddings
    • [cs.SI]False positives using social cognitive mapping to identify childrens’ peer groups
    • [cs.SI]Generation and Classification of Activity Sequences for Spatiotemporal Modeling of Human Populations
    • [cs.SI]Multi-MotifGAN (MMGAN): Motif-targeted Graph Generation and Prediction
    • [cs.SI]On the choice of graph neural network architectures
    • [econ.EM]Randomization tests of copula symmetry
    • [eess.AS]’Warriors of the Word’ — Deciphering Lyrical Topics in Music and Their Connection to Audio Feature Dimensions Based on a Corpus of Over 100,000 Metal Songs
    • [eess.AS]3-D Feature and Acoustic Modeling for Far-Field Speech Recognition
    • [eess.IV]Automatic Frame Selection Using MLP Neural Network in Ultrasound Elastography
    • [eess.IV]Fast Approximate Time-Delay Estimation in Ultrasound Elastography Using Principal Component Analysis
    • [eess.IV]Semi-Supervised Multi-Organ Segmentation through Quality Assurance Supervision
    • [eess.IV]Unsupervised Medical Image Segmentation with Adversarial Networks: From Edge Diagrams to Segmentation Maps
    • [eess.SP]IMNet: A Learning Based Detector for Index Modulation Aided MIMO-OFDM Systems
    • [eess.SP]Real-Time Anomaly Detection for Advanced Manufacturing: Improving on Twitter’s State of the Art
    • [eess.SP]Real-time ultra-low power ECG anomaly detection using an event-driven neuromorphic processor
    • [eess.SY]Energy-Efficient Beamforming and Cooperative Jamming in IRS-Assisted MISO Networks
    • [math.OC]Quadratic number of nodes is sufficient to learn a dataset via gradient descent
    • [math.OC]Shadowing Properties of Optimization Algorithms
    • [math.OC]Tropical Optimal Transport and Wasserstein Distances in Phylogenetic Tree Space
    • [math.PR]Mean and Variance of Brownian Motion with Given Final Value, Maximum and ArgMax: Extended Version
    • [math.PR]The Value of the High, Low and Close in the Estimation of Brownian Motion: Extended Version
    • [math.ST]Error bounds for some approximate posterior measures in Bayesian inference
    • [math.ST]Estimation after selection from bivariate normal population using LINEX loss function
    • [math.ST]Improved Concentration Bounds for Gaussian Quadratic Forms
    • [math.ST]Kriging prediction with isotropic Matérn correlations: robustness and experimental design
    • [math.ST]Optimality regions for designs in multiple linear regression models with correlated random coefficients
    • [physics.flu-dyn]Deep learning velocity signals allows to quantify turbulence intensity
    • [physics.soc-ph]Coordination Group Formation for OnLine Coordinated Routing Mechanisms
    • [q-bio.GN]A Graph Auto-Encoder for Haplotype Assembly and Viral Quasispecies Reconstruction
    • [q-bio.GN]Learning from Data-Rich Problems: A Case Study on Genetic Variant Calling
    • [q-bio.QM]AMPL: A Data-Driven Modeling Pipeline for Drug Discovery
    • [q-bio.QM]DARTS: DenseUnet-based Automatic Rapid Tool for brain Segmentation
    • [q-fin.CP]Neural networks for option pricing and hedging: a literature review
    • [q-fin.RM]An Unethical Optimization Principle
    • [stat.AP]Causality-based tests to detect the influence of confounders on mobile health diagnostic applications: a comparison with restricted permutations
    • [stat.AP]Human Immunodeficiency Virus(HIV) Cases in the Philippines: Analysis and Forecasting
    • [stat.AP]Identifying predictive biomarkers of CIMAvaxEGF success in advanced Lung Cancer Patients
    • [stat.AP]The effect of geographic sampling on extreme precipitation: from models to observations and back again
    • [stat.ME]A Bayesian hierarchical model for bridging across patient subgroups in phase I clinical trials with animal data
    • [stat.ME]A Simulation-free Group Sequential Design with Max-combo Tests in the Presence of Non-proportional Hazards
    • [stat.ME]Anomaly Detection in Large Scale Networks with Latent Space Models
    • [stat.ME]Balanced Policy Evaluation and Learning for Right Censored Data
    • [stat.ME]Robust Fitting for Generalized Additive Models for Location, Scale and Shape
    • [stat.ME]Sparse Linear Discriminant Analysis for Multi-view Structured Data
    • [stat.ML]Exponential Convergence Rates of Classification Errors on Learning with SGD and Random Features
    • [stat.ML]Harmonic Mean Point Processes: Proportional Rate Error Minimization for Obtundation Prediction
    • [stat.ML]Nonconvex Stochastic Nested Optimization via Stochastic ADMM

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

    • [cond-mat.stat-mech]Imperfect bifurcations in opinion dynamics under external fields
    Francisco Freitas, Allan R. Vieira, Celia Anteneodo
    http://arxiv.org/abs/1911.05124v1

    • [cs.AI]DRiLLS: Deep Reinforcement Learning for Logic Synthesis
    Abdelrahman Hosny, Soheil Hashemi, Mohamed Shalan, Sherief Reda
    http://arxiv.org/abs/1911.04021v2

    • [cs.AI]HDDL — A Language to Describe Hierarchical Planning Problems
    D. Höller, G. Behnke, P. Bercher, S. Biundo, H. Fiorino, D. Pellier, R. Alford
    http://arxiv.org/abs/1911.05499v1

    • [cs.CL]A Stable Variational Autoencoder for Text Modelling
    Ruizhe Li, Xiao Li, Chenghua Lin, Matthew Collinson, Rui Mao
    http://arxiv.org/abs/1911.05343v1

