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
David A. KnowlesStanford UniversityVerified email at stanford.edu
Yarin GalAssociate Professor, University of OxfordVerified email at cs.ox.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
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
Jurgen Van GaelFacebookVerified email at fb.com
James Robert LloydPhD from Machine Learning Group, Cambridge UniversityVerified email at cam.ac.uk
Ryan P. AdamsPrinceton UniversityVerified email at princeton.edu

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	3201	2003
An introduction to variational methods for graphical models MI Jordan, Z Ghahramani, TS Jaakkola, LK Saul Machine learning 37 (2), 183-233, 1999	3094	1999
An internal model for sensorimotor integration DM Wolpert, Z Ghahramani, MI Jordan Science 269 (5232), 1880-1882, 1995	2802	1995
Computational principles of movement neuroscience DM Wolpert, Z Ghahramani Nature Neuroscience 3, 1212-1217, 2000	1663	2000
Active learning with statistical models DA Cohn, Z Ghahramani, MI Jordan Journal of Artificial Intelligence Research 4, 129--145, 1996	1472	1996
Factorial hidden Markov models Z Ghahramani, MI Jordan Advances in Neural Information Processing Systems, 472-478, 1996	1440	1996
Learning from labeled and unlabeled data with label propagation X Zhu, Z Ghahramani Technical Report CMU-CALD-02-107, Carnegie Mellon University, 2002	1017	2002
A unifying review of linear Gaussian models S Roweis, Z Ghahramani Neural computation 11 (2), 305-345, 1999	1005	1999
Sparse Gaussian processes using pseudo-inputs E Snelson, Z Ghahramani Advances in Neural Information Processing Systems 18, 1257--1264, 2006	1003	2006
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	870	2004
Perspectives and problems in motor learning DM Wolpert, Z Ghahramani, JR Flanagan Trends in cognitive sciences 5 (11), 487-494, 2001	804	2001
Dropout as a bayesian approximation: Representing model uncertainty in deep learning Y Gal, Z Ghahramani international conference on machine learning, 1050-1059, 2016	744	2016
Infinite latent feature models and the Indian buffet process T Griffiths, Z Ghahramani Advances in Neural Information Processing Systems 18, 475--482, 2006	740*	2006
The EM algorithm for mixtures of factor analyzers Z Ghahramani, GE Hinton Technical Report CRG-TR-96-1, University of Toronto, 1996	727	1996
An introduction to hidden Markov models and Bayesian networks Z Ghahramani Hidden Markov models: applications in computer vision, 9-41, 2001	724	2001
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	723	2010
Learning dynamic Bayesian networks Z Ghahramani International School on Neural Networks, Initiated by IIASS and EMFCSC, 168-197, 1997	639	1997
Supervised learning from incomplete data via an EM approach Z Ghahramani, MI Jordan Advances in neural information processing systems, 120-127, 1994	630	1994
The infinite hidden Markov model MJ Beal, Z Ghahramani, CE Rasmussen Advances in neural information processing systems, 577-584, 2002	627	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	550	1996

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