Bayesian Nonparametric Topic Models
Bayesian Nonparametric Topic Models is a probabilistic topic modeling framework that belongs to the class of Bayesian nonparametric methods by employing flexible, infinite-dimensional priors to automatically infer the number of topics from data without requiring pre-specification of a fixed topic set.
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4.8K
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91
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48
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2017–2026
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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
A network approach to topic models
Martin Gerlach, Tiago P. Peixoto, Eduardo G. Altmann · Science Advances · 2018 · 253 citations · Full text
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Princeton University
Princeton, United States
9 h-index
17 publications
4K citations
University of California, Berkeley
Berkeley, United States
7 h-index
11 publications
4.4K citations
Tsinghua University
Beijing, China
3 h-index
8 publications
116 citations
Carnegie Mellon University
Pittsburgh, United States
3 h-index
3 publications
1.6K citations
Duke University
Durham, United States
3 h-index
7 publications
243 citations
Woods Hole Oceanographic Institution
Falmouth, United States
2 h-index
4 publications
108 citations

National Yang Ming Chiao Tung University
Hsinchu, Taiwan
2 h-index
2 publications
53 citations

Australian National University
Canberra, Australia
2 h-index
4 publications
111 citations
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Repository · 7 publications · 287 citations
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JMLR · 2 publications · 1.5K citations
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TPAMI · 2 publications · 56 citations
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1 publications · 49 citations
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1 publications · 253 citations
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