    • [cs.CL]Adapting and evaluating a deep learning language model for clinical why-question answering
    Andrew Wen, Mohamed Y. Elwazir, Sungrim Moon, Jungwei Fan
    http://arxiv.org/abs/1911.05604v1

    • [cs.CL]Can a Gorilla Ride a Camel? Learning Semantic Plausibility from Text
    Ian Porada, Kaheer Suleman, Jackie Chi Kit Cheung
    http://arxiv.org/abs/1911.05689v1

    • [cs.CL]Creating Auxiliary Representations from Charge Definitions for Criminal Charge Prediction
    Liangyi Kang, Jie Liu, Lingqiao Liu, Qinfeng Shi, Dan Ye
    http://arxiv.org/abs/1911.05202v1

    • [cs.CL]How to Evaluate Word Representations of Informal Domain?
    Yekun Chai, Naomi Saphra, Adam Lopez
    http://arxiv.org/abs/1911.04669v2

    • [cs.CL]Improving Robustness of Task Oriented Dialog Systems
    Arash Einolghozati, Sonal Gupta, Mrinal Mohit, Rushin Shah
    http://arxiv.org/abs/1911.05153v1

    • [cs.CL]LexiPers: An ontology based sentiment lexicon for Persian
    Behnam Sabeti, Pedram Hosseini, Gholamreza Ghassem-Sani, Seyed Abolghasem Mirroshandel
    http://arxiv.org/abs/1911.05263v1

    • [cs.CL]Mark my Word: A Sequence-to-Sequence Approach to Definition Modeling
    Timothee Mickus, Denis Paperno, Mathieu Constant
    http://arxiv.org/abs/1911.05715v1

    • [cs.CL]Neural Duplicate Question Detection without Labeled Training Data
    Andreas Rücklé, Nafise Sadat Moosavi, Iryna Gurevych
    http://arxiv.org/abs/1911.05594v1

    • [cs.CL]Prevalence of code mixing in semi-formal patient communication in low resource languages of South Africa
    Monicka Obrocka, Charles Copley, Themba Gqaza, Elizabeth Grant
    http://arxiv.org/abs/1911.05636v1

    • [cs.CL]Robustness to Capitalization Errors in Named Entity Recognition
    Sravan Bodapati, Hyokun Yun, Yaser Al-Onaizan
    http://arxiv.org/abs/1911.05241v1

    • [cs.CR]Blockchain-based System Evaluation: The Effectiveness of Blockchain on E-Procurements
    August Thio-ac, Alfred Keanu Serut, Rayn Louise Torrejos, Keenan Dave Rivo, Jessica Velasco
    http://arxiv.org/abs/1911.05399v1

    • [cs.CR]Development of a Secure and Private Electronic Procurement System based on Blockchain Implementation
    August Thio-ac, Erwin John Domingo, Ricca May Reyes, Nilo Arago, Romeo Jr. Jorda, Jessica Velasco
    http://arxiv.org/abs/1911.05391v1

    • [cs.CR]IStego100K: Large-scale Image Steganalysis Dataset
    Zhongliang Yang, Ke Wang, Sai Ma, Yongfeng Huang, Xiangui Kang, Xianfeng Zhao
    http://arxiv.org/abs/1911.05542v1

    • [cs.CR]Optical Proof of Work
    Michael Dubrovsky, Marshall Ball, Bogdan Penkovsky
    http://arxiv.org/abs/1911.05193v1

    • [cs.CV]CSPN++: Learning Context and Resource Aware Convolutional Spatial Propagation Networks for Depth Completion
    Xinjing Cheng, Peng Wang, Chenye Guan, Ruigang Yang
    http://arxiv.org/abs/1911.05377v1

    • [cs.CV]Cost-efficient segmentation of electron microscopy images using active learning
    Joris Roels, Yvan Saeys
    http://arxiv.org/abs/1911.05548v1

    • [cs.CV]Crowd Video Captioning
    Liqi Yan, Mingjian Zhu, Changbin Yu
    http://arxiv.org/abs/1911.05449v1

    • [cs.CV]Deep Multi-task Prediction of Lung Cancer and Cancer-free Progression from Censored Heterogenous Clinical Imaging
    Riqiang Gao, Lingfeng Li, Yucheng Tang, Sanja L. Antic, Alexis B. Paulson, Yuankai Huo, Kim L. Sandler, Pierre P. Massion, Bennett A. Landman
    http://arxiv.org/abs/1911.05115v1

    • [cs.CV]DupNet: Towards Very Tiny Quantized CNN with Improved Accuracy for Face Detection
    Hongxing Gao, Wei Tao, Dongchao Wen, Junjie Liu, Tse-Wei Chen, Kinya Osa, Masami Kato
    http://arxiv.org/abs/1911.05341v1

    • [cs.CV]Exploiting Local and Global Structure for Point Cloud Semantic Segmentation with Contextual Point Representations
    Xu Wang, Jingming He, Lin Ma
    http://arxiv.org/abs/1911.05277v1

    • [cs.CV]Extracting 2D weak labels from volume labels using multiple instance learning in CT hemorrhage detection
    Samuel W. Remedios, Zihao Wu, Camilo Bermudez, Cailey I. Kerley, Snehashis Roy, Mayur B. Patel, John A. Butman, Bennett A. Landman, Dzung L. Pham
    http://arxiv.org/abs/1911.05650v1

    • [cs.CV]Image Differential Invariants
    Hanlin Mo, Hua Li
    http://arxiv.org/abs/1911.05327v1

