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
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• [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