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
Yarin GalAssociate Professor, University of OxfordVerified email at cs.ox.ac.uk
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. Computer Science, University of TorontoVerified email at cs.toronto.edu
Carl Edward RasmussenProfessor of Machine Learning, University of CambridgeVerified email at cam.ac.uk
Katherine HellerGoogle ResearchVerified email at gatsby.ucl.ac.uk
José Miguel Hernández-LobatoProfessor of Machine Learning, University of CambridgeVerified email at cam.ac.uk
Matthew J. BealChicago Trading CompanyVerified email at chicagotrading.com
David A. KnowlesColumbia University & New York Genome CenterVerified email at columbia.edu
Tommi JaakkolaMITVerified email at csail.mit.edu
Sam Roweis (Memorial)New York UniversityVerified email at buffalo.edu
Alexander G. D. G. MatthewsDeepMindVerified email at google.com
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
Hong GeCambridge UniversityVerified email at cam.ac.uk
Amar ShahPhD student, University of CambridgeVerified 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	11964	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	5867*	2003
An introduction to variational methods for graphical models MI Jordan, Z Ghahramani, TS Jaakkola, LK Saul Machine learning 37, 183-233, 1999	5646	1999
An internal model for sensorimotor integration DM Wolpert, Z Ghahramani, MI Jordan Science 269 (5232), 1880-1882, 1995	4358	1995
Active learning with statistical models DA Cohn, Z Ghahramani, MI Jordan Journal of Artificial Intelligence Research 4, 129--145, 1996	2632	1996
Computational principles of movement neuroscience DM Wolpert, Z Ghahramani Nature Neuroscience 3, 1212-1217, 2000	2514	2000
Probabilistic machine learning and artificial intelligence Z Ghahramani Nature 521 (7553), 452-459, 2015	2452	2015
Sparse Gaussian processes using pseudo-inputs E Snelson, Z Ghahramani Advances in Neural Information Processing Systems 18, 1257--1264, 2006	2372	2006
Learning from labeled and unlabeled data with label propagation X ZhuЃ, Z GhahramaniЃн ProQuest number: information to all users, 2002	2144	2002
Deep bayesian active learning with image data Y Gal, R Islam, Z Ghahramani International conference on machine learning, 1183-1192, 2017	2126	2017
A theoretically grounded application of dropout in recurrent neural networks Y Gal, Z Ghahramani Advances in neural information processing systems 29, 2016	2103	2016
Factorial hidden Markov models Z Ghahramani, M Jordan Advances in neural information processing systems 8, 1995	1911	1995
A unifying review of linear Gaussian models S Roweis, Z Ghahramani Neural computation 11 (2), 305-345, 1999	1356	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	1340	2010
An introduction to hidden Markov models and Bayesian networks Z Ghahramani International journal of pattern recognition and artificial intelligence 15 …, 2001	1307	2001
Unsupervised learning Z Ghahramani Summer school on machine learning, 72-112, 2003	1299	2003
Gemma: Open models based on gemini research and technology G Team, T Mesnard, C Hardin, R Dadashi, S Bhupatiraju, S Pathak, ... arXiv preprint arXiv:2403.08295, 2024	1121	2024
Perspectives and problems in motor learning DM Wolpert, Z Ghahramani, JR Flanagan Trends in cognitive sciences 5 (11), 487-494, 2001	1121	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	1088	2004
The EM algorithm for mixtures of factor analyzers Z Ghahramani, GE Hinton Technical Report CRG-TR-96-1, University of Toronto, 1996	1020	1996

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