    • [cs.CV]Kaolin: A PyTorch Library for Accelerating 3D Deep Learning Research
    Krishna Murthy Jatavallabhula, Edward Smith, Jean-Francois Lafleche, Clement Fuji Tsang, Artem Rozantsev, Wenzheng Chen, Tommy Xiang, Rev Lebaredian, Sanja Fidler
    http://arxiv.org/abs/1911.05063v2

    • [cs.CV]Knowledge Representing: Efficient, Sparse Representation of Prior Knowledge for Knowledge Distillation
    Junjie Liu, Dongchao Wen, Hongxing Gao, Wei Tao, Tse-Wei Chen, Kinya Osa, Masami Kato
    http://arxiv.org/abs/1911.05329v1

    • [cs.CV]Learning Motion Priors for Efficient Video Object Detection
    Zhengkai Jiang, Yu Liu, Ceyuan Yang, Jihao Liu, Qian Zhang, Shiming Xiang, Chunhong Pan
    http://arxiv.org/abs/1911.05253v1

    • [cs.CV]Location-aware Upsampling for Semantic Segmentation
    Xiangyu He, Zitao Mo, Qiang Chen, Anda Cheng, Peisong Wang, Jian Cheng
    http://arxiv.org/abs/1911.05250v1

    • [cs.CV]Momentum Contrast for Unsupervised Visual Representation Learning
    Kaiming He, Haoqi Fan, Yuxin Wu, Saining Xie, Ross Girshick
    http://arxiv.org/abs/1911.05722v1

    • [cs.CV]Multi-domain CT metal artifacts reduction using partial convolution based inpainting
    Artem Pimkin, Alexander Samoylenko, Natalia Antipina, Anna Ovechkina, Andrey Golanov, Alexandra Dalechina, Mikhail Belyaev
    http://arxiv.org/abs/1911.05530v1

    • [cs.CV]Real or Fake? Spoofing State-Of-The-Art Face Synthesis Detection Systems
    João C. Neves, Ruben Tolosana, Ruben Vera-Rodriguez, Vasco Lopes, Hugo Proença
    http://arxiv.org/abs/1911.05351v1

    • [cs.CV]Self-labelling via simultaneous clustering and representation learning
    Yuki Markus Asano, Christian Rupprecht, Andrea Vedaldi
    http://arxiv.org/abs/1911.05371v1

    • [cs.CV]Statistical Deformation Reconstruction Using Multi-organ Shape Features for Pancreatic Cancer Localization
    Megumi Nakao, Mitsuhiro Nakamura, Takashi Mizowaki, Tetsuya Matsuda
    http://arxiv.org/abs/1911.05439v1

    • [cs.CV]SynSig2Vec: Learning Representations from Synthetic Dynamic Signatures for Real-world Verification
    Songxuan Lai, Lianwen Jin, Luojun Lin, Yecheng Zhu, Huiyun Mao
    http://arxiv.org/abs/1911.05358v1

    • [cs.CV]Time-Dynamic Estimates of the Reliability of Deep Semantic Segmentation Networks
    Kira Maag, Matthias Rottmann, Hanno Gottschalk
    http://arxiv.org/abs/1911.05075v1

    • [cs.CV]Trainable Spectrally Initializable Matrix Transformations in Convolutional Neural Networks
    Michele Alberti, Angela Botros, Narayan Schuez, Rolf Ingold, Marcus Liwicki, Mathias Seuret
    http://arxiv.org/abs/1911.05045v2

    • [cs.CV]Vehicle Re-identification: exploring feature fusion using multi-stream convolutional networks
    Icaro O. de Oliveira, Rayson Laroca, David Menotti, Keiko V. O. Fonseca, Rodrigo Minetto
    http://arxiv.org/abs/1911.05541v1

    • [cs.DC]Deconstructing Stellar Consensus (Extended Version)
    Álvaro García-Pérez, Maria A. Schett
    http://arxiv.org/abs/1911.05145v1

    • [cs.DC]Enhancing Programmability, Portability, and Performance with Rich Cross-Layer Abstractions
    Nandita Vijaykumar
    http://arxiv.org/abs/1911.05660v1

    • [cs.DC]HyPar-Flow: Exploiting MPI and Keras for Scalable Hybrid-Parallel DNN Training using TensorFlow
    Ammar Ahmad Awan, Arpan Jain, Quentin Anthony, Hari Subramoni, Dhabaleswar K., Panda
    http://arxiv.org/abs/1911.05146v1

    • [cs.DC]Improving the Space-Time Efficiency of Processor-Oblivious Matrix Multiplication Algorithms
    Yuan Tang
    http://arxiv.org/abs/1911.05328v1

    • [cs.DC]Modeling Constrained Preemption Dynamics Of Transient Cloud Servers
    Prateek Sharma, JCS Kadupitiya, Vikram Jadhao
    http://arxiv.org/abs/1911.05160v1

    • [cs.DC]Oblivious Permutations on the Plane
    Shantanu Das, Giuseppe A. Di Luna, Paola Flocchini, Nicola Santoro, Giovanni Viglietta, Masafumi Yamashita
    http://arxiv.org/abs/1911.05239v1

    • [cs.DS]Enumerative Data Compression with Non-Uniquely Decodable Codes
    M. Oğuzhan Külekci, Yasin Öztürk, Elif Altunok, Can Altıniğne
    http://arxiv.org/abs/1911.05676v1

    • [cs.DS]Nested Dataflow Algorithms for Dynamic Programming Recurrences with more than O(1) Dependency
    Yuan Tang
    http://arxiv.org/abs/1911.05333v1

