Kiri Wagstaff

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	すべて	2020 年以来
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32 件の論文

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David R. ThompsonJet Propulsion Laboratory, California Institute of Technology確認したメールアドレス: jpl.nasa.gov
Steve ChienJPL Fellow, Jet Propulsion Laboratory, Calif. Inst. Tech.確認したメールアドレス: jpl.nasa.gov
Claire CardieProfessor of Computer Science, Cornell University確認したメールアドレス: cs.cornell.edu
Gary DoranJet Propulsion Laboratory確認したメールアドレス: jpl.nasa.gov
B. BueResearch Data Scientist, Jet Propulsion Laboratory確認したメールアドレス: jpl.nasa.gov
Umaa RebbapragadaJet Propulsion Laboratory確認したメールアドレス: jpl.nasa.gov
Stefan SchroedlAtomwise, Inc確認したメールアドレス: atomwise.com
Lukas MandrakeJet Propulsion Laboratory確認したメールアドレス: jpl.nasa.gov
Randall WaythSKA-Low Senior Commissioning Scientist確認したメールアドレス: skao.int
Adam DellerCentre for Astrophysics & Supercomputing, Swinburne University of Technology確認したメールアドレス: astro.swin.edu.au
Steven TingayProfessor of Radio Astronomy, Curtin University確認したメールアドレス: curtin.edu.au
Marie desJardinsDean and Professor of Computer Science確認したメールアドレス: simmons.edu
Hannah Rae KernerAssistant Professor, Arizona State University確認したメールアドレス: umd.edu
Ian DavidsonProfessor of Computer Science, University of California - Davis確認したメールアドレス: cs.ucdavis.edu
Sugato BasuDistinguished Scientist at Google確認したメールアドレス: google.com
Benyang TangJet Propulsion Laboratory確認したメールアドレス: jpl.nasa.gov
Thomas J. FuchsIcahn School of Medicine at Mount Sinai確認したメールアドレス: mssm.edu
Heni Ben AmorAssociate Professor, Arizona State University確認したメールアドレス: asu.edu
Dmitriy BekkerJohns Hopkins University Applied Physics Laboratory確認したメールアドレス: jhuapl.edu
Heather Terran Laneathenahealth, Inc.確認したメールアドレス: illation.net

フォロー

Kiri Wagstaff

所属不明

確認したメールアドレス: wkiri.com - ホームページ

Machine Learning Geology Astronomy Education Library Science


タイトル引用回数順公開年順タイトル順	引用先引用先	年
Constrained k-means clustering with background knowledge K Wagstaff, C Cardie, S Rogers, S Schrödl Icml 1, 577-584, 2001	4054	2001
Clustering with instance-level constraints K Wagstaff, C Cardie AAAI/IAAI 1097 (577-584), 197, 2000	907	2000
Constrained clustering: Advances in algorithms, theory, and applications S Basu, I Davidson, K Wagstaff Chapman and Hall/CRC, 2008	764	2008
Machine learning that matters K Wagstaff Proceedings of the Twenty-Ninth International Conference on Machine Learning …, 2012	433	2012
Mining GPS traces for map refinement S Schroedl, K Wagstaff, S Rogers, P Langley, C Wilson Data mining and knowledge Discovery 9, 59-87, 2004	333	2004
Noun phrase coreference as clustering C Cardie, K Wagstaff 1999 Joint SIGDAT Conference on Empirical Methods in Natural Language …, 1999	326	1999
Measuring constraint-set utility for partitional clustering algorithms I Davidson, KL Wagstaff, S Basu European conference on principles of data mining and knowledge discovery …, 2006	288	2006
Machine learning for science and society C Rudin, KL Wagstaff Machine Learning 95, 1-9, 2014	168	2014
The commensal real-time ASKAP fast-transients (CRAFT) survey JP Macquart, M Bailes, NDR Bhat, GC Bower, JD Bunton, S Chatterjee, ... Publications of the Astronomical Society of Australia 27 (3), 272-282, 2010	165	2010
VAST: an ASKAP survey for variables and slow transients T Murphy, S Chatterjee, DL Kaplan, J Banyer, ME Bell, HE Bignall, ... Publications of the Astronomical Society of Australia 30, e006, 2013	155	2013
Multidocument summarization via information extraction M White, T Korelsky, C Cardie, V Ng, D Pierce, K Wagstaff Proceedings of the first international conference on Human language …, 2001	148	2001
Intelligent clustering with instance-level constraints KL Wagstaff Cornell University, 2002	139	2002
Clustering with missing values: no imputation required. K Wagstaff Pasadena, CA: Jet Propulsion Laboratory, National Aeronautics and Space …, 2004	116	2004
Alpha seeding for support vector machines D DeCoste, K Wagstaff Proceedings of the sixth ACM SIGKDD international conference on Knowledge …, 2000	110	2000
Onboard autonomy on the intelligent payload experiment cubesat mission S Chien, J Doubleday, DR Thompson, KL Wagstaff, J Bellardo, C Francis, ... Journal of Aerospace Information Systems 14 (6), 307-315, 2017	107	2017
When is constrained clustering beneficial, and why KL Wagstaff, S Basu, I Davidson Ionosphere 58 (60.1), 62-63, 2006	92	2006
Active constrained clustering by examining spectral eigenvectors Q Xu, M desJardins, KL Wagstaff Discovery Science: 8th International Conference, DS 2005, Singapore, October …, 2005	84	2005
V-fastr: The vlba fast radio transients experiment RB Wayth, WF Brisken, AT Deller, WA Majid, DR Thompson, SJ Tingay, ... The Astrophysical Journal 735 (2), 97, 2011	76	2011
Deep mars: Cnn classification of mars imagery for the pds imaging atlas K Wagstaff, Y Lu, A Stanboli, K Grimes, T Gowda, J Padams Proceedings of the AAAI Conference on Artificial Intelligence 32 (1), 2018	73	2018
Constrained Spectral Clustering under a Local Proximity Structure Assumption. Q Xu, Marie desJardins, K Wagstaff FLAIRS, 866-867, 2005	63	2005

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