Hierarchical Dirichlet Processes
Hierarchical Dirichlet Processes is a Bayesian nonparametric model that extends the Dirichlet process by organizing multiple Dirichlet processes in a hierarchical structure, enabling shared latent clusters across related groups through a global measure that serves as the base measure for group-specific Dirichlet processes.
239
Publications
20.9K
Citations
560
Authors
257
Institutions
Publications per year
2017–2026
63
239
Hierarchical Dirichlet Processes
Yee Whye Teh, Michael I. Jordan, Matthew J. Beal et al. · Journal of the American Statistical Association · 2006 · 3.6K citations
Stochastic variational inference
Matthew D. Hoffman, David M. Blei, Chong Wang et al. · Journal of Machine Learning Research · 2013 · 1.5K citations
Hanna Wallach · 2006 · 1.1K citations
1–5 of 239
560
17
h-index
12
h-index
8
h-index
7
h-index
7
h-index
1–5 of 560
257
University of California, Berkeley
Berkeley, United States
19 h-index
31 publications
5.4K citations
Princeton University
Princeton, United States
13 h-index
24 publications
4K citations
Duke University
Durham, United States
11 h-index
20 publications
1.4K citations
Massachusetts Institute of Technology
Cambridge, United States
11 h-index
27 publications
4.6K citations
University of Cambridge
Cambridge, United Kingdom
7 h-index
15 publications
2.1K citations
Carnegie Mellon University
Pittsburgh, United States
7 h-index
8 publications
1.7K citations
Tsinghua University
Beijing, China
7 h-index
18 publications
620 citations
Ritsumeikan University
Kyoto, Japan
6 h-index
13 publications
365 citations
1–8 of 257
Repository · 12 publications · 828 citations
11
h-index
TPAMI · 8 publications · 1.4K citations
8
h-index
7 publications · 5.4K citations
7
h-index
5 publications · 366 citations
5
h-index
4 publications · 393 citations
4
h-index
1–5