    • [cs.IR]All It Takes is 20 Questions!: A Knowledge Graph Based Approach
    Alvin Dey, Harsh Kumar Jain, Vikash Kumar Pandey, Tanmoy Chakraborty
    http://arxiv.org/abs/1911.05161v1

    • [cs.IR]Allowing for equal opportunities for artists in music recommendation
    Christine Bauer
    http://arxiv.org/abs/1911.05395v1

    • [cs.IR]Identification of Rhetorical Roles of Sentences in Indian Legal Judgments
    Paheli Bhattacharya, Shounak Paul, Kripabandhu Ghosh, Saptarshi Ghosh, Adam Wyner
    http://arxiv.org/abs/1911.05405v1

    • [cs.IT]Buffer-aware Wireless Scheduling based on Deep Reinforcement Learning
    Chen Xu, Jian Wang, Tianhang Yu, Chuili Kong, Yourui Huangfu, Rong Li, Yiqun Ge, Jun Wang
    http://arxiv.org/abs/1911.05281v1

    • [cs.IT]Coalition Formation Game for Delay Reduction in Instantly Decodable Network Coding Assisted D2D Communications
    Mohammed S. Al-Abiad, Ahmed Douik, Md. J. Hossain
    http://arxiv.org/abs/1911.05201v1

    • [cs.IT]Energy-Efficient UAV Backscatter Communication with Joint Trajectory Design and Resource Optimization
    Gang Yang, Rao Dai, Ying-Chang Liang
    http://arxiv.org/abs/1911.05553v1

    • [cs.IT]High Reliability Downlink MU-MIMO: New OSTBC Approach and Superposition Modulated Side Information
    Nora Boulaioune, Nandana Rajatheva, Matti Latva-aho
    http://arxiv.org/abs/1911.05347v1

    • [cs.IT]Multi-Purpose Aerial Drones for Network Coverage and Package Delivery
    Mohammadjavad Khosravi, Hamid Saeedi, Hossein Pishro-Nik
    http://arxiv.org/abs/1911.05624v1

    • [cs.IT]On the Constructions of MDS Self-dual Codes via Cyclotomy
    Aixian Zhang, Keqin Feng
    http://arxiv.org/abs/1911.05234v1

    • [cs.IT]On the Degrees of Freedom of the MISO Interference Broadcast Channel with Delayed CSIT
    Marc Torrellas, Adrian Agustin, Josep Vidal
    http://arxiv.org/abs/1911.05535v1

    • [cs.IT]Proofs of conservation inequalities for Levin’s notion of mutual information of 1974
    Nikolay Vereshchagin
    http://arxiv.org/abs/1911.05447v1

    • [cs.IT]Searching for Anomalies over Composite Hypotheses
    Bar Hemo, Tomer Gafni, Kobi Cohen, Qing Zhao
    http://arxiv.org/abs/1911.05381v1

    • [cs.IT]Single-Error Detection and Correction for Duplication and Substitution Channels
    Yuanyuan Tang, Yonatan Yehezkeally, Moshe Schwartz, Farzad Farnoud
    http://arxiv.org/abs/1911.05413v1

    • [cs.IT]The Strength of Connectivity of Random Graphs induced by Pairwise Key Predistribution Schemes: Implications on Security and Reliability of Heterogeneous Sensor Networks
    Mansi Sood, Osman Yağan
    http://arxiv.org/abs/1911.05147v1

    • [cs.LG]92c/MFlops/s, Ultra-Large-Scale Neural-Network Training on a PIII Cluster
    Douglas Aberdeen, Jonathan Baxter, Robert Edwards
    http://arxiv.org/abs/1911.05181v1

    • [cs.LG]A Convergent Off-Policy Temporal Difference Algorithm
    Raghuram Bharadwaj Diddigi, Chandramouli Kamanchi, Shalabh Bhatnagar
    http://arxiv.org/abs/1911.05697v1

    • [cs.LG]A Hierarchy of Graph Neural Networks Based on Learnable Local Features
    Michael Lingzhi Li, Meng Dong, Jiawei Zhou, Alexander M. Rush
    http://arxiv.org/abs/1911.05256v1

    • [cs.LG]Accelerating Training in Pommerman with Imitation and Reinforcement Learning
    Hardik Meisheri, Omkar Shelke, Richa Verma, Harshad Khadilkar
    http://arxiv.org/abs/1911.04947v2

    • [cs.LG]Adversarial Examples in Modern Machine Learning: A Review
    Rey Reza Wiyatno, Anqi Xu, Ousmane Dia, Archy de Berker
    http://arxiv.org/abs/1911.05268v1

    • [cs.LG]Asynchronous Distributed Learning from Constraints
    Francesco Farina, Stefano Melacci, Andrea Garulli, Antonio Giannitrapani
    http://arxiv.org/abs/1911.05473v1

    • [cs.LG]Avoiding hashing and encouraging visual semantics in referential emergent language games
    Daniela Mihai, Jonathon Hare
    http://arxiv.org/abs/1911.05546v1

    • [cs.LG]CHEETAH: An Ultra-Fast, Approximation-Free, and Privacy-Preserved Neural Network Framework based on Joint Obscure Linear and Nonlinear Computations
    Qiao Zhang, Cong Wang, Chunsheng Xin, Hongyi Wu
    http://arxiv.org/abs/1911.05184v1

    • [cs.LG]Clustering by Directly Disentangling Latent Space
    Fei Ding, Feng Luo
    http://arxiv.org/abs/1911.05210v1

    • [cs.LG]Collaborative Distillation for Top-N Recommendation
    Jae-woong Lee, Minjin Choi, Jongwuk Lee, Hyunjung Shim
    http://arxiv.org/abs/1911.05276v1

