Structured Variational Inference

Structured Variational Inference is a variational inference method that, unlike mean-field approximations, explicitly incorporates rich dependency structures among latent variables by employing structured variational families guided by the model's conditional independence structure.

37

Publications

3.6K

Citations

116

Authors

54

Institutions

Publications per year

2017–2026

16

Authors

116

  1. DM
    David M. Blei

    Princeton University, United States

    3 publications · 123 citations

    3

    h-index

  2. MD
    Matthew D. Hoffman

    Adobe Systems (United States), United States

    2 publications · 90 citations

    2

    h-index

  3. SG
    Soumya Ghosh

    The University of Western Australia, Australia

    2 publications · 41 citations

    2

    h-index

  4. JM
    James M. Rehg

    Georgia Institute of Technology, United States

    2 publications · 106 citations

    2

    h-index

  5. OW
    Ole Winther

    Technical University of Denmark, Denmark

    2 publications · 325 citations

    2

    h-index

1–5 of 116

Venues

  1. arXiv (Cornell University)

    Repository · 12 publications · 539 citations

    11

    h-index

  2. 2

    h-index

  3. IEEE Transactions on Medical Imaging

    1 publications · 20 citations

    1

    h-index

  4. Foundations and Trends® in Machine Learning

    1 publications · 1.8K citations

    1

    h-index

  5. Neural Information Processing Systems

    Conference · 1 publications · 21 citations

    1

    h-index

1–5