cs.AI - 人工智能
cs.CC - 计算复杂度 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.DS - 数据结构与算法 cs.GR - 计算机图形学 cs.HC - 人机接口 cs.IR - 信息检索 cs.IT - 信息论 cs.LG - 自动学习 cs.MM - 多媒体 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 econ.TH - 理论经济学 eess.IV - 图像与视频处理 eess.SP - 信号处理 eess.SY - 系统和控制 math.CO - 组合数学 math.NA - 数值分析 math.OC - 优化与控制 math.PR - 概率 math.ST - 统计理论 nlin.CG - 细胞自动机与晶格气 physics.soc-ph - 物理学与社会 q-bio.QM - 定量方法 stat.AP - 应用统计 stat.ME - 统计方法论 stat.ML - (统计)机器学习 stat.OT - 其他统计学
• [cs.AI]A Conformance Checking-based Approach for Drift Detection in Business Processes
• [cs.AI]A Scheme for Dynamic Risk-Sensitive Sequential Decision Making
• [cs.AI]Diverse Agents for Ad-Hoc Cooperation in Hanabi
• [cs.AI]Learning by Abstraction: The Neural State Machine
• [cs.AI]On the Semantic Interpretability of Artificial Intelligence Models
• [cs.CC]Interactive Verifiable Polynomial Evaluation
• [cs.CL]An Intrinsic Nearest Neighbor Analysis of Neural Machine Translation Architectures
• [cs.CL]Analyzing Phonetic and Graphemic Representations in End-to-End Automatic Speech Recognition
• [cs.CL]Hahahahaha, Duuuuude, Yeeessss!: A two-parameter characterization of stretchable words and the dynamics of mistypings and misspellings
• [cs.CL]Implicit Discourse Relation Identification for Open-domain Dialogues
• [cs.CL]Improving short text classification through global augmentation methods
• [cs.CL]Learning Neural Sequence-to-Sequence Models from Weak Feedback with Bipolar Ramp Loss
• [cs.CL]Multilingual Universal Sentence Encoder for Semantic Retrieval
• [cs.CL]NTT’s Machine Translation Systems for WMT19 Robustness Task
• [cs.CL]Neural Aspect and Opinion Term Extraction with Mined Rules as Weak Supervision
• [cs.CL]Sentiment and position-taking analysis of parliamentary debates: A systematic literature review
• [cs.CL]Systematic quantitative analyses reveal the folk-zoological knowledge embedded in folktales
• [cs.CR]ICLab: A Global, Longitudinal Internet Censorship Measurement Platform
• [cs.CR]Private key encryption and recovery in blockchain
• [cs.CR]Security for Distributed Deep Neural Networks Towards Data Confidentiality & Intellectual Property Protection
• [cs.CR]Using Temporal and Topological Features for Intrusion Detection in Operational Networks
• [cs.CV]3D pavement surface reconstruction using an RGB-D sensor
• [cs.CV]A Baseline for 3D Multi-Object Tracking
• [cs.CV]A Light weight and Hybrid Deep Learning Model based Online Signature Verification
• [cs.CV]Accurate Nuclear Segmentation with \Center Vector Encoding
• [cs.CV]Adaptive Exploration for Unsupervised Person Re-Identification
• [cs.CV]Attentive CT Lesion Detection Using Deep Pyramid Inference with Multi-Scale Booster
• [cs.CV]BADAM: A Public Dataset for Baseline Detection in Arabic-script Manuscripts
• [cs.CV]Deep Learning for Spacecraft Pose Estimation from Photorealistic Rendering
• [cs.CV]Deep Pixel-wise Binary Supervision for Face Presentation Attack Detection
• [cs.CV]Depth from Small Motion using Rank-1 Initialization
• [cs.CV]Distill-2MD-MTL: Data Distillation based on Multi-Dataset Multi-Domain Multi-Task Frame Work to Solve Face Related Tasksks, Multi Task Learning, Semi-Supervised Learning
• [cs.CV]Domain Adaptation in Multi-Channel Autoencoder based Features for Robust Face Anti-Spoofing
• [cs.CV]Efficient Pose Selection for Interactive Camera Calibration
• [cs.CV]Fast Visual Object Tracking with Rotated Bounding Boxes
• [cs.CV]Gated Multiple Feedback Network for Image Super-Resolution
• [cs.CV]Improving Deep Lesion Detection Using 3D Contextual and Spatial Attention
• [cs.CV]On the Exact Recovery Conditions of 3D Human Motion from 2D Landmark Motion with Sparse Articulated Motion
• [cs.CV]Personalised aesthetics with residual adapters
• [cs.CV]Positional Normalization
• [cs.CV]Signet Ring Cell Detection With a Semi-supervised Learning Framework
• [cs.CV]Sparse-to-Dense Hypercolumn Matching for Long-Term Visual Localization
• [cs.CV]Template-Based Posit Multiplication for Training and Inferring in Neural Networks
• [cs.CV]UnsuperPoint: End-to-end Unsupervised Interest Point Detector and Descriptor
• [cs.CV]calibDB: enabling web based computer vision through on-the-fly camera calibration
• [cs.CY]Characterizing Bitcoin donations to open source software on GitHub
• [cs.CY]Sharing and Learning Alloy on the Web
• [cs.DC]Bi-objective Optimisation of Data-parallel Applications on Heterogeneous Platforms for Performance and Energy via Workload Distribution
• [cs.DC]Streaming 1.9 Billion Hypersparse Network Updates per Second with D4M
• [cs.DS]Faster Deterministic Distributed Coloring Through Recursive List Coloring
• [cs.DS]Near-optimal Repair of Reed-Solomon Codes with Low Sub-packetization
• [cs.GR]Efficient Cloth Simulation using Miniature Cloth and Upscaling Deep Neural Networks
• [cs.HC]Belief places and spaces: Mapping cognitive environments
• [cs.HC]Pitako — Recommending Game Design Elements in Cicero
• [cs.HC]Translating neural signals to text using a Brain-Machine Interface
• [cs.IR]An Attention Mechanism for Musical Instrument Recognition
• [cs.IR]UW-BHI at MEDIQA 2019: An Analysis of Representation Methods for Medical Natural Language Inference
• [cs.IT]A Simple Derivation of AMP and its State Evolution via First-Order Cancellation
• [cs.IT]Collaborative Machine Learning at the Wireless Edge with Blind Transmitters
• [cs.IT]Control of Status Updates for Energy Harvesting Devices that Monitor Processes with Alarms
• [cs.IT]Deep Learning-Aided Dynamic Read Thresholds Design For Multi-Level-Cell Flash Memories
• [cs.IT]Distributed Approximation of Functions over Fast Fading Channels with Applications to Distributed Learning and the Max-Consensus Problem
• [cs.IT]Fundamental limits of quantum-secure covert communication over bosonic channels
• [cs.IT]On the 3-D Placement of Airborne Base Stations Using Tethered UAVs
• [cs.LG]Adversarial Fault Tolerant Training for Deep Neural Networks
• [cs.LG]Are deep ResNets provably better than linear predictors?
