cs.AI - 人工智能
cs.CL - 计算与语言
cs.CR - 加密与安全
cs.CV - 机器视觉与模式识别
cs.CY - 计算与社会
cs.DB - 数据库
cs.DC - 分布式、并行与集群计算
cs.DS - 数据结构与算法
cs.HC - 人机接口
cs.IR - 信息检索
cs.IT - 信息论
cs.LG - 自动学习
cs.LO - 计算逻辑
cs.NE - 神经与进化计算
cs.PF - 计算性能
cs.RO - 机器人学
cs.SD - 声音处理
cs.SI - 社交网络与信息网络
econ.EM - 计量经济学
math.OC - 优化与控制
math.PR - 概率
math.ST - 统计理论
q-fin.ST - 统计金融学
stat.AP - 应用统计
stat.CO - 统计计算
stat.ME - 统计方法论
stat.ML - (统计)机器学习
• [cs.AI]An adaptive stigmergy-based system for evaluating technological indicator dynamics in the context of smart specialization
• [cs.AI]Efficient Evolutionary Methods for Game Agent Optimisation: Model-Based is Best
• [cs.AI]Towards a Framework Combining Machine Ethics and Machine Explainability
• [cs.CL]Coarse-grain Fine-grain Coattention Network for Multi-evidence Question Answering
• [cs.CR]Scalable Information-Flow Analysis of Secure Three-Party Affine Computations
• [cs.CR]Towards Thwarting Social Engineering Attacks
• [cs.CV]A Hierarchical Grocery Store Image Dataset with Visual and Semantic Labels
• [cs.CV]A Noise-Sensitivity-Analysis-Based Test Prioritization Technique for Deep Neural Networks
• [cs.CV]A Remote Sensing Image Dataset for Cloud Removal
• [cs.CV]Active Learning with TensorBoard Projector
• [cs.CV]Adaptive Locality Preserving Regression
• [cs.CV]Baseline Desensitizing In Translation Averaging
• [cs.CV]CLEVR-Ref+: Diagnosing Visual Reasoning with Referring Expressions
• [cs.CV]Edge-Semantic Learning Strategy for Layout Estimation in Indoor Environment
• [cs.CV]Face Recognition: A Novel Multi-Level Taxonomy based Survey
• [cs.CV]Flow Based Self-supervised Pixel Embedding for Image Segmentation
• [cs.CV]Generating Multiple Objects at Spatially Distinct Locations
• [cs.CV]GeoNet: Deep Geodesic Networks for Point Cloud Analysis
• [cs.CV]Linear colour segmentation revisited
• [cs.CV]Photo-Sketching: Inferring Contour Drawings from Images
• [cs.CV]Rethinking on Multi-Stage Networks for Human Pose Estimation
• [cs.CV]Visualizing Deep Similarity Networks
• [cs.CY]Landscape of Big Medical Data: A Pragmatic Survey on Prioritized Tasks
• [cs.CY]Visibility and Training in Open Source Software Adoption: A Case in Philippine Higher Education
• [cs.DB]Une nouvelle approche de complétion des valeurs manquantes dans les bases de données
• [cs.DC]A Secure and Persistent Memory System for Non-volatile Memory
• [cs.DC]Machine Learning at the Wireless Edge: Distributed Stochastic Gradient Descent Over-the-Air
• [cs.DC]Quality Assessment and Improvement of Helm Charts for Kubernetes-Based Cloud Applications
• [cs.DS]Efficient Race Detection with Futures
• [cs.DS]Real-Time EEG Classification via Coresets for BCI Applications
• [cs.HC]Measuring Physical Activity of Older Adults via Smartwatch and Stigmergic Receptive Fields
• [cs.HC]Wi-Fi Sensing: Applications and Challenges
• [cs.IR]Pseudo-Implicit Feedback for Alleviating Data Sparsity in Top-K Recommendation
• [cs.IT]An Introductory Guide to Fano's Inequality with Applications in Statistical Estimation
• [cs.IT]Massive MIMO Unsourced Random Access
• [cs.LG]-Net: Molecular Structure Estimation from Cryo-EM Density Volumes
• [cs.LG]A Comprehensive Survey on Graph Neural Networks
• [cs.LG]Adversarial Learning of a Sampler Based on an Unnormalized Distribution
