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
Geoffrey HintonEmeritus Prof. Comp Sci, U.Toronto & Engineering Fellow, GoogleVerified email at cs.toronto.edu
Xiaojin ZhuProfessor in Computer Science at University of Wisconsin-MadisonVerified email at cs.wisc.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
José Miguel Hernández-LobatoUniversity Lecturer (US Assistant Professor) University of CambridgeVerified email at cam.ac.uk
Carl Edward RasmussenProfessor of Machine Learning, University of CambridgeVerified email at cam.ac.uk
Tommi JaakkolaMITVerified email at csail.mit.edu
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
Richard E TurnerProfessor, University of CambridgeVerified email at cam.ac.uk
Lawrence K SaulProfessor of Computer Science and Engineering, UC San DiegoVerified email at cs.ucsd.edu
Wei Chu（褚崴）AlibabaVerified email at gatsby.ucl.ac.uk
Ryan P. AdamsPrinceton UniversityVerified email at princeton.edu
Neil HoulsbyGoogleVerified email at google.com
Hugh Durrant-WhyteUniversity of SydneyVerified email at sydney.edu.au
James Robert LloydPhD from Machine Learning Group, Cambridge 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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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	4837*	2003
An introduction to variational methods for graphical models MI Jordan, Z Ghahramani, TS Jaakkola, LK Saul Machine learning 37 (2), 183-233, 1999	4371	1999
Dropout as a bayesian approximation: Representing model uncertainty in deep learning Y Gal, Z Ghahramani international conference on machine learning, 1050-1059, 2016	4186	2016
An internal model for sensorimotor integration DM Wolpert, Z Ghahramani, MI Jordan Science 269 (5232), 1880-1882, 1995	3558	1995
Computational principles of movement neuroscience DM Wolpert, Z Ghahramani Nature Neuroscience 3, 1212-1217, 2000	2104	2000
Active learning with statistical models DA Cohn, Z Ghahramani, MI Jordan Journal of Artificial Intelligence Research 4, 129--145, 1996	1968	1996
Factorial hidden Markov models Z Ghahramani, MI Jordan Machine learning 29 (2), 245-273, 1997	1724	1997
Sparse Gaussian processes using pseudo-inputs E Snelson, Z Ghahramani Advances in Neural Information Processing Systems 18, 1257--1264, 2006	1669	2006
Learning from labeled and unlabeled data with label propagation X ZhuЃ, Z GhahramaniЃн	1541	2002
A theoretically grounded application of dropout in recurrent neural networks Y Gal, Z Ghahramani Advances in neural information processing systems 29, 1019-1027, 2016	1430	2016
Probabilistic machine learning and artificial intelligence Z Ghahramani Nature 521 (7553), 452-459, 2015	1267	2015
A unifying review of linear Gaussian models S Roweis, Z Ghahramani Neural computation 11 (2), 305-345, 1999	1203	1999
An introduction to hidden Markov models and Bayesian networks Z Ghahramani Hidden Markov models: applications in computer vision, 9-41, 2001	1072	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	1033	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	1003	2004
Perspectives and problems in motor learning DM Wolpert, Z Ghahramani, JR Flanagan Trends in cognitive sciences 5 (11), 487-494, 2001	962	2001
Infinite latent feature models and the Indian buffet process T Griffiths, Z Ghahramani Advances in Neural Information Processing Systems 18, 475--482, 2006	916	2006
The EM algorithm for mixtures of factor analyzers Z Ghahramani, GE Hinton Technical Report CRG-TR-96-1, University of Toronto, 1996	888	1996
The infinite hidden Markov model MJ Beal, Z Ghahramani, CE Rasmussen Advances in neural information processing systems 1, 577-584, 2002	812	2002
Supervised learning from incomplete data via an EM approach Z Ghahramani, MI Jordan Advances in neural information processing systems, 120-127, 1994	784	1994

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