• [cs.LG]Asymptotic Bayes risk for Gaussian mixture in a semi-supervised setting
• [cs.LG]Attending to Emotional Narratives
• [cs.LG]Characterization of Overlap in Observational Studies
• [cs.LG]Comparing EM with GD in Mixture Models of Two Components
• [cs.LG]Contextual One-Class Classification in Data Streams
• [cs.LG]Deep Active Inference as Variational Policy Gradients
• [cs.LG]Fine-Grained Continual Learning
• [cs.LG]Latent ODEs for Irregularly-Sampled Time Series
• [cs.LG]Learning to Optimize Domain Specific Normalization for Domain Generalization
• [cs.LG]Mean Spectral Normalization of Deep Neural Networks for Embedded Automation
• [cs.LG]Multi-Scale Vector Quantization with Reconstruction Trees
• [cs.LG]Non-technical Loss Detection with Statistical Profile Images Based on Semi-supervised Learning
• [cs.LG]Nonnegative Matrix Factorization with Local Similarity Learning
• [cs.LG]On Activation Function Coresets for Network Pruning
• [cs.LG]PathRank: A Multi-Task Learning Framework to Rank Paths in Spatial Networks
• [cs.LG]Profiling Players with Engagement Predictions
• [cs.LG]Quantifying Confounding Bias in Neuroimaging Datasets with Causal Inference
• [cs.LG]Ranking-Based Reward Extrapolation without Rankings
• [cs.LG]SVGD: A Virtual Gradients Descent Method for Stochastic Optimization
• [cs.LG]Tensor p-shrinkage nuclear norm for low-rank tensor completion
• [cs.LG]The Power of Comparisons for Actively Learning Linear Classifiers
• [cs.LG]The What-If Tool: Interactive Probing of Machine Learning Models
• [cs.LG]Thompson Sampling for Combinatorial Network Optimization in Unknown Environments
• [cs.LG]Thompson Sampling on Symmetric $α$-Stable Bandits
• [cs.LG]Unified Optimal Analysis of the (Stochastic) Gradient Method
• [cs.LG]Variational Inference MPC for Bayesian Model-based Reinforcement Learning
• [cs.LG]Which Algorithmic Choices Matter at Which Batch Sizes? Insights From a Noisy Quadratic Model
• [cs.MM]Barriers towards no-reference metrics application to compressed video quality analysis: on the example of no-reference metric NIQE
• [cs.NE]A new hybrid genetic algorithm for protein structure prediction on the 2D triangular lattice
• [cs.NE]Event-based attention and tracking on neuromorphic hardware
• [cs.NE]Learning in Competitive Network with Haeusslers Equation adapted using FIREFLY algorithm
• [cs.NE]Melody Generation using an Interactive Evolutionary Algorithm
• [cs.NE]Procedural Content Generation through Quality Diversity
• [cs.RO]Assembly Planning by Subassembly Decomposition Using Blocking Reduction
• [cs.RO]Estimating Mass Distribution of Articulated Objects through Physical Interaction
• [cs.RO]Incremental Semantic Mapping with Unsupervised On-line Learning
• [cs.RO]Lidar-based Object Classification with Explicit Occlusion Modeling
• [cs.RO]Planning for target retrieval using a robotic manipulator in cluttered and occluded environments
• [cs.RO]RoboWalk: Explicit Augmented Human-Robot Dynamics Modeling for Design Optimization
• [cs.RO]Sequence-to-Sequence Natural Language to Humanoid Robot Sign Language
• [cs.RO]Swarm Engineering Through Quantitative Measurement of Swarm Robotic Principles in a 10,000 Robot Swarm
• [cs.RO]Towards Orientation Learning and Adaptation in Cartesian Space
• [cs.RO]Towards the Internet of Robotic Things: Analysis, Architecture, Components and Challenges
• [cs.RO]{Graph Policy Gradients for Large Scale Robot Control
• [cs.SD]Improving Reverberant Speech Training Using Diffuse Acoustic Simulation
• [cs.SI]Community Detection on Networks with Ricci Flow
• [cs.SI]Multitask Learning for Blackmarket Tweet Detection
• [econ.TH]Competing Models
• [eess.IV]Brain Tissues Segmentation on MR Perfusion Images Using CUSUM Filter for Boundary Pixels
• [eess.IV]DSNet: Automatic Dermoscopic Skin Lesion Segmentation
• [eess.IV]Deep Probabilistic Modeling of Glioma Growth
• [eess.IV]FC$^2$N: Fully Channel-Concatenated Network for Single Image Super-Resolution
• [eess.IV]Fully Convolutional Network for Removing DCT Artefacts From Images
• [eess.IV]Learning from Thresholds: Fully Automated Classification of Tumor Infiltrating Lymphocytes for Multiple Cancer Types
• [eess.SP]Channel Impulse Response-based Source Localization in a Diffusion-based Molecular Communication System
• [eess.SP]Decentralized Gaussian Mixture Fusion through Unified Quotient Approximations
• [eess.SP]Deep Learning Techniques for Improving Digital Gait Segmentation
• [eess.SP]Functional Brain Networks Discovery Using Dictionary Learning with Correlated Sparsity
• [eess.SY]Control of Painlevé Paradox in a Robotic System
• [math.CO]Placement Delivery Arrays from Combinations of Strong Edge Colorings
• [math.NA]A divide-and-conquer algorithm for binary matrix completion
