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
Matthew J. BealChicago Trading CompanyVerified email at chicagotrading.com
Katherine HellerGoogle ResearchVerified email at gatsby.ucl.ac.uk
José Miguel Hernández-LobatoAssociate Professor in Machine Learning, University of CambridgeVerified email at cam.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
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 SaulProfessor of Computer Science and Engineering, UC San DiegoVerified email at cs.ucsd.edu
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 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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Dropout as a bayesian approximation: Representing model uncertainty in deep learning Y Gal, Z Ghahramani international conference on machine learning, 1050-1059, 2016	5529	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	5043*	2003
An introduction to variational methods for graphical models MI Jordan, Z Ghahramani, TS Jaakkola, LK Saul Machine learning 37 (2), 183-233, 1999	4596	1999
An internal model for sensorimotor integration DM Wolpert, Z Ghahramani, MI Jordan Science 269 (5232), 1880-1882, 1995	3730	1995
Computational principles of movement neuroscience DM Wolpert, Z Ghahramani Nature Neuroscience 3, 1212-1217, 2000	2207	2000
Active learning with statistical models DA Cohn, Z Ghahramani, MI Jordan Journal of Artificial Intelligence Research 4, 129--145, 1996	2083	1996
Sparse Gaussian processes using pseudo-inputs E Snelson, Z Ghahramani Advances in Neural Information Processing Systems 18, 1257--1264, 2006	1797	2006
Factorial hidden Markov models Z Ghahramani, M Jordan Advances in Neural Information Processing Systems 8, 1995	1740	1995
Learning from labeled and unlabeled data with label propagation X ZhuЃ, Z GhahramaniЃн	1651	2002
A theoretically grounded application of dropout in recurrent neural networks Y Gal, Z Ghahramani Advances in neural information processing systems 29, 2016	1592	2016
Probabilistic machine learning and artificial intelligence Z Ghahramani Nature 521 (7553), 452-459, 2015	1483	2015
A unifying review of linear Gaussian models S Roweis, Z Ghahramani Neural computation 11 (2), 305-345, 1999	1220	1999
An introduction to hidden Markov models and Bayesian networks Z Ghahramani Hidden Markov models: applications in computer vision, 9-41, 2001	1132	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	1130	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	1019	2004
Perspectives and problems in motor learning DM Wolpert, Z Ghahramani, JR Flanagan Trends in cognitive sciences 5 (11), 487-494, 2001	1004	2001
Deep bayesian active learning with image data Y Gal, R Islam, Z Ghahramani International Conference on Machine Learning, 1183-1192, 2017	973	2017
Infinite latent feature models and the Indian buffet process T Griffiths, Z Ghahramani Advances in Neural Information Processing Systems 18, 475--482, 2006	905*	2006
The EM algorithm for mixtures of factor analyzers Z Ghahramani, GE Hinton Technical Report CRG-TR-96-1, University of Toronto, 1996	898	1996
The infinite hidden Markov model M Beal, Z Ghahramani, C Rasmussen Advances in neural information processing systems 14, 2001	814	2001

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