Zoubin Ghahramani - Google Scholar Citations

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Michael I. JordanProfessor of EECS and Professor of Statistics, University of California, 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
Katherine HellerDuke UniversityVerified 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-LobatoUniversity Lecturer (US Assistant Professor) University of CambridgeVerified email at cam.ac.uk
Sam Roweis (Memorial)New York UniversityVerified email at buffalo.edu
Yarin GalAssociate Professor, University of OxfordVerified email at cs.ox.ac.uk
David A. KnowlesStanford UniversityVerified email at stanford.edu
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
Hugh Durrant-WhyteUniversity of SydneyVerified email at sydney.edu.au
Naonori UedaNTT コミュニケーション科学基礎研究所Verified email at lab.ntt.co.jp
Daphne KollerCEO and Founder, insitroVerified email at insitro.com
Thomas L. GriffithsProfessor of Psychology and Computer Science, Princeton UniversityVerified email at princeton.edu
James Robert LloydPhD from Machine Learning Group, Cambridge UniversityVerified email at cam.ac.uk
Jurgen Van GaelFacebookVerified email at fb.com
Neil HoulsbyGoogleVerified email at google.com

Zoubin Ghahramani

Professor, University of Cambridge, and Chief Scientist, Uber

Verified email at eng.cam.ac.uk - Homepage

Machine Learning Bayesian Statistics Neural Networks Artificial Intelligence


Title	Cited by	Year
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	3250	2003
An introduction to variational methods for graphical models MI Jordan, Z Ghahramani, TS Jaakkola, LK Saul Machine learning 37 (2), 183-233, 1999	3129	1999
An internal model for sensorimotor integration DM Wolpert, Z Ghahramani, MI Jordan Science 269 (5232), 1880-1882, 1995	2833	1995
Computational principles of movement neuroscience DM Wolpert, Z Ghahramani Nature Neuroscience 3, 1212-1217, 2000	1671	2000
Active learning with statistical models DA Cohn, Z Ghahramani, MI Jordan Journal of Artificial Intelligence Research 4, 129--145, 1996	1494	1996
Factorial hidden Markov models Z Ghahramani, MI Jordan Advances in Neural Information Processing Systems, 472-478, 1996	1447	1996
Learning from labeled and unlabeled data with label propagation X Zhu, Z Ghahramani Technical Report CMU-CALD-02-107, Carnegie Mellon University, 2002	1032	2002
Sparse Gaussian processes using pseudo-inputs E Snelson, Z Ghahramani Advances in Neural Information Processing Systems 18, 1257--1264, 2006	1026	2006
A unifying review of linear Gaussian models S Roweis, Z Ghahramani Neural computation 11 (2), 305-345, 1999	1012	1999
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	873	2004
Dropout as a bayesian approximation: Representing model uncertainty in deep learning Y Gal, Z Ghahramani international conference on machine learning, 1050-1059, 2016	827	2016
Perspectives and problems in motor learning DM Wolpert, Z Ghahramani, JR Flanagan Trends in cognitive sciences 5 (11), 487-494, 2001	806	2001
Infinite latent feature models and the Indian buffet process T Griffiths, Z Ghahramani Advances in Neural Information Processing Systems 18, 475--482, 2006	745*	2006
Kronecker graphs: An approach to modeling networks J Leskovec, D Chakrabarti, J Kleinberg, C Faloutsos, Z Ghahramani Journal of Machine Learning Research 11 (Feb), 985-1042, 2010	738	2010
An introduction to hidden Markov models and Bayesian networks Z Ghahramani Hidden Markov models: applications in computer vision, 9-41, 2001	734	2001
The EM algorithm for mixtures of factor analyzers Z Ghahramani, GE Hinton Technical Report CRG-TR-96-1, University of Toronto, 1996	728	1996
Learning dynamic Bayesian networks Z Ghahramani International School on Neural Networks, Initiated by IIASS and EMFCSC, 168-197, 1997	645	1997
Supervised learning from incomplete data via an EM approach Z Ghahramani, MI Jordan Advances in neural information processing systems, 120-127, 1994	635	1994
The infinite hidden Markov model MJ Beal, Z Ghahramani, CE Rasmussen Advances in neural information processing systems, 577-584, 2002	632	2002
Parameter estimation for linear dynamical systems Z Ghahramani, GE Hinton Technical Report CRG-TR-96-2, University of Totronto, Dept. of Computer Science, 1996	554	1996

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