Yee Whye Teh

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Max WellingProfessor Machine Learning, University of AmsterdamVerified email at uva.nl
Geoffrey HintonEmeritus Prof. Comp Sci, U.Toronto & Engineering Fellow, GoogleVerified email at cs.toronto.edu
Simon OsinderoSenior Staff Scientist @ DeepMindVerified email at google.com
Michael I. JordanProfessor of Electrical Engineering and Computer Sciences and Professor of Statistics, UC BerkeleyVerified email at cs.berkeley.edu
Nicolas HeessDeepMindVerified email at google.com
Razvan PascanuResearch Scientist at Google DeepMindVerified email at google.com
Balaji LakshminarayananStaff Research Scientist at Google BrainVerified email at google.com
Frank WoodUniversity of British ColumbiaVerified email at cs.ubc.ca
Chris J. MaddisonUniversity of TorontoVerified email at cs.toronto.edu
Tom RainforthUniversity of OxfordVerified email at stats.ox.ac.uk
Andriy MnihResearch Scientist at DeepMindVerified email at cs.toronto.edu
Stefano FavaroUniversity of Torino and Collegio Carlo AlbertoVerified email at unito.it
Matthew J. BealChicago Trading CompanyVerified email at chicagotrading.com
Jonathan SchwarzDeepMind, Gatsby Computational Neuroscience Unit - UCLVerified email at google.com
Daniel M. RoyUniversity of Toronto, Dept of Statistical Sciences; Computer ScienceVerified email at utoronto.ca
Dilan GorurDeepMindVerified email at google.com
Arnaud DoucetProfessor of Statistics, University of Oxford & Research Scientist, Google DeepMindVerified email at stats.ox.ac.uk
David BleiProfessor of Statistics and Computer Science, Columbia UniversityVerified email at columbia.edu
Zoubin GhahramaniProfessor, University of Cambridge, and Distinguished Researcher, GoogleVerified email at eng.cam.ac.uk
Adam R. KosiorekResearch Scientist, DeepMindVerified email at google.com

Yee Whye Teh

Professor of Statistical Machine Learning, Oxford, Research Scientist, DeepMind

Verified email at stats.ox.ac.uk - Homepage

Machine Learning Artificial Intelligence Statistics Computer Science


Title Sort by citations Sort by year Sort by title	Cited by Cited by	Year
A fast learning algorithm for deep belief nets GE Hinton, S Osindero, YW Teh Neural computation 18 (7), 1527-1554, 2006	16027	2006
Hierarchical Dirichlet Processes YW Teh, MI Jordan, MJ Beal, DM Blei Journal of the American Statistical Association 101, 1566-1581, 2006	4487	2006
Bayesian Learning via Stochastic Gradient Langevin Dynamics M Welling, YW Teh ICML, 2011	1428	2011
The concrete distribution: A continuous relaxation of discrete random variables CJ Maddison, A Mnih, YW Teh ICLR, 2017	1302	2017
A collapsed variational Bayesian inference algorithm for latent Dirichlet allocation YW Teh, D Newman, M Welling CALIFORNIA UNIV IRVINE SCHOOL OF INFORMATION AND COMPUTER SCIENCE, 2007	720	2007
On smoothing and inference for topic models A Asuncion, M Welling, P Smyth, YW Teh arXiv preprint arXiv:1205.2662, 2009	719	2009
A hierarchical Bayesian language model based on Pitman-Yor processes YW Teh Proceedings of the 21st International Conference on Computational …, 2006	638	2006
A fast and simple algorithm for training neural probabilistic language models A Mnih, YW Teh arXiv preprint arXiv:1206.6426, 2012	570	2012
Dirichlet Process. YW Teh Encyclopedia of machine learning, 280-287, 2010	559	2010
Names and faces in the news TL Berg, AC Berg, J Edwards, M Maire, R White, YW Teh, ... Proceedings of the 2004 IEEE Computer Society Conference on Computer Vision …, 2004	514	2004
Hierarchical Bayesian nonparametric models with applications YW Teh, MI Jordan Bayesian nonparametrics 1, 158-207, 2010	396	2010
Sharing Clusters among Related Groups: Hierarchical Dirichlet Processes. YW Teh, MI Jordan, MJ Beal, DM Blei NIPS, 2005	336	2005
Beam sampling for the infinite hidden Markov model J Van Gael, Y Saatci, YW Teh, Z Ghahramani Proceedings of the 25th international conference on Machine learning, 1088-1095, 2008	318	2008
Stick-breaking construction for the Indian buffet process YW Teh, D Grür, Z Ghahramani Artificial Intelligence and Statistics, 556-563, 2007	311	2007
Progress & Compress: A scalable framework for continual learning J Schwarz, J Luketina, WM Czarnecki, A Grabska-Barwinska, YW Teh, ... ICML2018 arXiv:1805.06370, 2018	292	2018
Distral: Robust multitask reinforcement learning YW Teh, V Bapst, WM Czarnecki, J Quan, J Kirkpatrick, R Hadsell, ... NIPS2017, 2017	290	2017
Automatic alignment of local representations YW Teh, ST Roweis Advances in neural information processing systems, 865-872, 2003	281	2003
Do Deep Generative Models Know What They Don't Know? E Nalisnick, A Matsukawa, YW Teh, D Gorur, B Lakshminarayanan ICLR2019, 2018	272	2018
Semiparametric latent factor models YW Teh, M Seeger, MI Jordan International Workshop on Artificial Intelligence and Statistics, 333-340, 2005	263	2005
Conditional Neural Processes M Garnelo, D Rosenbaum, CJ Maddison, T Ramalho, D Saxton, ... ICML2018 arXiv:1807.01613, 2018	254	2018

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