• [cs.LG]Adversarial Robustness May Be at Odds With Simplicity
• [cs.LG]Human-Like Autonomous Car-Following Model with Deep Reinforcement Learning
• [cs.LG]Instance-Based Classification through Hypothesis Testing
• [cs.LG]Multi-Label Adversarial Perturbations
• [cs.LG]Multi-class Classification without Multi-class Labels
• [cs.LG]On Finding Local Nash Equilibria (and Only Local Nash Equilibria) in Zero-Sum Games
• [cs.LG]Personalized explanation in machine learning
• [cs.LG]Prediction of multi-dimensional spatial variation data via Bayesian tensor completion
• [cs.LG]Towards Global Remote Discharge Estimation: Using the Few to Estimate The Many
• [cs.LG]Volumetric Convolution: Automatic Representation Learning in Unit Ball
• [cs.LO]The Challenges in Specifying and Explaining Synthesized Implementations of Reactive Systems
• [cs.NE]A Constrained Cooperative Coevolution Strategy for Weights Adaptation Optimization of Heterogeneous Epidemic Spreading Networks
• [cs.NE]An Improved multi-objective genetic algorithm based on orthogonal design and adaptive clustering pruning strategy
• [cs.NE]Performance of Three Slim Variants of The Long Short-Term Memory (LSTM) Layer
• [cs.PF]Towards the Tradeoff Between Service Performance and Information Freshness
• [cs.RO]Design, Development and Experimental Realization of a Quadrupedal Research Platform: Stoch
• [cs.RO]From exploration to control: learning object manipulation skills through novelty search and local adaptation
• [cs.RO]Robotic Tankette for Intelligent BioEnergy Agriculture: Design, Development and Field Tests
• [cs.SD]Deep Speech Enhancement for Reverberated and Noisy Signals using Wide Residual Networks
• [cs.SD]Feature reinforcement with word embedding and parsing information in neural TTS
• [cs.SI]Event detection in Twitter: A keyword volume approach
• [cs.SI]Modeling Information Propagation in General V2V-enabled Transportation Networks
• [cs.SI]Sybil-Resilient Conductance-Based Community Expansion
• [cs.SI]Virtual Web Based Personalized PageRank Updating
• [econ.EM]Modeling Dynamic Transport Network with Matrix Factor Models: with an Application to International Trade Flow
• [math.OC]Finite rate distributed weight-balancing and average consensus over digraphs
• [math.OC]The Extended Kalman Filter is a Natural Gradient Descent in Trajectory Space
• [math.PR]Modelling Italian mortality rates with a geometric-type fractional Ornstein-Uhlenbeck process
• [math.ST]Energy distance and kernel mean embedding for two sample survival test
• [q-fin.ST]The market nanostructure origin of asset price time reversal asymmetry
• [stat.AP]Heavy-Tailed Data Breaches in the Nat-Cat Framework & the Challenge of Insuring Cyber Risks
• [stat.CO]A Simple Algorithm for Scalable Monte Carlo Inference
• [stat.ME]Efficient augmentation and relaxation learning for individualized treatment rules using observational data
• [stat.ME]Which practical interventions does the do-operator refer to in causal inference? Illustration on the example of obesity and cancer
• [stat.ML]Learning a Generator Model from Terminal Bus Data
• [stat.ML]Projecting "better than randomly": How to reduce the dimensionality of very large datasets in a way that outperforms random projections
