Sparse Topic Models

Sparse Topic Models is a type of probabilistic topic model that constrains the distribution of topics over words or documents to be sparse, thereby enhancing interpretability by emphasizing a few dominant topics or words while suppressing negligible associations.

147

Publications

10.3K

Citations

419

Authors

177

Institutions

Publications per year

2017–2026

62

Authors

419

  1. DM
    David M. Blei

    Princeton University, United States

    6 publications · 640 citations

    6

    h-index

  2. KV
    Konstantin Vorontsov

    Moscow Institute of Physics and Technology, Russia

    5 publications · 232 citations

    5

    h-index

  3. XC
    Xueqi Cheng

    Chinese Academy of Sciences, China

    5 publications · 1.9K citations

    5

    h-index

  4. JG
    Jiafeng Guo

    Chinese Academy of Sciences, China

    5 publications · 1.9K citations

    5

    h-index

  5. EP
    Eric P. Xing

    Carnegie Mellon University, United States

    5 publications · 583 citations

    5

    h-index

1–5 of 419

Venues

  1. arXiv (Cornell University)

    Repository · 11 publications · 907 citations

    11

    h-index

  2. Proceedings of the AAAI Conference on Artificial Intelligence

    Conference · 5 publications · 221 citations

    5

    h-index

  3. IEEE Transactions on Knowledge and Data Engineering

    TKDE · 5 publications · 951 citations

    5

    h-index

  4. 4

    h-index

  5. 2

    h-index

1–5