• [math.NA]Bayesian approach for inverse obstacle scattering with Poisson data
• [math.OC]A Hybrid Stochastic Optimization Framework for Stochastic Composite Nonconvex Optimization
• [math.OC]Empirical Bayesian Learning in AR Graphical Models
• [math.PR]Posterior Convergence Analysis of $α$-Stable Sheets
• [math.PR]Scaling Limit of Neural Networks with the Xavier Initialization and Convergence to a Global Minimum
• [math.ST]A Bayesian Approach for Analyzing Data on the Stiefel Manifold
• [math.ST]Nonconvex Regularized Robust Regression with Oracle Properties in Polynomial Time
• [math.ST]Optimal experimental designs for treatment contrasts in heteroscedastic models with covariates
• [nlin.CG]Universal One-Dimensional Cellular Automata Derived for Turing Machines and its Dynamical Behaviour
• [physics.soc-ph]Loss of transmission on directed networks due to link deletion
• [physics.soc-ph]Uncertainty and causal emergence in complex networks
• [q-bio.QM]Applications of a Novel Knowledge Discovery and Data Mining Process Model for Metabolomics
• [stat.AP]Predictively Consistent Prior Effective Sample Sizes
• [stat.AP]Risk models for breast cancer and their validation
• [stat.AP]Road Maintenance Operation Start Time Optimization Based on Real-time Traffic Map Data
• [stat.AP]Surrogate modeling of indoor down-link human exposure based on sparse polynomial chaos expansion
• [stat.ME]A Robust Two-Sample Test for Time Series data
• [stat.ME]A framework for the pre-specification of statistical analysis strategies in clinical trials (Pre-SPEC)
• [stat.ME]Adaptive inference for a semiparametric GARCH model
• [stat.ME]Aggregated False Discovery Rate Control
• [stat.ME]False Discovery Rates in Biological Networks
• [stat.ME]Identifying the Influential Inputs for Network Output Variance Using Sparse Polynomial Chaos Expansion
• [stat.ME]Incremental Intervention Effects in Studies with Many Timepoints, Repeated Outcomes, and Dropout
• [stat.ME]Residual Entropy
• [stat.ML]All Sparse PCA Models Are Wrong, But Some Are Useful. Part I: Computation of Scores, Residuals and Explained Variance
• [stat.ML]Characterizing Inter-Layer Functional Mappings of Deep Learning Models
• [stat.ML]Conditional Independence Testing using Generative Adversarial Networks
• [stat.ML]Multivariate Time Series Imputation with Variational Autoencoders
• [stat.ML]Statistical Analysis of Nearest Neighbor Methods for Anomaly Detection
• [stat.ML]Understanding Player Engagement and In-Game Purchasing Behavior with Ensemble Learning
• [stat.ML]k-GANs: Ensemble of Generative Models with Semi-Discrete Optimal Transport
• [stat.OT]Topological Information Data Analysis
·····································
• [cs.AI]A Conformance Checking-based Approach for Drift Detection in Business Processes
Víctor Gallego-Fontenla, Juan C. Vidal, Manuel Lama
http://arxiv.org/abs/1907.04276v1
• [cs.AI]A Scheme for Dynamic Risk-Sensitive Sequential Decision Making
Shuai Ma, Jia Yuan Yu, Ahmet Satir
http://arxiv.org/abs/1907.04269v1
• [cs.AI]Diverse Agents for Ad-Hoc Cooperation in Hanabi
Rodrigo Canaan, Julian Togelius, Andy Nealen, Stefan Menzel
http://arxiv.org/abs/1907.03840v1
• [cs.AI]Learning by Abstraction: The Neural State Machine
Drew A. Hudson, Christopher D. Manning
http://arxiv.org/abs/1907.03950v1
• [cs.AI]On the Semantic Interpretability of Artificial Intelligence Models
Vivian S. Silva, André Freitas, Siegfried Handschuh
http://arxiv.org/abs/1907.04105v1
• [cs.CC]Interactive Verifiable Polynomial Evaluation
Saeid Sahraei, Mohammad Ali Maddah-Ali, Salman Avestimehr
http://arxiv.org/abs/1907.04302v1
• [cs.CL]An Intrinsic Nearest Neighbor Analysis of Neural Machine Translation Architectures
Hamidreza Ghader, Christof Monz
http://arxiv.org/abs/1907.03885v1
• [cs.CL]Analyzing Phonetic and Graphemic Representations in End-to-End Automatic Speech Recognition
Yonatan Belinkov, Ahmed Ali, James Glass
http://arxiv.org/abs/1907.04224v1
• [cs.CL]Hahahahaha, Duuuuude, Yeeessss!: A two-parameter characterization of stretchable words and the dynamics of mistypings and misspellings
Tyler J. Gray, Christopher M. Danforth, Peter Sheridan Dodds
http://arxiv.org/abs/1907.03920v1
• [cs.CL]Implicit Discourse Relation Identification for Open-domain Dialogues
Mingyu Derek Ma, Kevin K. Bowden, Jiaqi Wu, Wen Cui, Marilyn Walker
http://arxiv.org/abs/1907.03975v1
• [cs.CL]Improving short text classification through global augmentation methods
Vukosi Marivate, Tshephisho Sefara
http://arxiv.org/abs/1907.03752v1
• [cs.CL]Learning Neural Sequence-to-Sequence Models from Weak Feedback with Bipolar Ramp Loss
Laura Jehl, Carolin Lawrence, Stefan Riezler
http://arxiv.org/abs/1907.03748v1
