Hoang Anh Dau

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Eamonn KeoghDistinguished Professor of Computer Science, University of California - RiversideCorreu electrònic verificat a cs.ucr.edu
Yan ZhuPhD Candidate in Computer Science, University of California, RiversideCorreu electrònic verificat a ucr.edu
Chin-Chia Michael YehVisa ResearchCorreu electrònic verificat a visa.com
Nurjahan BegumUniversity of California, RiversideCorreu electrònic verificat a cs.ucr.edu
Tony BagnallProfessor of Computer Science, University of East AngliaCorreu electrònic verificat a uea.ac.uk
Diego Furtado SilvaAssistant Professor, Departamento de Computação - Universidade Federal de São CarlosCorreu electrònic verificat a ufscar.br
Abdullah MueenAssociate Professor, Department of Computer Science, University of New MexicoCorreu electrònic verificat a cs.unm.edu
Liudmila UlanovaPhD in Computer Science, University of California, RiversideCorreu electrònic verificat a ucr.edu
Shaghayegh GharghabiDepartment of Electrical and Computer Engineering, University of California, RiversideCorreu electrònic verificat a ucr.edu
Germain ForestierFull Professor of Computer Science, Université de Haute AlsaceCorreu electrònic verificat a uha.fr
François PetitjeanAustralian GovernmentCorreu electrònic verificat a monash.edu
Andy SongA/Prof of AI, School of Computing Technologies, CIAIRI, RMIT UniversityCorreu electrònic verificat a rmit.edu.au
Chotirat Ann RatanamahatanaAssociate Professor, Dept. of Computer Engineering, Chulalongkorn UniversityCorreu electrònic verificat a chula.ac.th
Geoff WebbProfessor of Information Technology Research, Monash UniversityCorreu electrònic verificat a monash.edu
Zachary ZimmermanGoogleCorreu electrònic verificat a google.com
Flora SalimCisco Chair in Digital Transport, Professor, Computer Science and Engineering, UNSWCorreu electrònic verificat a unsw.edu.au
Margaret HamiltonProfessor, School of Computing Technologies, RMIT UniversityCorreu electrònic verificat a rmit.edu.au

Segueix

Hoang Anh Dau

Machine Learning Research Engineer, Uber

Correu electrònic verificat a uber.com - Pàgina d'inici

Data Mining Data Science Time Series Machine Learning


Títol Ordena per cites Ordena per any Ordena per títol	Citada per Citada per	Any
Matrix profile I: all pairs similarity joins for time series: a unifying view that includes motifs, discords and shapelets CCM Yeh, Y Zhu, L Ulanova, N Begum, Y Ding, HA Dau, DF Silva, ... 2016 IEEE 16th international conference on data mining (ICDM), 1317-1322, 2016	436	2016
The UCR time series archive HA Dau, A Bagnall, K Kamgar, CCM Yeh, Y Zhu, S Gharghabi, ... IEEE/CAA Journal of Automatica Sinica 6 (6), 1293-1305, 2019	395	2019
The UEA multivariate time series classification archive, 2018 A Bagnall, HA Dau, J Lines, M Flynn, J Large, A Bostrom, P Southam, ... arXiv preprint arXiv:1811.00075, 2018	148	2018
Generating synthetic time series to augment sparse datasets G Forestier, F Petitjean, HA Dau, GI Webb, E Keogh 2017 IEEE international conference on data mining (ICDM), 865-870, 2017	91	2017
Time series joins, motifs, discords and shapelets: a unifying view that exploits the matrix profile CCM Yeh, Y Zhu, L Ulanova, N Begum, Y Ding, HA Dau, Z Zimmerman, ... Data Mining and Knowledge Discovery 32 (1), 83-123, 2018	90	2018
Anomaly detection using replicator neural networks trained on examples of one class HA Dau, V Ciesielski, A Song Asia-Pacific Conference on Simulated Evolution and Learning, 311-322, 2014	82	2014
Matrix profile v: A generic technique to incorporate domain knowledge into motif discovery HA Dau, E Keogh Proceedings of the 23rd ACM SIGKDD international conference on knowledge …, 2017	56	2017
Optimizing dynamic time warping’s window width for time series data mining applications HA Dau, DF Silva, F Petitjean, G Forestier, A Bagnall, A Mueen, E Keogh Data mining and knowledge discovery 32 (4), 1074-1120, 2018	52	2018
Semi-supervision dramatically improves time series clustering under dynamic time warping HA Dau, N Begum, E Keogh Proceedings of the 25th ACM International on Conference on Information and …, 2016	40	2016
The ucr time series classification archive. 2018 HA Dau, E Keogh, K Kamgar, CCM Yeh, Y Zhu, S Gharghabi, ... URL www. cs. ucr. edu/~ eamonn/time_series_data_2018, 2018	31	2018
Judicious setting of Dynamic Time Warping's window width allows more accurate classification of time series HA Dau, DF Silva, F Petitjean, G Forestier, A Bagnall, E Keogh 2017 IEEE international conference on big data (big data), 917-922, 2017	25	2017
Generalized dynamic time warping: Unleashing the warping power hidden in point-wise distances R Neamtu, R Ahsan, EA Rundensteiner, G Sarkozy, E Keogh, HA Dau, ... 2018 IEEE 34th International Conference on Data Engineering (ICDE), 521-532, 2018	15	2018
Phone based fall detection by genetic programming HA Dau, FD Salim, A Song, L Hedin, M Hamilton Proceedings of the 13th International Conference on Mobile and Ubiquitous …, 2014	10	2014
Anomaly detection for insertion tasks in robotic assembly using Gaussian process models D Romeres, DK Jha, W Yerazunis, D Nikovski, HA Dau 2019 18th European Control Conference (ECC), 1017-1022, 2019	8	2019
Evolving PCB visual inspection programs using genetic programming F Xie, AH Dau, AL Uitdenbogerd, A Song 2013 28th International Conference on Image and Vision Computing New Zealand …, 2013	6	2013
Online amnestic dtw to allow real-time golden batch monitoring CCM Yeh, Y Zhu, HA Dau, A Darvishzadeh, M Noskov, E Keogh Proceedings of the 25th ACM SIGKDD International Conference on Knowledge …, 2019	5	2019
A general framework for density based time series clustering exploiting a novel admissible pruning strategy N Begum, L Ulanova, HA Dau, J Wang, E Keogh arXiv preprint arXiv:1612.00637, 2016	5	2016
Anomaly Detection in Discrete Manufacturing Systems using Event Relationship Tables. E Laftchiev, X Sun, HA Dau, D Nikovski DX@ Safeprocess, 2018	2	2018
Genetic Programming for Channel Selection from Multi-stream Sensor Data with Application on Learning Risky Driving Behaviours HA Dau, A Song, F Xie, FD Salim, V Ciesielski Asia-Pacific Conference on Simulated Evolution and Learning, 542-553, 2014		2014

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