    • [cs.LG]Compressive Transformers for Long-Range Sequence Modelling
    Jack W. Rae, Anna Potapenko, Siddhant M. Jayakumar, Timothy P. Lillicrap
    http://arxiv.org/abs/1911.05507v1

    • [cs.LG]Constant Curvature Graph Convolutional Networks
    Gregor Bachmann, Gary Bécigneul, Octavian-Eugen Ganea
    http://arxiv.org/abs/1911.05076v1

    • [cs.LG]Context-aware Dynamic Assets Selection for Online Portfolio Selection based on Contextual Bandit
    Mengying Zhu, Xiaolin Zheng, Yan Wang, Yuyuan Li, Qianqiao Liang
    http://arxiv.org/abs/1911.05309v1

    • [cs.LG]Detecting Patterns of Physiological Response to Hemodynamic Stress via Unsupervised Deep Learning
    Chufan Gao, Fabian Falck, Mononito Goswami, Anthony Wertz, Michael R. Pinsky, Artur Dubrawski
    http://arxiv.org/abs/1911.05121v1

    • [cs.LG]Dynamic Connected Neural Decision Classifier and Regressor with Dynamic Softing Pruning
    Faen Zhang, Xinyu Fan, Hui Xu, Pengcheng Zhou, Yujian He, Junlong Liu
    http://arxiv.org/abs/1911.05443v1

    • [cs.LG]Fair Adversarial Gradient Tree Boosting
    Vincent Grari, Boris Ruf, Sylvain Lamprier, Marcin Detyniecki
    http://arxiv.org/abs/1911.05369v1

    • [cs.LG]Finding Social Media Trolls: Dynamic Keyword Selection Methods for Rapidly-Evolving Online Debates
    Anqi Liu, Maya Srikanth, Nicholas Adams-Cohen, R. Michael Alvarez, Anima Anandkumar
    http://arxiv.org/abs/1911.05332v1

    • [cs.LG]Graph Representation Learning via Multi-task Knowledge Distillation
    Jiaqi Ma, Qiaozhu Mei
    http://arxiv.org/abs/1911.05700v1

    • [cs.LG]Incentivized Exploration for Multi-Armed Bandits under Reward Drift
    Zhiyuan Liu, Huazheng Wang, Fan Shen, Kai Liu, Lijun Chen
    http://arxiv.org/abs/1911.05142v1

    • [cs.LG]Learning From Brains How to Regularize Machines
    Zhe Li, Wieland Brendel, Edgar Y. Walker, Erick Cobos, Taliah Muhammad, Jacob Reimer, Matthias Bethge, Fabian H. Sinz, Xaq Pitkow, Andreas S. Tolias
    http://arxiv.org/abs/1911.05072v1

    • [cs.LG]Learning Non-Parametric Invariances from Data with Permanent Random Connectomes
    Dipan K. Pal, Marios Savvides
    http://arxiv.org/abs/1911.05266v1

    • [cs.LG]Learning Relationships between Text, Audio, and Video via Deep Canonical Correlation for Multimodal Language Analysis
    Zhongkai Sun, Prathusha Sarma, William Sethares, Yingyu Liang
    http://arxiv.org/abs/1911.05544v1

    • [cs.LG]Learning Representations in Reinforcement Learning:An Information Bottleneck Approach
    Pei Yingjun, Hou Xinwen
    http://arxiv.org/abs/1911.05695v1

    • [cs.LG]Learning from a Teacher using Unlabeled Data
    Gaurav Menghani, Sujith Ravi
    http://arxiv.org/abs/1911.05275v1

    • [cs.LG]Learning to Communicate in Multi-Agent Reinforcement Learning : A Review
    Mohamed Salah Zaïem, Etienne Bennequin
    http://arxiv.org/abs/1911.05438v1

    • [cs.LG]MIDAS: Microcluster-Based Detector of Anomalies in Edge Streams
    Siddharth Bhatia, Bryan Hooi, Minji Yoon, Kijung Shin, Christos Faloutsos
    http://arxiv.org/abs/1911.04464v2

    • [cs.LG]Model-Based Reinforcement Learning with Adversarial Training for Online Recommendation
    Xueying Bai, Jian Guan, Hongning Wang
    http://arxiv.org/abs/1911.03845v2

    • [cs.LG]Modeling patterns of smartphone usage and their relationship to cognitive health
    Jonas Rauber, Emily B. Fox, Leon A. Gatys
    http://arxiv.org/abs/1911.05683v1

    • [cs.LG]Negative sampling in semi-supervised learning
    John Chen, Vatsal Shah, Anastasios Kyrillidis
    http://arxiv.org/abs/1911.05166v1

    • [cs.LG]On the Shattering Coefficient of Supervised Learning Algorithms
    Rodrigo Fernandes de Mello
    http://arxiv.org/abs/1911.05461v1

    • [cs.LG]Predicting microRNA-disease associations from knowledge graph using tensor decomposition with relational constraints
    Feng Huang, Zhankun Xiong, Guan Zhang, Zhouxin Yu, Xinran Xu, Wen Zhang
    http://arxiv.org/abs/1911.05584v1

    • [cs.LG]Predictive Multi-level Patient Representations from Electronic Health Records
    Zichang Wang, Haoran Li, Luchen Liu, Haoxian Wu, Ming Zhang
    http://arxiv.org/abs/1911.05698v1

    • [cs.LG]Regression via Arbitrary Quantile Modeling
    Faen Zhang, Xinyu Fan, Hui Xu, Pengcheng Zhou, Yujian He, Junlong Liu
    http://arxiv.org/abs/1911.05441v1

