Zoubin Ghahramani

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Michael I. JordanProfessor of Electrical Engineering and Computer Sciences and Professor of Statistics, UC BerkeleyVerified email at cs.berkeley.edu
Daniel WolpertProfessor of Neuroscience, Columbia UniversityVerified email at columbia.edu
Yarin GalAssociate Professor, University of OxfordVerified email at cs.ox.ac.uk
Xiaojin ZhuProfessor in Computer Science at University of Wisconsin-MadisonVerified email at cs.wisc.edu
Geoffrey HintonEmeritus Prof. Comp Sci, U.Toronto & Engineering Fellow, GoogleVerified email at cs.toronto.edu
Katherine HellerGoogle ResearchVerified email at gatsby.ucl.ac.uk
Matthew J. BealChicago Trading CompanyVerified email at chicagotrading.com
José Miguel Hernández-LobatoProfessor of Machine Learning, University of CambridgeVerified email at cam.ac.uk
Carl Edward RasmussenProfessor of Machine Learning, University of CambridgeVerified email at cam.ac.uk
Alexander G. D. G. MatthewsDeepMindVerified email at google.com
David A. KnowlesColumbia University & New York Genome CenterVerified email at columbia.edu
Sam Roweis (Memorial)New York UniversityVerified email at buffalo.edu
Tommi JaakkolaMITVerified email at csail.mit.edu
Wei Chu（褚崴）AlibabaVerified email at gatsby.ucl.ac.uk
Richard E TurnerProfessor, University of CambridgeVerified email at cam.ac.uk
Lawrence K SaulSenior Research Scientist, Center for Computational Mathematics, Flatiron InstituteVerified email at flatironinstitute.org
Neil HoulsbyGoogleVerified email at google.com
Ryan P. AdamsPrinceton UniversityVerified email at princeton.edu
Hugh Durrant-WhyteUniversity of SydneyVerified email at sydney.edu.au
James HensmanMicrosoft ResearchVerified email at microsoft.com

Zoubin Ghahramani

Professor, University of Cambridge, and Distinguished Researcher, Google

Verified email at eng.cam.ac.uk - Homepage

Machine Learning Bayesian Statistics Neural Networks Artificial Intelligence


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Dropout as a bayesian approximation: Representing model uncertainty in deep learning Y Gal, Z Ghahramani international conference on machine learning, 1050-1059, 2016	6820	2016
Semi-supervised learning using Gaussian fields and harmonic functions X Zhu, Z Ghahramani, J Lafferty Proceedings of the Twentieth International Conference on Machine Learning …, 2003	5231*	2003
An introduction to variational methods for graphical models MI Jordan, Z Ghahramani, TS Jaakkola, LK Saul Machine learning 37, 183-233, 1999	4841	1999
An internal model for sensorimotor integration DM Wolpert, Z Ghahramani, MI Jordan Science 269 (5232), 1880-1882, 1995	3859	1995
Computational principles of movement neuroscience DM Wolpert, Z Ghahramani Nature Neuroscience 3, 1212-1217, 2000	2270	2000
Active learning with statistical models DA Cohn, Z Ghahramani, MI Jordan Journal of Artificial Intelligence Research 4, 129--145, 1996	2211	1996
Sparse Gaussian processes using pseudo-inputs E Snelson, Z Ghahramani Advances in Neural Information Processing Systems 18, 1257--1264, 2006	1955	2006
Factorial hidden Markov models Z Ghahramani, M Jordan Advances in Neural Information Processing Systems 8, 1995	1788	1995
Learning from labeled and unlabeled data with label propagation X ZhuЃ, Z GhahramaniЃн	1753	2002
A theoretically grounded application of dropout in recurrent neural networks Y Gal, Z Ghahramani Advances in neural information processing systems 29, 2016	1719	2016
Probabilistic machine learning and artificial intelligence Z Ghahramani Nature 521 (7553), 452-459, 2015	1669	2015
A unifying review of linear Gaussian models S Roweis, Z Ghahramani Neural computation 11 (2), 305-345, 1999	1259	1999
Deep bayesian active learning with image data Y Gal, R Islam, Z Ghahramani International conference on machine learning, 1183-1192, 2017	1227	2017
Kronecker graphs: an approach to modeling networks. J Leskovec, D Chakrabarti, J Kleinberg, C Faloutsos, Z Ghahramani Journal of Machine Learning Research 11 (2), 2010	1184	2010
An introduction to hidden Markov models and Bayesian networks Z Ghahramani International journal of pattern recognition and artificial intelligence 15 …, 2001	1164	2001
Perspectives and problems in motor learning DM Wolpert, Z Ghahramani, JR Flanagan Trends in cognitive sciences 5 (11), 487-494, 2001	1035	2001
Simultaneous localization and mapping with sparse extended information filters S Thrun, Y Liu, D Koller, AY Ng, Z Ghahramani, H Durrant-Whyte The international journal of robotics research 23 (7-8), 693-716, 2004	1032	2004
The EM algorithm for mixtures of factor analyzers Z Ghahramani, GE Hinton Technical Report CRG-TR-96-1, University of Toronto, 1996	926	1996
Infinite latent feature models and the Indian buffet process T Griffiths, Z Ghahramani Advances in Neural Information Processing Systems 18, 475--482, 2006	924*	2006
Unsupervised learning Z Ghahramani Advanced Lectures on Machine Learning: ML Summer Schools 2003, Canberra …, 2004	831	2004

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