Zoubin Ghahramani

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Michael I. JordanProfessor of Electrical Engineering and Computer Sciences and Professor of Statistics, UC BerkeleyVerificeret mail på cs.berkeley.edu
Yarin GalAssociate Professor, University of OxfordVerificeret mail på cs.ox.ac.uk
Daniel WolpertProfessor of Neuroscience, Columbia UniversityVerificeret mail på columbia.edu
Xiaojin ZhuProfessor in Computer Science at University of Wisconsin-MadisonVerificeret mail på cs.wisc.edu
Geoffrey HintonEmeritus Prof. Computer Science, University of TorontoVerificeret mail på cs.toronto.edu
Carl Edward RasmussenProfessor of Machine Learning, University of CambridgeVerificeret mail på cam.ac.uk
José Miguel Hernández-LobatoProfessor of Machine Learning, University of CambridgeVerificeret mail på cam.ac.uk
Matthew J. BealChicago Trading CompanyVerificeret mail på chicagotrading.com
Katherine HellerGoogle ResearchVerificeret mail på gatsby.ucl.ac.uk
David A. KnowlesColumbia University & New York Genome CenterVerificeret mail på columbia.edu
Alexander G. D. G. MatthewsDeepMindVerificeret mail på google.com
Tommi JaakkolaMITVerificeret mail på csail.mit.edu
Sam Roweis (Memorial)New York UniversityVerificeret mail på buffalo.edu
Wei Chu（褚崴）InfVerificeret mail på gatsby.ucl.ac.uk
Richard E TurnerProfessor, University of CambridgeVerificeret mail på cam.ac.uk
Neil HoulsbyGoogleVerificeret mail på google.com
Ryan P. AdamsPrinceton UniversityVerificeret mail på princeton.edu
James Robert LloydPhD from Machine Learning Group, Cambridge UniversityVerificeret mail på cam.ac.uk
Amar ShahPhD student, University of CambridgeVerificeret mail på cam.ac.uk
Naonori Ueda理化学研究所/NTTコミュニケーション科学基礎研究所 /Verificeret mail på riken.jp

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Zoubin Ghahramani

Professor, University of Cambridge, and Distinguished Researcher, Google

Verificeret mail på eng.cam.ac.uk - Startside

Machine Learning Bayesian Statistics Neural Networks Artificial Intelligence


Titel Sortér efter henvisninger Sortér efter årstal Sortér efter titel	Citeret af Citeret af	År
Dropout as a bayesian approximation: Representing model uncertainty in deep learning Y Gal, Z Ghahramani international conference on machine learning, 1050-1059, 2016	11630	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	5841*	2003
An introduction to variational methods for graphical models MI Jordan, Z Ghahramani, TS Jaakkola, LK Saul Machine learning 37, 183-233, 1999	5591	1999
An internal model for sensorimotor integration DM Wolpert, Z Ghahramani, MI Jordan Science 269 (5232), 1880-1882, 1995	4348	1995
Active learning with statistical models DA Cohn, Z Ghahramani, MI Jordan Journal of Artificial Intelligence Research 4, 129--145, 1996	2608	1996
Computational principles of movement neuroscience DM Wolpert, Z Ghahramani Nature Neuroscience 3, 1212-1217, 2000	2523	2000
Probabilistic machine learning and artificial intelligence Z Ghahramani Nature 521 (7553), 452-459, 2015	2426	2015
Sparse Gaussian processes using pseudo-inputs E Snelson, Z Ghahramani Advances in Neural Information Processing Systems 18, 1257--1264, 2006	2352	2006
Learning from labeled and unlabeled data with label propagation X ZhuЃ, Z GhahramaniЃн ProQuest number: information to all users, 2002	2123	2002
A theoretically grounded application of dropout in recurrent neural networks Y Gal, Z Ghahramani Advances in neural information processing systems 29, 2016	2104	2016
Deep bayesian active learning with image data Y Gal, R Islam, Z Ghahramani International conference on machine learning, 1183-1192, 2017	2100	2017
Factorial hidden Markov models Z Ghahramani, M Jordan Advances in neural information processing systems 8, 1995	1911	1995
A unifying review of linear Gaussian models S Roweis, Z Ghahramani Neural computation 11 (2), 305-345, 1999	1350	1999
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	1336	2010
An introduction to hidden Markov models and Bayesian networks Z Ghahramani International journal of pattern recognition and artificial intelligence 15 …, 2001	1306	2001
Unsupervised learning Z Ghahramani Summer school on machine learning, 72-112, 2003	1275	2003
Perspectives and problems in motor learning DM Wolpert, Z Ghahramani, JR Flanagan Trends in cognitive sciences 5 (11), 487-494, 2001	1104	2001
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	1084	2004
The EM algorithm for mixtures of factor analyzers Z Ghahramani, GE Hinton Technical Report CRG-TR-96-1, University of Toronto, 1996	1016	1996
Bayesian convolutional neural networks with Bernoulli approximate variational inference Y Gal, Z Ghahramani arXiv preprint arXiv:1506.02158, 2015	995	2015

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