• [cs.CL]Multilingual Universal Sentence Encoder for Semantic Retrieval
Yinfei Yang, Daniel Cer, Amin Ahmad, Mandy Guo, Jax Law, Noah Constant, Gustavo Hernandez Abrego, Steve Yuan, Chris Tar, Yun-Hsuan Sung, Brian Strope, Ray Kurzweil
http://arxiv.org/abs/1907.04307v1
• [cs.CL]NTT’s Machine Translation Systems for WMT19 Robustness Task
Soichiro Murakami, Makoto Morishita, Tsutomu Hirao, Masaaki Nagata
http://arxiv.org/abs/1907.03927v1
• [cs.CL]Neural Aspect and Opinion Term Extraction with Mined Rules as Weak Supervision
Hongliang Dai, Yangqiu Song
http://arxiv.org/abs/1907.03750v1
• [cs.CL]Sentiment and position-taking analysis of parliamentary debates: A systematic literature review
Gavin Abercrombie, Riza Batista-Navarro
http://arxiv.org/abs/1907.04126v1
• [cs.CL]Systematic quantitative analyses reveal the folk-zoological knowledge embedded in folktales
Yo Nakawake, Kosuke Sato
http://arxiv.org/abs/1907.03969v1
• [cs.CR]ICLab: A Global, Longitudinal Internet Censorship Measurement Platform
Arian Akhavan Niaki, Shinyoung Cho, Zachary Weinberg, Nguyen Phong Hoang, Abbas Razaghpanah, Nicolas Christin, Phillipa Gill
http://arxiv.org/abs/1907.04245v1
• [cs.CR]Private key encryption and recovery in blockchain
Mehmet Aydar, Salih Cemil Cetin, Serkan Ayvaz, Betul Aygun
http://arxiv.org/abs/1907.04156v1
• [cs.CR]Security for Distributed Deep Neural Networks Towards Data Confidentiality & Intellectual Property Protection
Laurent Gomez, Marcus Wilhelm, José Márquez, Patrick Duverger
http://arxiv.org/abs/1907.04246v1
• [cs.CR]Using Temporal and Topological Features for Intrusion Detection in Operational Networks
Simon D. Duque Anton, Daniel Fraunholz, Hans Dieter Schotten
http://arxiv.org/abs/1907.04098v1
• [cs.CV]3D pavement surface reconstruction using an RGB-D sensor
Ahmadreza Mahmoudzadeh, Sayna Firoozi Yeganeh, Amir Golroo
http://arxiv.org/abs/1907.04124v1
• [cs.CV]A Baseline for 3D Multi-Object Tracking
Xinshuo Weng, Kris Kitani
http://arxiv.org/abs/1907.03961v1
• [cs.CV]A Light weight and Hybrid Deep Learning Model based Online Signature Verification
Chandra Sekhar V., Anoushka Doctor, Prerana Mukherjee, Viswanath Pulabaigiri
http://arxiv.org/abs/1907.04061v1
• [cs.CV]Accurate Nuclear Segmentation with \Center Vector Encoding
Jiahui Li, Zhiqiang Hu, Shuang Yang
http://arxiv.org/abs/1907.03951v1
• [cs.CV]Adaptive Exploration for Unsupervised Person Re-Identification
Yuhang Ding, Hehe Fan, Mingliang Xu, Yi Yang
http://arxiv.org/abs/1907.04194v1
• [cs.CV]Attentive CT Lesion Detection Using Deep Pyramid Inference with Multi-Scale Booster
Qingbin Shao, Lijun Gong, Kai Ma, Hualuo Liu, Yefeng Zheng
http://arxiv.org/abs/1907.03958v1
• [cs.CV]BADAM: A Public Dataset for Baseline Detection in Arabic-script Manuscripts
Benjamin Kiessling, Daniel Stökl Ben Ezra, Matthew Thomas Miller
http://arxiv.org/abs/1907.04041v1
• [cs.CV]Deep Learning for Spacecraft Pose Estimation from Photorealistic Rendering
Pedro F. Proenca, Yang Gao
http://arxiv.org/abs/1907.04298v1
• [cs.CV]Deep Pixel-wise Binary Supervision for Face Presentation Attack Detection
Anjith George, Sebastien Marcel
http://arxiv.org/abs/1907.04047v1
• [cs.CV]Depth from Small Motion using Rank-1 Initialization
Peter O. Fasogbon
http://arxiv.org/abs/1907.04058v1
• [cs.CV]Distill-2MD-MTL: Data Distillation based on Multi-Dataset Multi-Domain Multi-Task Frame Work to Solve Face Related Tasksks, Multi Task Learning, Semi-Supervised Learning
Sepidehsadat Hosseini, Mohammad Amin Shabani, Nam Ik Cho
http://arxiv.org/abs/1907.03402v2
• [cs.CV]Domain Adaptation in Multi-Channel Autoencoder based Features for Robust Face Anti-Spoofing
Olegs Nikisins, Anjith George, Sebastien Marcel
http://arxiv.org/abs/1907.04048v1
• [cs.CV]Efficient Pose Selection for Interactive Camera Calibration
Pavel Rojtberg, Arjan Kuijper
http://arxiv.org/abs/1907.04096v1
• [cs.CV]Fast Visual Object Tracking with Rotated Bounding Boxes
Bao Xin Chen, John K. Tsotsos
http://arxiv.org/abs/1907.03892v1
• [cs.CV]Gated Multiple Feedback Network for Image Super-Resolution
Qilei Li, Zhen Li, Lu Lu, Gwanggil Jeon, Kai Liu, Xiaomin Yang
http://arxiv.org/abs/1907.04253v1
• [cs.CV]Improving Deep Lesion Detection Using 3D Contextual and Spatial Attention
Qingyi Tao, Zongyuan Ge, Jianfei Cai, Jianxiong Yin, Simon See
http://arxiv.org/abs/1907.04052v1
• [cs.CV]On the Exact Recovery Conditions of 3D Human Motion from 2D Landmark Motion with Sparse Articulated Motion
Abed Malti
http://arxiv.org/abs/1907.03967v1
• [cs.CV]Personalised aesthetics with residual adapters
Carlos Rodríguez - Pardo, Hakan Bilen
http://arxiv.org/abs/1907.03802v1
• [cs.CV]Positional Normalization
Boyi Li, Felix Wu, Kilian Q. Weinberger, Serge Belongie
http://arxiv.org/abs/1907.04312v1