    • [cs.LG]SAVEHR: Self Attention Vector Representations for EHR based Personalized Chronic Disease Onset Prediction and Interpretability
    Sunil Mallya, Marc Overhage, Sravan Bodapati, Navneet Srivastava, Sahika Genc
    http://arxiv.org/abs/1911.05370v1

    • [cs.LG]Schedule Earth Observation satellites with Deep Reinforcement Learning
    Adrien Hadj-Salah, Rémi Verdier, Clément Caron, Mathieu Picard, Mikaël Capelle
    http://arxiv.org/abs/1911.05696v1

    • [cs.LG]Selective Brain Damage: Measuring the Disparate Impact of Model Pruning
    Sara Hooker, Aaron Courville, Yann Dauphin, Andrea Frome
    http://arxiv.org/abs/1911.05248v1

    • [cs.LG]Self-supervised representation learning from electroencephalography signals
    Hubert Banville, Isabela Albuquerque, Aapo Hyvärinen, Graeme Moffat, Denis-Alexander Engemann, Alexandre Gramfort
    http://arxiv.org/abs/1911.05419v1

    • [cs.LG]Streaming Bayesian Inference for Crowdsourced Classification
    Edoardo Manino, Long Tran-Thanh, Nicholas R. Jennings
    http://arxiv.org/abs/1911.05712v1

    • [cs.LG]Structured Sparsification of Gated Recurrent Neural Networks
    Ekaterina Lobacheva, Nadezhda Chirkova, Alexander Markovich, Dmitry Vetrov
    http://arxiv.org/abs/1911.05585v1

    • [cs.LG]The Deep Learning Revolution and Its Implications for Computer Architecture and Chip Design
    Jeffrey Dean
    http://arxiv.org/abs/1911.05289v1

    • [cs.LG]Time-Aware Prospective Modeling of Users for Online Display Advertising
    Djordje Gligorijevic, Jelena Gligorijevic, Aaron Flores
    http://arxiv.org/abs/1911.05100v1

    • [cs.LG]Topological Stability: Guided Determination of the Nearest Neighbors in Non-Linear Dimensionality Reduction Techniques
    Mohammed Elhenawy
    http://arxiv.org/abs/1911.05312v1

    • [cs.LG]Transfer Value Iteration Networks
    Junyi Shen, Hankz Hankui Zhuo, Jin Xu, Bin Zhong, Sinno Jialin Pan
    http://arxiv.org/abs/1911.05701v1

    • [cs.LG]Uncertainty on Asynchronous Time Event Prediction
    Marin Biloš, Bertrand Charpentier, Stephan Günnemann
    http://arxiv.org/abs/1911.05503v1

    • [cs.LG]ZiMM: a deep learning model for long term adverse events with non-clinical claims data
    Emmanuel Bacry, Stéphane Gaïffas, Anastasiia Kabeshova, Yiyang Yu
    http://arxiv.org/abs/1911.05346v1

    • [cs.NI]Age-Delay Tradeoffs in Queueing Systems
    Rajat Talak, Eytan Modiano
    http://arxiv.org/abs/1911.05601v1

    • [cs.NI]MOTH- Mobility-induced Outages in THz: A Beyond 5G (B5G) application
    Rohit Singh, Douglas Sicker, Kazi Mohammed Saidul Huq
    http://arxiv.org/abs/1911.05589v1

    • [cs.NI]MSDF: A Deep Reinforcement Learning Framework for Service Function Chain Migration
    Ruoyun Chen, Hancheng Lu, Yujiao Lu, Jinxue Liu
    http://arxiv.org/abs/1911.04801v2

    • [cs.RO]Are We Ready for Service Robots? The OpenLORIS-Scene Datasets for Lifelong SLAM
    Xuesong Shi, Dongjiang Li, Pengpeng Zhao, Qinbin Tian, Yuxin Tian, Qiwei Long, Chunhao Zhu, Jingwei Song, Fei Qiao, Le Song, Yangquan Guo, Zhigang Wang, Yimin Zhang, Baoxing Qin, Wei Yang, Fangshi Wang, Rosa H. M. Chan, Qi She
    http://arxiv.org/abs/1911.05603v1

    • [cs.RO]Deep Reinforcement Learning Attitude Control of Fixed-Wing UAVs Using Proximal Policy Optimization
    Eivind Bøhn, Erlend M. Coates, Signe Moe, Tor Arne Johansen
    http://arxiv.org/abs/1911.05478v1

    • [cs.RO]IRIS: Implicit Reinforcement without Interaction at Scale for Learning Control from Offline Robot Manipulation Data
    Ajay Mandlekar, Fabio Ramos, Byron Boots, Li Fei-Fei, Animesh Garg, Dieter Fox
    http://arxiv.org/abs/1911.05321v1

    • [cs.RO]Numerical and experimental realization of analytical SLAM
    Jozef Bucko, Yulia Sandamirskaya, Jean-Jacques Slotine
    http://arxiv.org/abs/1911.05177v1

    • [cs.RO]Pose estimation and bin picking for deformable products
    Benjamin Joffe, Tevon Walker. Remi Gourdon, Konrad Ahlin
    http://arxiv.org/abs/1911.05185v1

    • [cs.SE]Reinforcement Learning-Driven Test Generation for Android GUI Applications using Formal Specifications
    Yavuz Koroglu, Alper Sen
    http://arxiv.org/abs/1911.05403v1

    • [cs.SI]Classifying Relevant Social Media Posts During Disasters Using Ensemble of Domain-agnostic and Domain-specific Word Embeddings
    Ganesh Nalluru, Rahul Pandey, Hemant Purohit
    http://arxiv.org/abs/1911.05165v1