• [stat.ML]Sparse Learning in reproducing kernel Hilbert space
·····································
• [cs.AI]An adaptive stigmergy-based system for evaluating technological indicator dynamics in the context of smart specialization
A. L. Alfeo, F. P. Appio, M. G. C. A. Cimino, A. Lazzeri, A. Martini, G. Vaglini
http://arxiv.org/abs/1901.00553v1
• [cs.AI]Efficient Evolutionary Methods for Game Agent Optimisation: Model-Based is Best
Simon M. Lucas, Jialin Liu, Ivan Bravi, Raluca D. Gaina, John Woodward, Vanessa Volz, Diego Perez-Liebana
http://arxiv.org/abs/1901.00723v1
• [cs.AI]Towards a Framework Combining Machine Ethics and Machine Explainability
Kevin Baum, Holger Hermanns, Timo Speith
http://arxiv.org/abs/1901.00590v1
• [cs.CL]Coarse-grain Fine-grain Coattention Network for Multi-evidence Question Answering
Victor Zhong, Caiming Xiong, Nitish Shirish Keskar, Richard Socher
http://arxiv.org/abs/1901.00603v1
• [cs.CR]Scalable Information-Flow Analysis of Secure Three-Party Affine Computations
Patrick Ah-Fat, Michael Huth
http://arxiv.org/abs/1901.00798v1
• [cs.CR]Towards Thwarting Social Engineering Attacks
Zheyuan Ryan Shi, Aaron Schlenker, Brian Hay, Fei Fang
http://arxiv.org/abs/1901.00586v1
• [cs.CV]A Hierarchical Grocery Store Image Dataset with Visual and Semantic Labels
Marcus Klasson, Cheng Zhang, Hedvig Kjellström
http://arxiv.org/abs/1901.00711v1
• [cs.CV]A Noise-Sensitivity-Analysis-Based Test Prioritization Technique for Deep Neural Networks
Long Zhang, Xuechao Sun, Yong Li, Zhenyu Zhang, Yang Feng
http://arxiv.org/abs/1901.00054v2
• [cs.CV]A Remote Sensing Image Dataset for Cloud Removal
Daoyu Lin, Guangluan Xu, Xiaoke Wang, Yang Wang, Xian Sun, Kun Fu
http://arxiv.org/abs/1901.00600v1
• [cs.CV]Active Learning with TensorBoard Projector
Francois Luus, Naweed Khan, Ismail Akhalwaya
http://arxiv.org/abs/1901.00675v1
• [cs.CV]Adaptive Locality Preserving Regression
Jie Wen, Zuofeng Zhong, Zheng Zhang, Lunke Fei, Zhihui Lai, Runze Chen
http://arxiv.org/abs/1901.00563v1
• [cs.CV]Baseline Desensitizing In Translation Averaging
Bingbing Zhuang, Loong-Fah Cheong, Gim Hee Lee
http://arxiv.org/abs/1901.00643v1
• [cs.CV]CLEVR-Ref+: Diagnosing Visual Reasoning with Referring Expressions
Runtao Liu, Chenxi Liu, Yutong Bai, Alan Yuille
http://arxiv.org/abs/1901.00850v1
• [cs.CV]Edge-Semantic Learning Strategy for Layout Estimation in Indoor Environment
Weidong Zhang, Wei Zhang, Jason Gu
http://arxiv.org/abs/1901.00621v1
• [cs.CV]Face Recognition: A Novel Multi-Level Taxonomy based Survey
Alireza Sepas-Moghaddam, Fernando Pereira, Paulo Lobato Correia
http://arxiv.org/abs/1901.00713v1
• [cs.CV]Flow Based Self-supervised Pixel Embedding for Image Segmentation
Bin Ma, Shubao Liu, Yingxuan Zhi, Qi Song
http://arxiv.org/abs/1901.00520v1
• [cs.CV]Generating Multiple Objects at Spatially Distinct Locations
Tobias Hinz, Stefan Heinrich, Stefan Wermter
http://arxiv.org/abs/1901.00686v1
• [cs.CV]GeoNet: Deep Geodesic Networks for Point Cloud Analysis
Tong He, Haibin Huang, Li Yi, Yuqian Zhou, Chihao Wu, Jue Wang, Stefano Soatto
http://arxiv.org/abs/1901.00680v1
• [cs.CV]Linear colour segmentation revisited
Anna Smagina, Valentina Bozhkova, Sergey Gladilin, Dmitry Nikolaev