• [cs.CV]Signet Ring Cell Detection With a Semi-supervised Learning Framework
Jiahui Li, Shuang Yang, Xiaodi Huang, Qian Da, Xiaoqun Yang, Zhiqiang Hu, Qi Duan, Chaofu Wang, Hongsheng Li
http://arxiv.org/abs/1907.03954v1
• [cs.CV]Sparse-to-Dense Hypercolumn Matching for Long-Term Visual Localization
Hugo Germain, Guillaume Bourmaud, Vincent Lepetit
http://arxiv.org/abs/1907.03965v1
• [cs.CV]Template-Based Posit Multiplication for Training and Inferring in Neural Networks
Raúl Murillo Montero, Alberto A. Del Barrio, Guillermo Botella
http://arxiv.org/abs/1907.04091v1
• [cs.CV]UnsuperPoint: End-to-end Unsupervised Interest Point Detector and Descriptor
Peter Hviid Christiansen, Mikkel Fly Kragh, Yury Brodskiy, Henrik Karstoft
http://arxiv.org/abs/1907.04011v1
• [cs.CV]calibDB: enabling web based computer vision through on-the-fly camera calibration
Pavel Rojtberg, Felix Gorschlüter
http://arxiv.org/abs/1907.04100v1
• [cs.CY]Characterizing Bitcoin donations to open source software on GitHub
Yury Zhauniarovich, Yazan Boshmaf, Husam Al Jawaheri, Mashael Al Sabah
http://arxiv.org/abs/1907.04002v1
• [cs.CY]Sharing and Learning Alloy on the Web
Nuno Macedo, Alcino Cunha, José Pereira, Renato Carvalho, Ricardo Silva, Ana C. R. Paiva, Miguel S. Ramalho, Daniel Silva
http://arxiv.org/abs/1907.02275v1
• [cs.DC]Bi-objective Optimisation of Data-parallel Applications on Heterogeneous Platforms for Performance and Energy via Workload Distribution
Hamidreza Khaleghzadeh, Muhammad Fahad, Arsalan Shahid, Ravi Reddy Manumachu, Alexey Lastovetsky
http://arxiv.org/abs/1907.04080v1
• [cs.DC]Streaming 1.9 Billion Hypersparse Network Updates per Second with D4M
Jeremy Kepner, Vijay Gadepally, Lauren Milechin, Siddharth Samsi, William Arcand, David Bestor, William Bergeron, Chansup Byun, Matthew Hubbell, Michael Houle, Michael Jones, Anne Klein, Peter Michaleas, Julie Mullen, Andrew Prout, Antonio Rosa, Charles Yee, Albert Reuther
http://arxiv.org/abs/1907.04217v1
• [cs.DS]Faster Deterministic Distributed Coloring Through Recursive List Coloring
Fabian Kuhn
http://arxiv.org/abs/1907.03797v1
• [cs.DS]Near-optimal Repair of Reed-Solomon Codes with Low Sub-packetization
Venkatesan Guruswami, Haotian Jiang
http://arxiv.org/abs/1907.03931v1
• [cs.GR]Efficient Cloth Simulation using Miniature Cloth and Upscaling Deep Neural Networks
Tae Min Lee, Young Jin Oh, In-Kwon Lee
http://arxiv.org/abs/1907.03953v1
• [cs.HC]Belief places and spaces: Mapping cognitive environments
Philip Feldman, Aaron Dant, Wayne Lutters
http://arxiv.org/abs/1907.04191v1
• [cs.HC]Pitako — Recommending Game Design Elements in Cicero
Tiago Machado, Dan Gopstein, Andy Nealen, Julian Togelius
http://arxiv.org/abs/1907.03877v1
• [cs.HC]Translating neural signals to text using a Brain-Machine Interface
Janaki Sheth, Ariel Tankus, Michelle Tran, Nader Pouratian, Itzhak Fried, William Speier
http://arxiv.org/abs/1907.04265v1
• [cs.IR]An Attention Mechanism for Musical Instrument Recognition
Siddharth Gururani, Mohit Sharma, Alexander Lerch
http://arxiv.org/abs/1907.04294v1
• [cs.IR]UW-BHI at MEDIQA 2019: An Analysis of Representation Methods for Medical Natural Language Inference
William R. Kearns, Wilson Lau, Jason A. Thomas
http://arxiv.org/abs/1907.04286v1
• [cs.IT]A Simple Derivation of AMP and its State Evolution via First-Order Cancellation
Philip Schniter
http://arxiv.org/abs/1907.04235v1
• [cs.IT]Collaborative Machine Learning at the Wireless Edge with Blind Transmitters
Mohammad Mohammadi Amiri, Tolga M. Duman, Deniz Gunduz
http://arxiv.org/abs/1907.03909v1
• [cs.IT]Control of Status Updates for Energy Harvesting Devices that Monitor Processes with Alarms
George Stamatakis, Nikolaos Pappas, Apostolos Traganitis
http://arxiv.org/abs/1907.03826v1
• [cs.IT]Deep Learning-Aided Dynamic Read Thresholds Design For Multi-Level-Cell Flash Memories
Zhen Mei, Kui Cai, Xuan He
http://arxiv.org/abs/1907.03938v1
• [cs.IT]Distributed Approximation of Functions over Fast Fading Channels with Applications to Distributed Learning and the Max-Consensus Problem
Igor Bjelakovic, Matthias Frey, Slawomir Stanczak
http://arxiv.org/abs/1907.03777v1
• [cs.IT]Fundamental limits of quantum-secure covert communication over bosonic channels
Michael S. Bullock, Christos N. Gagatsos, Saikat Guha, Boulat A. Bash
http://arxiv.org/abs/1907.04228v1
• [cs.IT]On the 3-D Placement of Airborne Base Stations Using Tethered UAVs
Mustafa A. Kishk, Ahmed Bader, Mohamed-Slim Alouini
http://arxiv.org/abs/1907.04299v1
• [cs.LG]Adversarial Fault Tolerant Training for Deep Neural Networks
Vasisht Duddu, D. Vijay Rao, Valentina E. Balas
http://arxiv.org/abs/1907.03103v2
• [cs.LG]Are deep ResNets provably better than linear predictors?
