Variational Autoencoder Imputation

Variational Autoencoder Imputation is a missing data imputation method that leverages a probabilistic autoencoding framework to model the data distribution and generate plausible values for missing entries through variational inference.

26

Publications

1.5K

Citations

103

Authors

56

Institutions

Publications per year

2017–2026

25

Variants

Vae Imputation

Authors

103

  1. PH
    Pedro Henriques Abreu

    University of Coimbra, Portugal

    2 publications · 130 citations

    2

    h-index

  2. JS
    Javier Serrano

    Universitat Autònoma de Barcelona, Spain

    2 publications · 213 citations

    2

    h-index

  3. RC
    Ricardo Cardoso Pereira

    Universidade do Porto, Portugal

    2 publications · 130 citations

    2

    h-index

  4. AM
    Antoni Morell

    Universitat Autònoma de Barcelona, Spain

    2 publications · 213 citations

    2

    h-index

  5. JL
    José López Vicario

    Universitat Autònoma de Barcelona, Spain

    2 publications · 213 citations

    2

    h-index

1–5 of 103

Venues

  1. Nature Communications

    2 publications · 139 citations

    2

    h-index

  2. 1

    h-index

  3. 1

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  4. ACM Transactions on Graphics

    TOG · 1 publications · 69 citations

    1

    h-index

  5. arXiv (Cornell University)

    Repository · 1 publications · 67 citations

    1

    h-index

1–5