    • [cs.SI]False positives using social cognitive mapping to identify childrens’ peer groups
    Zachary Neal, Jennifer Watling Neal, Rachel Domagalski
    http://arxiv.org/abs/1911.05703v1

    • [cs.SI]Generation and Classification of Activity Sequences for Spatiotemporal Modeling of Human Populations
    Albert M Lund, Ramkiran Gouripeddi, Julio C Facelli
    http://arxiv.org/abs/1911.05476v1

    • [cs.SI]Multi-MotifGAN (MMGAN): Motif-targeted Graph Generation and Prediction
    Anuththari Gamage, Eli Chien, Jianhao Peng, Olgica Milenkovic
    http://arxiv.org/abs/1911.05469v1

    • [cs.SI]On the choice of graph neural network architectures
    Clément Vignac, Guillermo Ortiz-Jiménez, Pascal Frossard
    http://arxiv.org/abs/1911.05384v1

    • [econ.EM]Randomization tests of copula symmetry
    Brendan K. Beare, Juwon Seo
    http://arxiv.org/abs/1911.05307v1

    • [eess.AS]‘Warriors of the Word’ — Deciphering Lyrical Topics in Music and Their Connection to Audio Feature Dimensions Based on a Corpus of Over 100,000 Metal Songs
    Isabella Czedik-Eysenberg, Oliver Wieczorek, Christoph Reuter
    http://arxiv.org/abs/1911.04952v2

    • [eess.AS]3-D Feature and Acoustic Modeling for Far-Field Speech Recognition
    Anurenjan Purushothaman, Anirudh Sreeram, Sriram Ganapathy
    http://arxiv.org/abs/1911.05504v1

    • [eess.IV]Automatic Frame Selection Using MLP Neural Network in Ultrasound Elastography
    Abdelrahman Zayed, Hassan Rivaz
    http://arxiv.org/abs/1911.05245v1

    • [eess.IV]Fast Approximate Time-Delay Estimation in Ultrasound Elastography Using Principal Component Analysis
    Abdelrahman Zayed, Hassan Rivaz
    http://arxiv.org/abs/1911.05242v1

    • [eess.IV]Semi-Supervised Multi-Organ Segmentation through Quality Assurance Supervision
    Ho Hin Lee, Yucheng Tang, Olivia Tang, Yuchen Xu, Yunqiang Chen, Dashan Gao, Shizhong Han, Riqiang Gao, Michael R. Savona, Richard G. Abramson, Yuankai Huo, Bennett A. Landman
    http://arxiv.org/abs/1911.05113v1

    • [eess.IV]Unsupervised Medical Image Segmentation with Adversarial Networks: From Edge Diagrams to Segmentation Maps
    Umaseh Sivanesan, Luis H. Braga, Ranil R. Sonnadara, Kiret Dhindsa
    http://arxiv.org/abs/1911.05140v1

    • [eess.SP]IMNet: A Learning Based Detector for Index Modulation Aided MIMO-OFDM Systems
    Jinxue Liu, Hancheng Lu
    http://arxiv.org/abs/1911.04133v2

    • [eess.SP]Real-Time Anomaly Detection for Advanced Manufacturing: Improving on Twitter’s State of the Art
    Caitríona M. Ryan, Andrew Parnell, Catherine Mahoney
    http://arxiv.org/abs/1911.05376v1

    • [eess.SP]Real-time ultra-low power ECG anomaly detection using an event-driven neuromorphic processor
    Felix Christian Bauer, Dylan Richard Muir, Giacomo Indiveri
    http://arxiv.org/abs/1911.05521v1

    • [eess.SY]Energy-Efficient Beamforming and Cooperative Jamming in IRS-Assisted MISO Networks
    Qun Wang, Fuhui Zhou, Rose Qingyang Hu, Yi Qian
    http://arxiv.org/abs/1911.05133v1

    • [math.OC]Quadratic number of nodes is sufficient to learn a dataset via gradient descent
    Biswarup Das, Eugene. A. Golikov
    http://arxiv.org/abs/1911.05402v1

    • [math.OC]Shadowing Properties of Optimization Algorithms
    Antonio Orvieto, Aurelien Lucchi
    http://arxiv.org/abs/1911.05206v1

    • [math.OC]Tropical Optimal Transport and Wasserstein Distances in Phylogenetic Tree Space
    Wonjun Lee, Wuchen Li, Bo Lin, Anthea Monod
    http://arxiv.org/abs/1911.05401v1

    • [math.PR]Mean and Variance of Brownian Motion with Given Final Value, Maximum and ArgMax: Extended Version
    Kurt S. Riedel
    http://arxiv.org/abs/1911.05272v1

    • [math.PR]The Value of the High, Low and Close in the Estimation of Brownian Motion: Extended Version
    Kurt S Riedel
    http://arxiv.org/abs/1911.05280v1

    • [math.ST]Error bounds for some approximate posterior measures in Bayesian inference
    Han Cheng Lie, T. J. Sullivan, Aretha Teckentrup
    http://arxiv.org/abs/1911.05669v1

    • [math.ST]Estimation after selection from bivariate normal population using LINEX loss function
    Mohd. Arshad, Omer Abdalghani, Kalu Ram Meena
    http://arxiv.org/abs/1911.05422v1

    • [math.ST]Improved Concentration Bounds for Gaussian Quadratic Forms
    Robert E. Gallagher, Louis J. M. Aslett, David Steinsaltz, Ryan R. Christ
    http://arxiv.org/abs/1911.05720v1