Chulhee Yun, Suvrit Sra, Ali Jadbabaie
http://arxiv.org/abs/1907.03922v1
• [cs.LG]Asymptotic Bayes risk for Gaussian mixture in a semi-supervised setting
Marc Lelarge, Leo Miolane
http://arxiv.org/abs/1907.03792v1
• [cs.LG]Attending to Emotional Narratives
Zhengxuan Wu, Xiyu Zhang, Tan Zhi-Xuan, Jamil Zaki, Desmond C. Ong
http://arxiv.org/abs/1907.04197v1
• [cs.LG]Characterization of Overlap in Observational Studies
Fredrik D. Johansson, Dennis Wei, Michael Oberst, Tian Gao, Gabriel Brat, David Sontag, Kush R. Varshney
http://arxiv.org/abs/1907.04138v1
• [cs.LG]Comparing EM with GD in Mixture Models of Two Components
Guojun Zhang, Pascal Poupart, George Trimponias
http://arxiv.org/abs/1907.03783v1
• [cs.LG]Contextual One-Class Classification in Data Streams
Richard Hugh Moulton, Herna L. Viktor, Nathalie Japkowicz, João Gama
http://arxiv.org/abs/1907.04233v1
• [cs.LG]Deep Active Inference as Variational Policy Gradients
Beren Millidge
http://arxiv.org/abs/1907.03876v1
• [cs.LG]Fine-Grained Continual Learning
Vincenzo Lomonaco, Davide Maltoni, Lorenzo Pellegrini
http://arxiv.org/abs/1907.03799v1
• [cs.LG]Latent ODEs for Irregularly-Sampled Time Series
Yulia Rubanova, Ricky T. Q. Chen, David Duvenaud
http://arxiv.org/abs/1907.03907v1
• [cs.LG]Learning to Optimize Domain Specific Normalization for Domain Generalization
Seonguk Seo, Yumin Suh, Dongwan Kim, Jongwoo Han, Bohyung Han
http://arxiv.org/abs/1907.04275v1
• [cs.LG]Mean Spectral Normalization of Deep Neural Networks for Embedded Automation
Anand Krishnamoorthy Subramanian, Nak Young Chong
http://arxiv.org/abs/1907.04003v1
• [cs.LG]Multi-Scale Vector Quantization with Reconstruction Trees
Enrico Cecini, Ernesto De Vito, Lorenzo Rosasco
http://arxiv.org/abs/1907.03875v1
• [cs.LG]Non-technical Loss Detection with Statistical Profile Images Based on Semi-supervised Learning
Jiangteng Li, Fei Wang
http://arxiv.org/abs/1907.03925v1
• [cs.LG]Nonnegative Matrix Factorization with Local Similarity Learning
Chong Peng, Zhao Kang, Chenglizhao Chen, Qiang Cheng
http://arxiv.org/abs/1907.04150v1
• [cs.LG]On Activation Function Coresets for Network Pruning
Ben Mussay, Samson Zhou, Vladimir Braverman, Dan Feldman
http://arxiv.org/abs/1907.04018v1
• [cs.LG]PathRank: A Multi-Task Learning Framework to Rank Paths in Spatial Networks
Sean Bin Yang, Bin Yang
http://arxiv.org/abs/1907.04028v1
• [cs.LG]Profiling Players with Engagement Predictions
Ana Fernández del Río, Pei Pei Chen, África Periáñez
http://arxiv.org/abs/1907.03870v1
• [cs.LG]Quantifying Confounding Bias in Neuroimaging Datasets with Causal Inference
Christian Wachinger, Benjamin Gutierrez Becker, Anna Rieckmann, Sebastian Pölsterl
http://arxiv.org/abs/1907.04102v1
• [cs.LG]Ranking-Based Reward Extrapolation without Rankings
Daniel S. Brown, Wonjoon Goo, Scott Niekum
http://arxiv.org/abs/1907.03976v1
• [cs.LG]SVGD: A Virtual Gradients Descent Method for Stochastic Optimization
Zheng Li, Shi Shu
http://arxiv.org/abs/1907.04021v1
• [cs.LG]Tensor p-shrinkage nuclear norm for low-rank tensor completion
Chunsheng Liu, Hong Shan, Chunlei Chen
http://arxiv.org/abs/1907.04092v1
• [cs.LG]The Power of Comparisons for Actively Learning Linear Classifiers
Max Hopkins, Daniel M. Kane, Shachar Lovett
http://arxiv.org/abs/1907.03816v1
• [cs.LG]The What-If Tool: Interactive Probing of Machine Learning Models
James Wexler, Mahima Pushkarna, Tolga Bolukbasi, Martin Wattenberg, Fernanda Viegas, Jimbo Wilson
http://arxiv.org/abs/1907.04135v1
• [cs.LG]Thompson Sampling for Combinatorial Network Optimization in Unknown Environments
Alihan Hüyük, Cem Tekin
http://arxiv.org/abs/1907.04201v1
• [cs.LG]Thompson Sampling on Symmetric $α$-Stable Bandits
Abhimanyu Dubey, Alex Pentland
http://arxiv.org/abs/1907.03821v1
• [cs.LG]Unified Optimal Analysis of the (Stochastic) Gradient Method
Sebastian U. Stich
http://arxiv.org/abs/1907.04232v1
• [cs.LG]Variational Inference MPC for Bayesian Model-based Reinforcement Learning
Masashi Okada, Tadahiro Taniguchi
http://arxiv.org/abs/1907.04202v1
• [cs.LG]Which Algorithmic Choices Matter at Which Batch Sizes? Insights From a Noisy Quadratic Model
Guodong Zhang, Lala Li, Zachary Nado, James Martens, Sushant Sachdeva, George E. Dahl, Christopher J. Shallue, Roger Grosse
http://arxiv.org/abs/1907.04164v1
• [cs.MM]Barriers towards no-reference metrics application to compressed video quality analysis: on the example of no-reference metric NIQE
Anastasia Antsiferova, Dmitriy Kulikov, Denis Kondranin, Dmitriy Vatolin
http://arxiv.org/abs/1907.03842v1
• [cs.NE]A new hybrid genetic algorithm for protein structure prediction on the 2D triangular lattice
Nabil Boumedine, Sadek Bouroubi
http://arxiv.org/abs/1907.04190v1
• [cs.NE]Event-based attention and tracking on neuromorphic hardware
Alpha Renner, Matthew Evanusa, Yulia Sandamirskaya
http://arxiv.org/abs/1907.04060v1
• [cs.NE]Learning in Competitive Network with Haeusslers Equation adapted using FIREFLY algorithm
N. Joshi
http://arxiv.org/abs/1907.04160v1
• [cs.NE]Melody Generation using an Interactive Evolutionary Algorithm
Majid Farzaneh, Rahil Mahdian Toroghi
http://arxiv.org/abs/1907.04258v1