http://arxiv.org/abs/1901.00534v1
• [cs.CV]Photo-Sketching: Inferring Contour Drawings from Images
Mengtian Li, Zhe Lin, Radomir Mech, Ersin Yumer, Deva Ramanan
http://arxiv.org/abs/1901.00542v1
• [cs.CV]Rethinking on Multi-Stage Networks for Human Pose Estimation
Wenbo Li, Zhicheng Wang, Binyi Yin, Qixiang Peng, Yuming Du, Tianzi Xiao, Gang Yu, Hongtao Lu, Yichen Wei, Jian Sun
http://arxiv.org/abs/1901.00148v2
• [cs.CV]Visualizing Deep Similarity Networks
Abby Stylianou, Richard Souvenir, Robert Pless
http://arxiv.org/abs/1901.00536v1
• [cs.CY]Landscape of Big Medical Data: A Pragmatic Survey on Prioritized Tasks
Zhifei Zhang, Wanling Gao, Fan Zhang, Yunyou Huang, Shaopeng Dai, Fanda Fan, Jianfeng Zhan, Mengjia Du, Silin Yin, Longxin Xiong, Juan Du, Yumei Cheng, Xiexuan Zhou, Rui Ren, Lei Wang, Hainan Ye
http://arxiv.org/abs/1901.00642v1
• [cs.CY]Visibility and Training in Open Source Software Adoption: A Case in Philippine Higher Education
Ryan Ebardo
http://arxiv.org/abs/1901.00750v1
• [cs.DB]Une nouvelle approche de complétion des valeurs manquantes dans les bases de données
Leila Ben Othman
http://arxiv.org/abs/1901.00671v1
• [cs.DC]A Secure and Persistent Memory System for Non-volatile Memory
Pengfei Zuo, Yu Hua, Yuan Xie
http://arxiv.org/abs/1901.00620v1
• [cs.DC]Machine Learning at the Wireless Edge: Distributed Stochastic Gradient Descent Over-the-Air
Mohammad Mohammadi Amiri, Deniz Gunduz
http://arxiv.org/abs/1901.00844v1
• [cs.DC]Quality Assessment and Improvement of Helm Charts for Kubernetes-Based Cloud Applications
Josef Spillner
http://arxiv.org/abs/1901.00644v1
• [cs.DS]Efficient Race Detection with Futures
Robert Utterback, Kunal Agrawal, Jeremy Fineman, I-Ting Angelina Lee
http://arxiv.org/abs/1901.00622v1
• [cs.DS]Real-Time EEG Classification via Coresets for BCI Applications
Eitan Netzer, Alex Frid, Dan Feldman
http://arxiv.org/abs/1901.00512v1
• [cs.HC]Measuring Physical Activity of Older Adults via Smartwatch and Stigmergic Receptive Fields
A. L. Alfeo, M. G. C. A. Cimino, G. Vaglini
http://arxiv.org/abs/1901.00552v1
• [cs.HC]Wi-Fi Sensing: Applications and Challenges
A. M. Khalili, Abdel-Hamid Soliman, Md Asaduzzaman, Alison Griffiths
http://arxiv.org/abs/1901.00715v1
• [cs.IR]Pseudo-Implicit Feedback for Alleviating Data Sparsity in Top-K Recommendation
Yun He, Haochen Chen, Ziwei Zhu, James Caverlee
http://arxiv.org/abs/1901.00597v1
• [cs.IT]An Introductory Guide to Fano's Inequality with Applications in Statistical Estimation
Jonathan Scarlett, Volkan Cevher
http://arxiv.org/abs/1901.00555v1
• [cs.IT]Massive MIMO Unsourced Random Access
Alexander Fengler, Giuseppe Caire, Peter Jung, Saeid Haghighatshoar
http://arxiv.org/abs/1901.00828v1
• [cs.LG]-Net: Molecular Structure Estimation from Cryo-EM Density Volumes
Kui Xu, Zhe Wang, Jiangping Shi, Hongsheng Li, Qiangfeng Cliff Zhang
http://arxiv.org/abs/1901.00785v1
• [cs.LG]A Comprehensive Survey on Graph Neural Networks
Zonghan Wu, Shirui Pan, Fengwen Chen, Guodong Long, Chengqi Zhang, Philip S. Yu
http://arxiv.org/abs/1901.00596v1
• [cs.LG]Adversarial Learning of a Sampler Based on an Unnormalized Distribution
Chunyuan Li, Ke Bai, Jianqiao Li, Guoyin Wang, Changyou Chen, Lawrence Carin
