Collapsed Variational Inference

Collapsed Variational Inference is a variational inference method that integrates out certain latent variables from the model to improve approximation accuracy by reducing the dimensionality of the variational optimization problem.

22

Publications

932

Citations

56

Authors

27

Institutions

Publications per year

2017–2026

8

Authors

56

  1. IS
    Issei Sato

    The University of Tokyo, Japan

    4 publications · 75 citations

    4

    h-index

  2. HN
    Hiroshi Nakagawa

    The University of Tokyo, Japan

    3 publications · 53 citations

    3

    h-index

  3. MW
    Max Welling

    University of Amsterdam, Netherlands

    2 publications · 274 citations

    2

    h-index

  4. KK
    Kenichi Kurihara

    Japan Atomic Energy Agency, Japan

    2 publications · 172 citations

    2

    h-index

  5. JH
    James Hensman

    University of Sheffield, United Kingdom

    2 publications · 128 citations

    2

    h-index

1–5 of 56

Venues

  1. arXiv (Cornell University)

    Repository · 3 publications · 106 citations

    3

    h-index

  2. 3

    h-index

  3. Neurocomputing

    1 publications · 15 citations

    1

    h-index

  4. Oxford University Research Archive (ORA) (University of Oxford)

    Repository · 1 publications · 24 citations

    1

    h-index

  5. 1

    h-index

1–5