Multimodal Variational Inference

Multimodal Variational Inference is a variational inference framework that extends standard variational methods by modeling complex, multimodal posterior distributions through flexible approximation families capable of capturing dependencies across multiple distinct data modalities.

38

Publications

2.2K

Citations

164

Authors

80

Institutions

Publications per year

2017–2026

34

Authors

164

  1. SO
    Solange Oliveira Rezende

    Universidade de São Paulo, Brazil

    2 publications · 43 citations

    2

    h-index

  2. RG
    Rafael Geraldeli Rossi

    Universidade de São Paulo, Brazil

    2 publications · 43 citations

    2

    h-index

  3. RM
    Ricardo Marcondes Marcacini

    Universidade de São Paulo, Brazil

    2 publications · 43 citations

    2

    h-index

  4. AC
    Aaron Courville

    Université de Montréal, Canada

    2 publications · 35 citations

    2

    h-index

  5. TN
    Tal Nawy

    Memorial Sloan Kettering Cancer Center, United States

    2 publications · 73 citations

    2

    h-index

1–5 of 164

Venues

  1. arXiv (Cornell University)

    Repository · 6 publications · 101 citations

    6

    h-index

  2. Neural Information Processing Systems

    Conference · 2 publications · 41 citations

    2

    h-index

  3. Proceedings of the AAAI Conference on Artificial Intelligence

    Conference · 2 publications · 60 citations

    2

    h-index

  4. Briefings in Bioinformatics

    1 publications · 23 citations

    1

    h-index

  5. International Conference on Learning Representations

    Conference · 1 publications · 51 citations

    1

    h-index

1–5