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. Computer Science, University of TorontoVerified email at cs.toronto.edu
José Miguel Hernández-LobatoProfessor of Machine Learning, University of CambridgeVerified email at cam.ac.uk
Matthew J. BealChicago Trading CompanyVerified email at chicagotrading.com
Katherine HellerGoogle ResearchVerified email at gatsby.ucl.ac.uk
Carl Edward RasmussenProfessor of Machine Learning, University of CambridgeVerified email at cam.ac.uk
David A. KnowlesColumbia University & New York Genome CenterVerified email at columbia.edu
Alexander G. D. G. MatthewsDeepMindVerified email at google.com
Tommi JaakkolaMITVerified email at csail.mit.edu
Sam Roweis (Memorial)New York UniversityVerified email at buffalo.edu
Wei Chu（褚崴）InfVerified email at gatsby.ucl.ac.uk
Richard E TurnerProfessor, University of CambridgeVerified email at cam.ac.uk
Neil HoulsbyGoogleVerified email at google.com
Ryan P. AdamsPrinceton UniversityVerified email at princeton.edu
James Robert LloydPhD from Machine Learning Group, Cambridge UniversityVerified email at cam.ac.uk
Amar ShahPhD student, University of CambridgeVerified email at cam.ac.uk
Hong GeCambridge UniversityVerified email at cam.ac.uk

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	10767	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	5762*	2003
An introduction to variational methods for graphical models MI Jordan, Z Ghahramani, TS Jaakkola, LK Saul Machine learning 37, 183-233, 1999	5458	1999
An internal model for sensorimotor integration DM Wolpert, Z Ghahramani, MI Jordan Science 269 (5232), 1880-1882, 1995	4257	1995
Active learning with statistical models DA Cohn, Z Ghahramani, MI Jordan Journal of Artificial Intelligence Research 4, 129--145, 1996	2535	1996
Computational principles of movement neuroscience DM Wolpert, Z Ghahramani Nature Neuroscience 3, 1212-1217, 2000	2488	2000
Sparse Gaussian processes using pseudo-inputs E Snelson, Z Ghahramani Advances in Neural Information Processing Systems 18, 1257--1264, 2006	2289	2006
Probabilistic machine learning and artificial intelligence Z Ghahramani Nature 521 (7553), 452-459, 2015	2287	2015
Learning from labeled and unlabeled data with label propagation X ZhuЃ, Z GhahramaniЃн ProQuest number: information to all users, 2002	2063	2002
A theoretically grounded application of dropout in recurrent neural networks Y Gal, Z Ghahramani Advances in neural information processing systems 29, 2016	2052	2016
Deep bayesian active learning with image data Y Gal, R Islam, Z Ghahramani International conference on machine learning, 1183-1192, 2017	1945	2017
Factorial hidden Markov models Z Ghahramani, M Jordan Advances in neural information processing systems 8, 1995	1881	1995
A unifying review of linear Gaussian models S Roweis, Z Ghahramani Neural computation 11 (2), 305-345, 1999	1333	1999
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	1319	2010
An introduction to hidden Markov models and Bayesian networks Z Ghahramani International journal of pattern recognition and artificial intelligence 15 …, 2001	1278	2001
Unsupervised learning Z Ghahramani Summer school on machine learning, 72-112, 2003	1181	2003
Perspectives and problems in motor learning DM Wolpert, Z Ghahramani, JR Flanagan Trends in cognitive sciences 5 (11), 487-494, 2001	1094	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	1077	2004
The EM algorithm for mixtures of factor analyzers Z Ghahramani, GE Hinton Technical Report CRG-TR-96-1, University of Toronto, 1996	1008	1996
Infinite latent feature models and the Indian buffet process T Griffiths, Z Ghahramani Advances in Neural Information Processing Systems 18, 475--482, 2006	963*	2006

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