• [cs.NE]Procedural Content Generation through Quality Diversity
Daniele Gravina, Ahmed Khalifa, Antonios Liapis, Julian Togelius, Georgios N. Yannakakis
http://arxiv.org/abs/1907.04053v1
• [cs.RO]Assembly Planning by Subassembly Decomposition Using Blocking Reduction
James Watson, Tucker Hermans
http://arxiv.org/abs/1907.03835v1
• [cs.RO]Estimating Mass Distribution of Articulated Objects through Physical Interaction
Niranjan Kumar Kannabiran, Irfan Essa, C. Karen Liu
http://arxiv.org/abs/1907.03964v1
• [cs.RO]Incremental Semantic Mapping with Unsupervised On-line Learning
Ygor C. N. Sousa, Hansenclever F. Bassani
http://arxiv.org/abs/1907.04001v1
• [cs.RO]Lidar-based Object Classification with Explicit Occlusion Modeling
Xiaoxiang Zhang, Hao Fu, Bin Dai
http://arxiv.org/abs/1907.04057v1
• [cs.RO]Planning for target retrieval using a robotic manipulator in cluttered and occluded environments
Changjoo Nam, Jinhwi Lee, Younggil Cho, Jeongho Lee, Dong Hwan Kim, ChangHwan Kim
http://arxiv.org/abs/1907.03956v1
• [cs.RO]RoboWalk: Explicit Augmented Human-Robot Dynamics Modeling for Design Optimization
S. Ali A. Moosavian, Mahdi Nabipour, Farshid Absalan, Vahdi Akbari
http://arxiv.org/abs/1907.04114v1
• [cs.RO]Sequence-to-Sequence Natural Language to Humanoid Robot Sign Language
Jennifer J. Gago, Valentina Vasco, Bartek Łukawski, Ugo Pattacini, Vadim Tikhanoff, Juan G. Victores, Carlos Balaguer
http://arxiv.org/abs/1907.04198v1
• [cs.RO]Swarm Engineering Through Quantitative Measurement of Swarm Robotic Principles in a 10,000 Robot Swarm
John Harwell, Maria Gini
http://arxiv.org/abs/1907.03880v1
• [cs.RO]Towards Orientation Learning and Adaptation in Cartesian Space
Yanlong Huang, Fares J. Abu-Dakka, João Silvério, Darwin G. Caldwell
http://arxiv.org/abs/1907.03918v1
• [cs.RO]Towards the Internet of Robotic Things: Analysis, Architecture, Components and Challenges
Ilya Afanasyev, Manuel Mazzara, Subham Chakraborty, Nikita Zhuchkov, Aizhan Maksatbek, Mohamad Kassab, Salvatore Distefano
http://arxiv.org/abs/1907.03817v1
• [cs.RO]{Graph Policy Gradients for Large Scale Robot Control
Arbaaz Khan, Ekaterina Tolstaya, Alejandro Ribeiro, Vijay Kumar
http://arxiv.org/abs/1907.03822v1
• [cs.SD]Improving Reverberant Speech Training Using Diffuse Acoustic Simulation
Zhenyu Tang, Lianwu Chen, Bo Wu, Dong Yu, Dinesh Manocha
http://arxiv.org/abs/1907.03988v1
• [cs.SI]Community Detection on Networks with Ricci Flow
Chien-Chun Ni, Yu-Yao Lin, Feng Luo, Jie Gao
http://arxiv.org/abs/1907.03993v1
• [cs.SI]Multitask Learning for Blackmarket Tweet Detection
Udit Arora, William Scott Paka, Tanmoy Chakraborty
http://arxiv.org/abs/1907.04072v1
• [econ.TH]Competing Models
Jose Luis Montiel Olea, Pietro Ortoleva, Mallesh M Pai, Andrea Prat
http://arxiv.org/abs/1907.03809v1
• [eess.IV]Brain Tissues Segmentation on MR Perfusion Images Using CUSUM Filter for Boundary Pixels
S. M. Alkhimova, A. P. Krenevych
http://arxiv.org/abs/1907.03865v1
• [eess.IV]DSNet: Automatic Dermoscopic Skin Lesion Segmentation
Md. Kamrul Hasan, Lavsen Dahal, Prasad N. Samarakoon, Fakrul Islam Tushar, Robert Marti Marly
http://arxiv.org/abs/1907.04305v1
• [eess.IV]Deep Probabilistic Modeling of Glioma Growth
Jens Petersen, Paul F. Jäger, Fabian Isensee, Simon A. A. Kohl, Ulf Neuberger, Wolfgang Wick, Jürgen Debus, Sabine Heiland, Martin Bendszus, Philipp Kickingereder, Klaus H. Maier-Hein
http://arxiv.org/abs/1907.04064v1
• [eess.IV]FC$^2$N: Fully Channel-Concatenated Network for Single Image Super-Resolution
Xiaole Zhao, Ying Liao, Ye Li, Tao Zhang, Xueming Zou
http://arxiv.org/abs/1907.03221v2
• [eess.IV]Fully Convolutional Network for Removing DCT Artefacts From Images
Patryk Najgebauer, Rafal Scherer
http://arxiv.org/abs/1907.03798v1
• [eess.IV]Learning from Thresholds: Fully Automated Classification of Tumor Infiltrating Lymphocytes for Multiple Cancer Types
Shahira Abousamra, Le Hou, Rajarsi Gupta, Chao Chen, Dimitris Samaras, Tahsin Kurc, Rebecca Batiste, Tianhao Zhao, Shroyer Kenneth, Joel Saltz
http://arxiv.org/abs/1907.03960v1
• [eess.SP]Channel Impulse Response-based Source Localization in a Diffusion-based Molecular Communication System
Henry Ernest Baidoo-Williams, Muhammad Mahboob Ur Rahman, Qammer Hussain Abbasi
http://arxiv.org/abs/1907.04239v1
• [eess.SP]Decentralized Gaussian Mixture Fusion through Unified Quotient Approximations
Nisar R. Ahmed
http://arxiv.org/abs/1907.04008v1
• [eess.SP]Deep Learning Techniques for Improving Digital Gait Segmentation
Matteo Gadaleta, Giulia Cisotto, Michele Rossi, Rana Zia Ur Rehman, Lynn Rochester, Silvia Del Din
http://arxiv.org/abs/1907.04281v1
• [eess.SP]Functional Brain Networks Discovery Using Dictionary Learning with Correlated Sparsity
Mohsen Joneidi
http://arxiv.org/abs/1907.03929v1
• [eess.SY]Control of Painlevé Paradox in a Robotic System
Davide Marchese, Marco Coraggio, S. John Hogan, Mario di Bernardo
http://arxiv.org/abs/1907.04070v1
• [math.CO]Placement Delivery Arrays from Combinations of Strong Edge Colorings
Jerod Michel, Qi Wang
http://arxiv.org/abs/1907.03177v2
• [math.NA]A divide-and-conquer algorithm for binary matrix completion
Melanie Beckerleg, Andrew Thompson
http://arxiv.org/abs/1907.04251v1
