Brian Kulis

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Inderjit S. DhillonDistinguished Scientist at Google & Professor of Computer Science at UT Austin, Ex-VP at AmazonVerified email at cs.utexas.edu
Kate SaenkoBoston UniversityVerified email at bu.edu
Trevor DarrellProfessor of Computer Science, U.C. BerkeleyVerified email at eecs.berkeley.edu
Prateek JainGoogle Research IndiaVerified email at google.com
Kristen GraumanProfessor of Computer Science, University of Texas at AustinVerified email at cs.utexas.edu
Suvrit SraTUM and MITVerified email at mit.edu
Mario FritzFaculty CISPA Helmholtz Center for Information Security; Professor Saarland UniversityVerified email at cispa.de
Xide XiaResearch Scientist, Meta GenAIVerified email at fb.com
Michael I. JordanProfessor of Electrical Engineering and Computer Sciences and Professor of Statistics, UC BerkeleyVerified email at cs.berkeley.edu
Bart SelmanProfessor of Computer Science, Cornell UniversityVerified email at cs.cornell.edu
John HopcroftCornell UniversityVerified email at cs.cornell.edu
Ali SiahkamariPhD, Boston UniversityVerified email at bu.edu
Raymond MooneyProfessor of Computer Science, University of Texas at AustinVerified email at cs.utexas.edu
Sugato BasuDistinguished Scientist at GoogleVerified email at google.com
Judy HoffmanAssistant Professor, Georgia TechVerified email at gatech.edu
Matyas A SustikUniversity of Texas at AustinVerified email at cs.utexas.edu
Venkatesh SaligramaAI Professor and Amazon ScholarVerified email at bu.edu
Jiawen ChenResearch Scientist at AdobeVerified email at adobe.com
Tianfan XueInformation Engineering Department, The Chinese University of Hong KongVerified email at ie.cuhk.edu.hk
Kun HeResearch Scientist, Reality Labs @ MetaVerified email at meta.com

Brian Kulis

Associate Professor at Boston University & Amazon Scholar

Verified email at bu.edu - Homepage

Machine Learning Artificial Intelligence Computer Vision


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Adapting visual category models to new domains K Saenko, B Kulis, M Fritz, T Darrell Computer Vision–ECCV 2010: 11th European Conference on Computer Vision …, 2010	3567	2010
Information-theoretic metric learning JV Davis, B Kulis, P Jain, S Sra, IS Dhillon Proceedings of the 24th international conference on Machine learning, 209-216, 2007	2695	2007
Kernel k-means: spectral clustering and normalized cuts IS Dhillon, Y Guan, B Kulis Proceedings of the tenth ACM SIGKDD international conference on Knowledge …, 2004	1728	2004
Weighted graph cuts without eigenvectors a multilevel approach IS Dhillon, Y Guan, B Kulis IEEE transactions on pattern analysis and machine intelligence 29 (11), 1944 …, 2007	1362	2007
Metric learning: A survey B Kulis Foundations and Trends® in Machine Learning 5 (4), 287-364, 2013	1231	2013
Kernelized locality-sensitive hashing for scalable image search B Kulis, K Grauman 2009 IEEE 12th international conference on computer vision, 2130-2137, 2009	1143	2009
Learning to hash with binary reconstructive embeddings B Kulis, T Darrell Advances in neural information processing systems 22, 2009	1042	2009
What you saw is not what you get: Domain adaptation using asymmetric kernel transforms B Kulis, K Saenko, T Darrell CVPR 2011, 1785-1792, 2011	913	2011
Semi-supervised graph clustering: a kernel approach B Kulis, S Basu, I Dhillon, R Mooney Proceedings of the 22nd international conference on machine learning, 457-464, 2005	679	2005
Revisiting k-means: New algorithms via Bayesian nonparametrics B Kulis, MI Jordan arXiv preprint arXiv:1111.0352, 2011	482	2011
Tracking evolving communities in large linked networks J Hopcroft, O Khan, B Kulis, B Selman Proceedings of the National Academy of Sciences 101 (suppl_1), 5249-5253, 2004	472	2004
Kernelized locality-sensitive hashing B Kulis, K Grauman IEEE Transactions on Pattern Analysis and Machine Intelligence 34 (6), 1092-1104, 2011	426	2011
Fast image search for learned metrics P Jain, B Kulis, K Grauman 2008 IEEE Conference on computer vision and pattern recognition, 1-8, 2008	358	2008
W-net: A deep model for fully unsupervised image segmentation X Xia, B Kulis arXiv preprint arXiv:1711.08506, 2017	351	2017
Deep metric learning to rank F Cakir, K He, X Xia, B Kulis, S Sclaroff Proceedings of the IEEE/CVF conference on computer vision and pattern …, 2019	337	2019
Fast similarity search for learned metrics B Kulis, P Jain, K Grauman IEEE Transactions on Pattern Analysis and Machine Intelligence 31 (12), 2143 …, 2009	327	2009
Low-Rank Kernel Learning with Bregman Matrix Divergences. B Kulis, MA Sustik, IS Dhillon Journal of Machine Learning Research 10 (2), 2009	316	2009
A unified view of kernel k-means, spectral clustering and graph cuts IS Dhillon, Y Guan, B Kulis Computer Science Department, University of Texas at Austin, 2004	291	2004
Online metric learning and fast similarity search P Jain, B Kulis, I Dhillon, K Grauman Advances in neural information processing systems 21, 2008	265	2008
Learning low-rank kernel matrices B Kulis, M Sustik, I Dhillon Proceedings of the 23rd international conference on Machine learning, 505-512, 2006	249	2006

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