Nonparametric Topic Models
Nonparametric Topic Models is a statistical framework for topic modeling that belongs to the class of Bayesian nonparametric methods, distinguished by its ability to infer the number of topics from data without pre-specifying a fixed number, thereby allowing the topic structure to grow flexibly with increasing document collections.
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7.9K
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175
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79
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Publications per year
2017–2026
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David M. Blei, John Lafferty · 2006 · 2.4K citations
Stochastic variational inference
Matthew D. Hoffman, David M. Blei, Chong Wang et al. · Journal of Machine Learning Research · 2013 · 1.5K citations
Sharing Clusters among Related Groups: Hierarchical Dirichlet Processes
Yee Whye Teh, Michael I. Jordan, Matthew J. Beal et al. · 2004 · 434 citations
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Korea Advanced Institute of Science and Technology, South Korea
5 publications · 250 citations
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Princeton University
Princeton, United States
7 h-index
11 publications
5.5K citations
Tsinghua University
Beijing, China
6 h-index
13 publications
179 citations
Korea Advanced Institute of Science and Technology
Daejeon, South Korea
5 h-index
14 publications
669 citations
Carnegie Mellon University
Pittsburgh, United States
5 h-index
11 publications
4.3K citations

Australian National University
Canberra, Australia
4 h-index
4 publications
123 citations
Duke University
Durham, United States
4 h-index
8 publications
254 citations
University of California, Berkeley
Berkeley, United States
4 h-index
7 publications
4.7K citations
Brown University
Providence, United States
3 h-index
7 publications
284 citations
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Repository · 9 publications · 377 citations
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TPAMI · 2 publications · 51 citations
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2 publications · 76 citations
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Conference · 2 publications · 948 citations
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2 publications · 27 citations
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