Hierarchical Topic Models
Hierarchical Topic Models is a type of probabilistic topic model that extends flat topic structures by organizing topics into a hierarchy to capture broader and more fine-grained thematic relationships in text corpora.
344
Publications
26.5K
Citations
993
Authors
376
Institutions
Publications per year
2017–2026
123
Parents
344
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
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376
Princeton University
Princeton, United States
12 h-index
23 publications
5.2K citations
University of Illinois Urbana-Champaign
Urbana, United States
11 h-index
40 publications
1.5K citations

Chinese Academy of Sciences
Beijing, China
10 h-index
33 publications
2.2K citations
Massachusetts Institute of Technology
Cambridge, United States
8 h-index
20 publications
4.3K citations
Carnegie Mellon University
Pittsburgh, United States
8 h-index
15 publications
3K citations
University of California, Berkeley
Berkeley, United States
8 h-index
11 publications
4.4K citations
Tsinghua University
Beijing, China
7 h-index
19 publications
994 citations

University of Massachusetts Amherst
Amherst Center, United States
6 h-index
17 publications
2.6K citations
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Repository · 10 publications · 506 citations
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TVCG · 7 publications · 549 citations
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TPAMI · 6 publications · 924 citations
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TKDE · 6 publications · 465 citations
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Conference · 6 publications · 1.2K citations
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