Yee Whye Teh
In today's article, we are going to delve into the fascinating world of Yee Whye Teh. From its origins to its relevance today, we will immerse ourselves in a journey that will allow us to understand the importance of Yee Whye Teh in different areas. We will explore its impacts on society, its influence on popular culture and its relevance in history. Through detailed analysis, we will discover how Yee Whye Teh has shaped our world and continues to be a topic of interest and debate today. Get ready for a complete immersion in the universe of Yee Whye Teh and all its facets.
Yee-Whye Teh is a professor of statistical machine learning in the Department of Statistics, University of Oxford.[4][5] Prior to 2012 he was a reader at the Gatsby Charitable Foundation computational neuroscience unit at University College London.[6] His work is primarily in machine learning, artificial intelligence, statistics and computer science.[1][7]
Education
Teh was educated at the University of Waterloo and the University of Toronto where he was awarded a PhD in 2003 for research supervised by Geoffrey Hinton.[3][8]
Research and career
Teh was a postdoctoral fellow at the University of California, Berkeley and the National University of Singapore before he joined University College London as a lecturer.[2]
Teh was one of the original developers of deep belief networks[9] and of hierarchical Dirichlet processes.[10]
He has been a research scientist at Google DeepMind.[11]
Awards and honours
Teh was a keynote speaker at Uncertainty in Artificial Intelligence (UAI) 2019, and was invited to give the Breiman lecture at the Conference on Neural Information Processing Systems (NeurIPS) 2017.[12] He served as program co-chair of the International Conference on Machine Learning (ICML) in 2017, one of the premier conferences in machine learning.[4]
References
- ^ a b Yee Whye Teh publications indexed by Google Scholar
- ^ a b "Yee-Whye Teh, Professor of Statistical Machine Learning". stats.ox.ac.uk.
- ^ a b Yee Whye Teh at the Mathematics Genealogy Project
- ^ a b www
.stats .ox .ac .uk /~teh / 
- ^ Gram-Hansen, Bradley (2021). Extending probabilistic programming systems and applying them to real-world simulators. ox.ac.uk (DPhil thesis). University of Oxford. OCLC 1263818188. EThOS uk.bl.ethos.833365.
- ^ Gasthaus, Jan Alexander (2020). Hierarchical Bayesian nonparametric models for power-law sequences. ucl.ac.uk (PhD thesis). University College London. OCLC 1197757196. EThOS uk.bl.ethos.807804.
- ^ Yee Whye Teh at DBLP Bibliography Server
- ^ Whye Teh, Yee (2003). Bethe free energy and contrastive divergence approximations for undirected graphical models. utoronto.ca (PhD thesis). University of Toronto. hdl:1807/122253. OCLC 56683361. ProQuest 305242430.
- ^ Geoffrey E. Hinton; Simon Osindero; Yee-Whye Teh (1 July 2006). "A fast learning algorithm for deep belief nets". Neural Computation. 18 (7): 1527–1554. doi:10.1162/NECO.2006.18.7.1527. ISSN 0899-7667. PMID 16764513. Zbl 1106.68094. Wikidata Q33996665.
- ^ Yee W. Teh; Michael I. Jordan; Matthew J. Beal; David M. Blei (2005). "Sharing Clusters among Related Groups: Hierarchical Dirichlet Processes" (PDF). Advances in Neural Information Processing Systems 17. Advances in Neural Information Processing Systems. Wikidata Q77688418.
- ^ Sample, Ian (2017-11-02). "Big tech firms' AI hiring frenzy leads to brain drain at UK universities". The Guardian. ISSN 0261-3077.
- ^ "On Bayesian Deep Learning and Deep Bayesian Learning". nips.cc.
