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.

66

Publications

7.9K

Citations

175

Authors

79

Institutions

Publications per year

2017–2026

16

Authors

175

  1. DM
    David M. Blei

    Princeton University, United States

    9 publications · 6K citations

    9

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  2. AO
    Alice Oh

    Korea Advanced Institute of Science and Technology, South Korea

    5 publications · 250 citations

    5

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  3. JZ
    Jun Zhu

    Tsinghua University, China

    5 publications · 64 citations

    5

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  4. CC
    Changyou Chen

    Duke University, United States

    4 publications · 118 citations

    4

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  5. MZ
    Mingyuan Zhou

    The University of Texas at Austin, United States

    3 publications · 83 citations

    3

    h-index

1–5 of 175

Venues

  1. arXiv (Cornell University)

    Repository · 9 publications · 377 citations

    9

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  2. 2

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  3. Information Sciences

    2 publications · 76 citations

    2

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  4. Neural Information Processing Systems

    Conference · 2 publications · 948 citations

    2

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  5. Neurocomputing

    2 publications · 27 citations

    2

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1–5