Variational Em Algorithm

Variational EM Algorithm is a variational inference method that iteratively optimizes a lower bound on the marginal likelihood by alternating between updating the variational distribution over latent variables (E-step) and updating the model parameters (M-step) using a mean-field approximation.

169

Publications

12.3K

Citations

404

Authors

190

Institutions

Publications per year

2017–2026

38

Authors

404

  1. AB
    Antoni B. Chan

    City University of Hong Kong, Hong Kong SAR China

    6 publications · 156 citations

    6

    h-index

  2. HA
    Hagai Attias

    University of California, San Francisco, United States

    4 publications · 628 citations

    4

    h-index

  3. EP
    Eric P. Xing

    Carnegie Mellon University, United States

    4 publications · 170 citations

    4

    h-index

  4. YB
    Yoshua Bengio

    Université de Montréal, Canada

    4 publications · 514 citations

    4

    h-index

  5. JL
    Jörg Lücke

    Frankfurt Institute for Advanced Studies, Germany

    3 publications · 87 citations

    3

    h-index

1–5 of 404

Venues

  1. arXiv (Cornell University)

    Repository · 21 publications · 957 citations

    15

    h-index

  2. 6

    h-index

  3. The Annals of Applied Statistics

    AOAS · 4 publications · 149 citations

    4

    h-index

  4. Pattern Recognition

    4 publications · 118 citations

    4

    h-index

  5. Neural Networks

    3 publications · 400 citations

    3

    h-index

1–5