http://arxiv.org/abs/1901.00612v1
• [cs.LG]Adversarial Robustness May Be at Odds With Simplicity
Preetum Nakkiran
http://arxiv.org/abs/1901.00532v1
• [cs.LG]Human-Like Autonomous Car-Following Model with Deep Reinforcement Learning
Meixin Zhu, Xuesong Wang, Yinhai Wang
http://arxiv.org/abs/1901.00569v1
• [cs.LG]Instance-Based Classification through Hypothesis Testing
Zengyou He, Chaohua Sheng, Yan Liu, Quan Zou
http://arxiv.org/abs/1901.00560v1
• [cs.LG]Multi-Label Adversarial Perturbations
Qingquan Song, Haifeng Jin, Xiao Huang, Xia Hu
http://arxiv.org/abs/1901.00546v1
• [cs.LG]Multi-class Classification without Multi-class Labels
Yen-Chang Hsu, Zhaoyang Lv, Joel Schlosser, Phillip Odom, Zsolt Kira
http://arxiv.org/abs/1901.00544v1
• [cs.LG]On Finding Local Nash Equilibria (and Only Local Nash Equilibria) in Zero-Sum Games
Eric V. Mazumdar, Michael I. Jordan, S. Shankar Sastry
http://arxiv.org/abs/1901.00838v1
• [cs.LG]Personalized explanation in machine learning
Johanes Schneider, Joshua Handali
http://arxiv.org/abs/1901.00770v1
• [cs.LG]Prediction of multi-dimensional spatial variation data via Bayesian tensor completion
Jiali Luan, Zheng Zhang
http://arxiv.org/abs/1901.00578v1
• [cs.LG]Towards Global Remote Discharge Estimation: Using the Few to Estimate The Many
Yotam Gigi, Gal Elidan, Avinatan Hassidim, Yossi Matias, Zach Moshe, Sella Nevo, Guy Shalev, Ami Wiesel
http://arxiv.org/abs/1901.00786v1
• [cs.LG]Volumetric Convolution: Automatic Representation Learning in Unit Ball
Sameera Ramasinghe, Salman Khan, Nick Barnes
http://arxiv.org/abs/1901.00616v1
• [cs.LO]The Challenges in Specifying and Explaining Synthesized Implementations of Reactive Systems
Hadas Kress-Gazit, Hazem Torfah
http://arxiv.org/abs/1901.00591v1
• [cs.NE]A Constrained Cooperative Coevolution Strategy for Weights Adaptation Optimization of Heterogeneous Epidemic Spreading Networks
Yun Feng, Bing-Chuan Wang, Li Ding
http://arxiv.org/abs/1901.00602v1
• [cs.NE]An Improved multi-objective genetic algorithm based on orthogonal design and adaptive clustering pruning strategy
Xinwu Yang, Guizeng You, Chong Zhao, Mengfei Dou, Xinian Guo
http://arxiv.org/abs/1901.00577v1
• [cs.NE]Performance of Three Slim Variants of The Long Short-Term Memory (LSTM) Layer
Daniel Kent, Fathi M. Salem
http://arxiv.org/abs/1901.00525v1
• [cs.PF]Towards the Tradeoff Between Service Performance and Information Freshness
Zhongdong Liu, Bo Ji
http://arxiv.org/abs/1901.00826v1
• [cs.RO]Design, Development and Experimental Realization of a Quadrupedal Research Platform: Stoch
Dhaivat Dholakiya, Shounak Bhattacharya, Ajay Gunalan, Abhik Singla, Shalabh Bhatnagar, Bharadwaj Amrutur, Ashitava Ghosal, Shishir Kolathaya
http://arxiv.org/abs/1901.00697v1
• [cs.RO]From exploration to control: learning object manipulation skills through novelty search and local adaptation
Seungsu Kim, Alexandre Coninx, Stephane Doncieux
http://arxiv.org/abs/1901.00811v1
• [cs.RO]Robotic Tankette for Intelligent BioEnergy Agriculture: Design, Development and Field Tests
Marco F. S. Xaud, Antonio C. Leite, Evelyn S. Barbosa, Henrique D. Faria, Gabriel S. M. Loureiro, Pål J. From
http://arxiv.org/abs/1901.00761v1
