Yee Whye Teh Google Scholar: h-index, Total Citations, and Citation Map
Yee Whye Teh's h-index is 93 (245 i10-index, 76,549+ total citations across 415+ publications) according to Google Scholar as of October 2026. Yee Whye Teh is affiliated with Oxford, Research Director, Google DeepMind.
Yee Whye Teh is a researcher affiliated with Oxford, Research Director, Google DeepMind, specializing in various fields. Their work has been cited 76,549 times. This profile visualizes their global influence, highlighting strong citation networks in United States.
Yee Whye Teh's Citation Metrics
Bibliometric impact based on 415 indexed publications.
- H-Index
- 93
- i10-Index
- 245
- Total Citations
- 76,549
- Citing Countries
- 67
As of October 2026.
Yee Whye Teh has an h-index of 93 and 76,549 total citations across 415 publications, with research cited by institutions in 67 countries.
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A fast learning algorithm for deep belief nets
200624,376
Top Citing Countries
Top Citing Institutions
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Significant Contributions
Auto-detected research lines — a seminal paper and the follow-up work building on it. Review and edit before using in a petition. Each Free PDF opens in a new tab — EB-1A organises this into the structure USCIS applies to Criterion 5 of 8 CFR § 204.5(h)(3)(v); EB-1B re-frames it under § 204.5(i)(3) (outstanding researcher); NIW presents it under prong 2 of Matter of Dhanasar.
12 citing papers could not be classified (no author data) — excluded from the percentages above.
The researcher advanced set-based deep learning architectures through the seminal Set Transformer and extended capsule network methodologies, establishing foundational models widely adopted by the independent research community.
The researcher developed a fast learning algorithm for deep belief nets, establishing a foundational method for training deep neural networks that has been widely adopted across the field.
The researcher introduced hierarchical Dirichlet processes to enable sharing of cluster structures among related groups, establishing a foundational framework for Bayesian nonparametric modeling.
Citation trend (last 10 years)Click to expand
Citation Trend (Last 10 Years)
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About Yee Whye Teh's research
Yee Whye Teh is a researcher at Oxford, Research Director, Google DeepMind. Their work has been cited 76,549 times across 415 publications (h-index 93), according to Google Scholar.
Their most-cited work, “A fast learning algorithm for deep belief nets” (2006), has accumulated 24,376 citations. Other influential works include “Sharing Clusters among Related Groups: Hierarchical Dirichlet Processes.” (2005) with 5,816 citations and “Bayesian Learning via Stochastic Gradient Langevin Dynamics” (2011) with 4,038 citations.
Citations of Yee Whye Teh's research come primarily from United States, China and United Kingdom, reflecting international research impact across 5+ countries. The interactive citation map above shows the full geographic distribution of the institutions citing this work.