    • [math.ST]Kriging prediction with isotropic Matérn correlations: robustness and experimental design
    Rui Tuo, Wenjia Wang
    http://arxiv.org/abs/1911.05570v1

    • [math.ST]Optimality regions for designs in multiple linear regression models with correlated random coefficients
    Ulrike Graßhoff, Heinz Holling, Frank Röttger, Rainer Schwabe
    http://arxiv.org/abs/1911.05538v1

    • [physics.flu-dyn]Deep learning velocity signals allows to quantify turbulence intensity
    Alessandro Corbetta, Vlado Menkovski, Roberto Benzi, Federico Toschi
    http://arxiv.org/abs/1911.05718v1

    • [physics.soc-ph]Coordination Group Formation for OnLine Coordinated Routing Mechanisms
    Wang Peng, Lili Du
    http://arxiv.org/abs/1911.05159v1

    • [q-bio.GN]A Graph Auto-Encoder for Haplotype Assembly and Viral Quasispecies Reconstruction
    Ziqi Ke, Haris Vikalo
    http://arxiv.org/abs/1911.05316v1

    • [q-bio.GN]Learning from Data-Rich Problems: A Case Study on Genetic Variant Calling
    Ren Yi, Pi-Chuan Chang, Gunjan Baid, Andrew Carroll
    http://arxiv.org/abs/1911.05151v1

    • [q-bio.QM]AMPL: A Data-Driven Modeling Pipeline for Drug Discovery
    Amanda J. Minnich, Kevin McLoughlin, Margaret Tse, Jason Deng, Andrew Weber, Neha Murad, Benjamin D. Madej, Bharath Ramsundar, Tom Rush, Stacie Calad-Thomson, Jim Brase, Jonathan E. Allen
    http://arxiv.org/abs/1911.05211v1

    • [q-bio.QM]DARTS: DenseUnet-based Automatic Rapid Tool for brain Segmentation
    Aakash Kaku, Chaitra V. Hegde, Jeffrey Huang, Sohae Chung, Xiuyuan Wang, Matthew Young, Yvonne W. Lui, Narges Razavian
    http://arxiv.org/abs/1911.05567v1

    • [q-fin.CP]Neural networks for option pricing and hedging: a literature review
    Johannes Ruf, Weiguan Wang
    http://arxiv.org/abs/1911.05620v1

    • [q-fin.RM]An Unethical Optimization Principle
    Nicholas Beale, Heather Battey, Anthony C. Davison, Robert S. MacKay
    http://arxiv.org/abs/1911.05116v1

    • [stat.AP]Causality-based tests to detect the influence of confounders on mobile health diagnostic applications: a comparison with restricted permutations
    Elias Chaibub Neto, Meghasyam Tummalacherla, Lara Mangravite, Larsson Omberg
    http://arxiv.org/abs/1911.05139v1

    • [stat.AP]Human Immunodeficiency Virus(HIV) Cases in the Philippines: Analysis and Forecasting
    Analaine May A. Tatoy, Roel F. Ceballos
    http://arxiv.org/abs/1911.05423v1

    • [stat.AP]Identifying predictive biomarkers of CIMAvaxEGF success in advanced Lung Cancer Patients
    Patricia Luaces, Lizet Sanchez, Danay Saavedra, Tania Crombet, Wim Van der Elst, Ariel Alonso, Geert Molenberghs, Agustin Lage
    http://arxiv.org/abs/1911.05148v1

    • [stat.AP]The effect of geographic sampling on extreme precipitation: from models to observations and back again
    Mark D. Risser, Michael F. Wehner
    http://arxiv.org/abs/1911.05103v1

    • [stat.ME]A Bayesian hierarchical model for bridging across patient subgroups in phase I clinical trials with animal data
    Haiyan Zheng, Lisa V. Hampson, Thomas Jaki
    http://arxiv.org/abs/1911.05592v1

    • [stat.ME]A Simulation-free Group Sequential Design with Max-combo Tests in the Presence of Non-proportional Hazards
    Lili Wang, Xiaodong Luo, Zheng Cheng
    http://arxiv.org/abs/1911.05684v1

    • [stat.ME]Anomaly Detection in Large Scale Networks with Latent Space Models
    Wesley Lee, Tyler H. McCormick, Joshua Neil, Cole Sodja
    http://arxiv.org/abs/1911.05522v1

    • [stat.ME]Balanced Policy Evaluation and Learning for Right Censored Data
    Owen E. Leete, Nathan Kallus, Michael G. Hudgens, Sonia Napravnik, Michael R. Kosorok
    http://arxiv.org/abs/1911.05728v1

    • [stat.ME]Robust Fitting for Generalized Additive Models for Location, Scale and Shape
    William H. Aeberhard, Eva Cantoni, Giampiero Marra, Rosalba Radice
    http://arxiv.org/abs/1911.05125v1

    • [stat.ME]Sparse Linear Discriminant Analysis for Multi-view Structured Data
    Sandra E. Safo, Eun Jeong Min, Lillian Haine
    http://arxiv.org/abs/1911.05643v1

    • [stat.ML]Exponential Convergence Rates of Classification Errors on Learning with SGD and Random Features
    Shingo Yashima, Atsushi Nitanda, Taiji Suzuki
    http://arxiv.org/abs/1911.05350v1

    • [stat.ML]Harmonic Mean Point Processes: Proportional Rate Error Minimization for Obtundation Prediction
    Yoonjung Kim, Jeremy C. Weiss
    http://arxiv.org/abs/1911.05109v1

    • [stat.ML]Nonconvex Stochastic Nested Optimization via Stochastic ADMM
    Zhongruo Wang
    http://arxiv.org/abs/1911.05167v1