• [cs.SD]Deep Speech Enhancement for Reverberated and Noisy Signals using Wide Residual Networks
Dayana Ribas, Jorge Llombart, Antonio Miguel, Luis Vicente
http://arxiv.org/abs/1901.00660v1
• [cs.SD]Feature reinforcement with word embedding and parsing information in neural TTS
Huaiping Ming, Lei He, Haohan Guo, Frank Soong
http://arxiv.org/abs/1901.00707v1
• [cs.SI]Event detection in Twitter: A keyword volume approach
Ahmad Hany Hossny, Lewis Mitchell
http://arxiv.org/abs/1901.00570v1
• [cs.SI]Modeling Information Propagation in General V2V-enabled Transportation Networks
Jungyeol Kim, Saswati Sarkar, Santosh S. Venkatesh, Megan Smirti Ryerson, David Starobinski
http://arxiv.org/abs/1901.00527v1
• [cs.SI]Sybil-Resilient Conductance-Based Community Expansion
Ouri Poupko, Gal Shahaf, Ehud Shapiro, Nimrod Talmon
http://arxiv.org/abs/1901.00752v1
• [cs.SI]Virtual Web Based Personalized PageRank Updating
Bo Song, Xiaobo Jiang, Xinhua Zhuang
http://arxiv.org/abs/1901.00678v1
• [econ.EM]Modeling Dynamic Transport Network with Matrix Factor Models: with an Application to International Trade Flow
Elynn Y. Chen, Rong Chen
http://arxiv.org/abs/1901.00769v1
• [math.OC]Finite rate distributed weight-balancing and average consensus over digraphs
Chang-Shen Lee, Nicolò Michelusi, Gesualdo Scutari
http://arxiv.org/abs/1901.00611v1
• [math.OC]The Extended Kalman Filter is a Natural Gradient Descent in Trajectory Space
Yann Ollivier
http://arxiv.org/abs/1901.00696v1
• [math.PR]Modelling Italian mortality rates with a geometric-type fractional Ornstein-Uhlenbeck process
Francisco Delgado-Vences, Arelly Ornelas
http://arxiv.org/abs/1901.00795v1
• [math.ST]Energy distance and kernel mean embedding for two sample survival test
Marcos Matabuena
http://arxiv.org/abs/1901.00833v1
• [q-fin.ST]The market nanostructure origin of asset price time reversal asymmetry
Marcus Cordi, Damien Challet, Serge Kassibrakis
http://arxiv.org/abs/1901.00834v1
• [stat.AP]Heavy-Tailed Data Breaches in the Nat-Cat Framework & the Challenge of Insuring Cyber Risks
Annette Hofmann, Spencer Wheatley, Didier Sornette
http://arxiv.org/abs/1901.00699v1
• [stat.CO]A Simple Algorithm for Scalable Monte Carlo Inference
Alexander Borisenko, Maksym Byshkin, Alessandro Lomi
http://arxiv.org/abs/1901.00533v1
• [stat.ME]Efficient augmentation and relaxation learning for individualized treatment rules using observational data
Ying-Qi Zhao, Eric B. Laber, Yang Ning, Sumona Saha, Bruce Sands
http://arxiv.org/abs/1901.00663v1
• [stat.ME]Which practical interventions does the do-operator refer to in causal inference? Illustration on the example of obesity and cancer
Lola Etievant, Vivian Viallon
http://arxiv.org/abs/1901.00772v1
• [stat.ML]Learning a Generator Model from Terminal Bus Data
Nikolay Stulov, Dejan J Sobajic, Yury Maximov, Deepjyoti Deka, Michael Chertkov
http://arxiv.org/abs/1901.00781v1
• [stat.ML]Projecting "better than randomly": How to reduce the dimensionality of very large datasets in a way that outperforms random projections
Michael Wojnowicz, Di Zhang, Glenn Chisholm, Xuan Zhao, Matt Wolff
http://arxiv.org/abs/1901.00630v1
• [stat.ML]Sparse Learning in reproducing kernel Hilbert space
Xin He, Junhui Wang
http://arxiv.org/abs/1901.00615v1
网友评论