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.

43

Publications

4.8K

Citations

91

Authors

48

Institutions

Publications per year

2017–2026

9

Authors

91

  1. DM
    David M. Blei

    Princeton University, United States

    12 publications · 3.5K citations

    12

    h-index

  2. JP
    John Paisley

    Columbia University, United States

    5 publications · 1.9K citations

    5

    h-index

  3. MI
    Michael I. Jordan

    University of California, Berkeley, United States

    4 publications · 1.2K citations

    4

    h-index

  4. CW
    Chong Wang

    Princeton University, United States

    4 publications · 544 citations

    4

    h-index

  5. JZ
    Jun Zhu

    Tsinghua University, China

    3 publications · 43 citations

    3

    h-index

1–5 of 91

Venues

  1. arXiv (Cornell University)

    Repository · 7 publications · 287 citations

    7

    h-index

  2. Journal of Machine Learning Research

    JMLR · 2 publications · 1.5K citations

    2

    h-index

  3. 2

    h-index

  4. Bayesian Analysis

    1 publications · 49 citations

    1

    h-index

  5. Science Advances

    1 publications · 253 citations

    1

    h-index

1–5