• [math.NA]Bayesian approach for inverse obstacle scattering with Poisson data
Xiaomei Yang, Zhiliang Deng
http://arxiv.org/abs/1907.03955v1
• [math.OC]A Hybrid Stochastic Optimization Framework for Stochastic Composite Nonconvex Optimization
Quoc Tran-Dinh, Nhan H. Pham, Dzung T. Phan, Lam M. Nguyen
http://arxiv.org/abs/1907.03793v1
• [math.OC]Empirical Bayesian Learning in AR Graphical Models
Mattia Zorzi
http://arxiv.org/abs/1907.03829v1
• [math.PR]Posterior Convergence Analysis of $α$-Stable Sheets
Neil K. Chada, Sari Lasanen, Lassi Roininen
http://arxiv.org/abs/1907.03086v2
• [math.PR]Scaling Limit of Neural Networks with the Xavier Initialization and Convergence to a Global Minimum
Justin Sirignano, Konstantinos Spiliopoulos
http://arxiv.org/abs/1907.04108v1
• [math.ST]A Bayesian Approach for Analyzing Data on the Stiefel Manifold
Subhadip Pal, Subhajit Sengupta, Riten Mitra, Arunava Banerjee
http://arxiv.org/abs/1907.04303v1
• [math.ST]Nonconvex Regularized Robust Regression with Oracle Properties in Polynomial Time
Xiaoou Pan, Qiang Sun, Wen-Xin Zhou
http://arxiv.org/abs/1907.04027v1
• [math.ST]Optimal experimental designs for treatment contrasts in heteroscedastic models with covariates
Samuel Rosa
http://arxiv.org/abs/1907.04044v1
• [nlin.CG]Universal One-Dimensional Cellular Automata Derived for Turing Machines and its Dynamical Behaviour
Sergio J. Martinez, Ivan M. Mendoza, Genaro J. Martinez, Shigeru Ninagawa
http://arxiv.org/abs/1907.04211v1
• [physics.soc-ph]Loss of transmission on directed networks due to link deletion
G. Kashyap, G. Ambika
http://arxiv.org/abs/1907.04007v1
• [physics.soc-ph]Uncertainty and causal emergence in complex networks
Brennan Klein, Erik Hoel
http://arxiv.org/abs/1907.03902v1
• [q-bio.QM]Applications of a Novel Knowledge Discovery and Data Mining Process Model for Metabolomics
Ahmed BaniMustafa, Nigel Hardy
http://arxiv.org/abs/1907.03755v1
• [stat.AP]Predictively Consistent Prior Effective Sample Sizes
Beat Neuenschwander, Sebastian Weber, Heinz Schmidli, Anthony O’Hagan
http://arxiv.org/abs/1907.04185v1
• [stat.AP]Risk models for breast cancer and their validation
Adam R Brentnall, Jack Cuzick
http://arxiv.org/abs/1907.02829v2
• [stat.AP]Road Maintenance Operation Start Time Optimization Based on Real-time Traffic Map Data
Zhepu Xu, Qun Yang
http://arxiv.org/abs/1907.03814v1
• [stat.AP]Surrogate modeling of indoor down-link human exposure based on sparse polynomial chaos expansion
Zicheng Liu, Dominique Lesselier, Bruno Sudret, Joe Wiart
http://arxiv.org/abs/1907.03933v1
• [stat.ME]A Robust Two-Sample Test for Time Series data
Alexis Bellot, Mihaela van der Schaar
http://arxiv.org/abs/1907.04081v1
• [stat.ME]A framework for the pre-specification of statistical analysis strategies in clinical trials (Pre-SPEC)
Brennan C Kahan, Gordon Forbes, Suzie Cro
http://arxiv.org/abs/1907.04078v1
• [stat.ME]Adaptive inference for a semiparametric GARCH model
Feiyu Jiang, Dong Li, Ke Zhu
http://arxiv.org/abs/1907.04147v1
• [stat.ME]Aggregated False Discovery Rate Control
Fang Xie, Johannes Lederer
http://arxiv.org/abs/1907.03807v1
• [stat.ME]False Discovery Rates in Biological Networks
Lu Yu, Tobias Kaufmann, Johannes Lederer
http://arxiv.org/abs/1907.03808v1
• [stat.ME]Identifying the Influential Inputs for Network Output Variance Using Sparse Polynomial Chaos Expansion
Zhanlin Liu, Ashis G. Banerjee, Youngjun Choe
http://arxiv.org/abs/1907.04266v1
• [stat.ME]Incremental Intervention Effects in Studies with Many Timepoints, Repeated Outcomes, and Dropout
Kwangho Kim, Edward H. Kennedy, Ashley I. Naimi
http://arxiv.org/abs/1907.04004v1
• [stat.ME]Residual Entropy
Barnaby Rowe
http://arxiv.org/abs/1907.03888v1
• [stat.ML]All Sparse PCA Models Are Wrong, But Some Are Useful. Part I: Computation of Scores, Residuals and Explained Variance
J. Camacho, A. K. Smilde, E. Saccenti, J. A. Westerhuis
http://arxiv.org/abs/1907.03989v1
• [stat.ML]Characterizing Inter-Layer Functional Mappings of Deep Learning Models
Donald Waagen, Katie Rainey, Jamie Gantert, David Gray, Megan King, M. Shane Thompson, Jonathan Barton, Will Waldron, Samantha Livingston, Don Hulsey
http://arxiv.org/abs/1907.04223v1
• [stat.ML]Conditional Independence Testing using Generative Adversarial Networks
Alexis Bellot, Mihaela van der Schaar
http://arxiv.org/abs/1907.04068v1
• [stat.ML]Multivariate Time Series Imputation with Variational Autoencoders
Vincent Fortuin, Gunnar Rätsch, Stephan Mandt
http://arxiv.org/abs/1907.04155v1
• [stat.ML]Statistical Analysis of Nearest Neighbor Methods for Anomaly Detection
Xiaoyi Gu, Leman Akoglu, Alessandro Rinaldo
http://arxiv.org/abs/1907.03813v1
• [stat.ML]Understanding Player Engagement and In-Game Purchasing Behavior with Ensemble Learning
Anna Guitart, Ana Fernández del Río, África Periáñez
http://arxiv.org/abs/1907.03947v1
• [stat.ML]k-GANs: Ensemble of Generative Models with Semi-Discrete Optimal Transport
Luca Ambrogioni, Umut Güçlü, Marcel van Gerven
http://arxiv.org/abs/1907.04050v1
• [stat.OT]Topological Information Data Analysis
Pierre Baudot, Monica Tapia, Daniel Bennequin, Jean-Marc Goaillard
http://arxiv.org/abs/1907.04242v1