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
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
Yarin GalAssociate Professor, University of OxfordVerified email at cs.ox.ac.uk
Katherine HellerGoogle ResearchVerified email at gatsby.ucl.ac.uk
Matthew J. BealChicago Trading CompanyVerified email at chicagotrading.com
Carl Edward RasmussenProfessor of Machine Learning, University of CambridgeVerified email at cam.ac.uk
José Miguel Hernández-LobatoAssociate Professor in Machine Learning, University of CambridgeVerified email at cam.ac.uk
Tommi JaakkolaMITVerified email at csail.mit.edu
David A. KnowlesColumbia University & New York Genome CenterVerified email at columbia.edu
Sam Roweis (Memorial)New York UniversityVerified email at buffalo.edu
Alexander G. D. G. MatthewsDeepMindVerified email at google.com
Wei Chu（褚崴）AlibabaVerified email at gatsby.ucl.ac.uk
Lawrence K SaulProfessor of Computer Science and Engineering, UC San DiegoVerified email at cs.ucsd.edu
Richard E TurnerProfessor, University of CambridgeVerified email at cam.ac.uk
Ryan P. AdamsPrinceton UniversityVerified email at princeton.edu
Hugh Durrant-WhyteUniversity of SydneyVerified email at sydney.edu.au
James Robert LloydPhD from Machine Learning Group, Cambridge UniversityVerified email at cam.ac.uk
Neil HoulsbyGoogleVerified email at google.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	5261	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	4999*	2003
An introduction to variational methods for graphical models MI Jordan, Z Ghahramani, TS Jaakkola, LK Saul Machine learning 37 (2), 183-233, 1999	4534	1999
An internal model for sensorimotor integration DM Wolpert, Z Ghahramani, MI Jordan Science 269 (5232), 1880-1882, 1995	3704	1995
Computational principles of movement neuroscience DM Wolpert, Z Ghahramani Nature Neuroscience 3, 1212-1217, 2000	2200	2000
Active learning with statistical models DA Cohn, Z Ghahramani, MI Jordan Journal of Artificial Intelligence Research 4, 129--145, 1996	2065	1996
Sparse Gaussian processes using pseudo-inputs E Snelson, Z Ghahramani Advances in Neural Information Processing Systems 18, 1257--1264, 2006	1769	2006
Factorial hidden Markov models Z Ghahramani, M Jordan Advances in Neural Information Processing Systems 8, 1995	1711	1995
Learning from labeled and unlabeled data with label propagation X ZhuЃ, Z GhahramaniЃн	1641	2002
A theoretically grounded application of dropout in recurrent neural networks Y Gal, Z Ghahramani Advances in neural information processing systems 29, 2016	1578	2016
Probabilistic machine learning and artificial intelligence Z Ghahramani Nature 521 (7553), 452-459, 2015	1445	2015
A unifying review of linear Gaussian models S Roweis, Z Ghahramani Neural computation 11 (2), 305-345, 1999	1216	1999
An introduction to hidden Markov models and Bayesian networks Z Ghahramani Hidden Markov models: applications in computer vision, 9-41, 2001	1122	2001
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	1094	2010
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	1023	2004
Perspectives and problems in motor learning DM Wolpert, Z Ghahramani, JR Flanagan Trends in cognitive sciences 5 (11), 487-494, 2001	995	2001
Deep bayesian active learning with image data Y Gal, R Islam, Z Ghahramani International Conference on Machine Learning, 1183-1192, 2017	925	2017
Infinite latent feature models and the Indian buffet process T Griffiths, Z Ghahramani Advances in Neural Information Processing Systems 18, 475--482, 2006	901*	2006
The EM algorithm for mixtures of factor analyzers Z Ghahramani, GE Hinton Technical Report CRG-TR-96-1, University of Toronto, 1996	896	1996
The infinite hidden Markov model M Beal, Z Ghahramani, C Rasmussen Advances in neural information processing systems 14, 2001	813	2001

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