Neural Topic Models
Neural Topic Models is a class of probabilistic topic modeling frameworks that leverages deep neural networks to learn latent thematic structures from text corpora, distinguishing itself from classical topic models by replacing explicit generative assumptions with flexible neural architectures for inference.
350
Publications
17.6K
Citations
1.2K
Authors
388
Institutions
Publications per year
2017–2026
262
Parents
350
Continuous Time Dynamic Topic Models
Chong Wang, David M. Blei, David Heckerman · arXiv (Cornell University) · 2012 · 325 citations · Full text
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388
Wuhan University
Wuhan, China
15 h-index
45 publications
2.8K citations

Chinese Academy of Sciences
Beijing, China
14 h-index
33 publications
1.3K citations
Tsinghua University
Beijing, China
10 h-index
23 publications
641 citations

Harbin Institute of Technology
Harbin, China
9 h-index
24 publications
1.1K citations

University of Warwick
Coventry, United Kingdom
8 h-index
19 publications
1.2K citations
Chinese University of Hong Kong
Hong Kong, Hong Kong
8 h-index
22 publications
1.8K citations

Sun Yat-sen University
Guangzhou, China
8 h-index
25 publications
557 citations
Baidu (China)
Beijing, China
7 h-index
18 publications
492 citations
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Repository · 31 publications · 2.8K citations
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Conference · 12 publications · 562 citations
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11 publications · 461 citations
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10 publications · 325 citations
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9 publications · 223 citations
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