Zoubin Ghahramani
Zoubin Ghahramani
Professor, University of Cambridge, and Chief Scientist, Uber
Verified email at eng.cam.ac.uk - Homepage
TitleCited byYear
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
An introduction to variational methods for graphical models
MI Jordan, Z Ghahramani, TS Jaakkola, LK Saul
Machine learning 37 (2), 183-233, 1999
An internal model for sensorimotor integration
DM Wolpert, Z Ghahramani, MI Jordan
Science 269 (5232), 1880-1882, 1995
Computational principles of movement neuroscience
DM Wolpert, Z Ghahramani
Nature Neuroscience 3, 1212-1217, 2000
Active learning with statistical models
DA Cohn, Z Ghahramani, MI Jordan
Journal of Artificial Intelligence Research 4, 129--145, 1996
Factorial hidden Markov models
Z Ghahramani, MI Jordan
Advances in Neural Information Processing Systems, 472-478, 1996
Learning from labeled and unlabeled data with label propagation
X Zhu, Z Ghahramani
Technical Report CMU-CALD-02-107, Carnegie Mellon University, 2002
Sparse Gaussian processes using pseudo-inputs
E Snelson, Z Ghahramani
Advances in Neural Information Processing Systems 18, 1257--1264, 2006
A unifying review of linear Gaussian models
S Roweis, Z Ghahramani
Neural computation 11 (2), 305-345, 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
Dropout as a bayesian approximation: Representing model uncertainty in deep learning
Y Gal, Z Ghahramani
international conference on machine learning, 1050-1059, 2016
Perspectives and problems in motor learning
DM Wolpert, Z Ghahramani, JR Flanagan
Trends in cognitive sciences 5 (11), 487-494, 2001
Infinite latent feature models and the Indian buffet process
T Griffiths, Z Ghahramani
Advances in Neural Information Processing Systems 18, 475--482, 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
An introduction to hidden Markov models and Bayesian networks
Z Ghahramani
Hidden Markov models: applications in computer vision, 9-41, 2001
The EM algorithm for mixtures of factor analyzers
Z Ghahramani, GE Hinton
Technical Report CRG-TR-96-1, University of Toronto, 1996
Learning dynamic Bayesian networks
Z Ghahramani
International School on Neural Networks, Initiated by IIASS and EMFCSC, 168-197, 1997
Supervised learning from incomplete data via an EM approach
Z Ghahramani, MI Jordan
Advances in neural information processing systems, 120-127, 1994
The infinite hidden Markov model
MJ Beal, Z Ghahramani, CE Rasmussen
Advances in neural information processing systems, 577-584, 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
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Articles 1–20