Likelihood-based Generative Modeling

Likelihood-Based Generative Modeling is a generative modeling paradigm that belongs to the class of probabilistic models and is distinguished by its formulation of a tractable likelihood function, through which the model explicitly learns the probability distribution of the data by maximizing the likelihood of observed samples.

93

Publications

13.5K

Citations

245

Authors

77

Institutions

Publications per year

2017–2026

63

Authors

245

  1. JC
    Jukka Corander

    University of Helsinki, Finland

    4 publications · 394 citations

    4

    h-index

  2. MU
    Michael U. Gutmann

    University of Helsinki, Finland

    4 publications · 394 citations

    4

    h-index

  3. YB
    Yoshua Bengio

    Université de Montréal, Canada

    4 publications · 71 citations

    4

    h-index

  4. WG
    Will Grathwohl

    University of Toronto, Canada

    3 publications · 475 citations

    3

    h-index

  5. EH
    Emiel Hoogeboom

    University of Amsterdam, Netherlands

    3 publications · 108 citations

    3

    h-index

1–5 of 245

Venues

  1. arXiv (Cornell University)

    Repository · 54 publications · 4.1K citations

    29

    h-index

  2. IEEE Transactions on Artificial Intelligence

    AI · 1 publications · 119 citations

    1

    h-index

  3. Physical review. D/Physical review. D.

    PRD · 1 publications · 22 citations

    1

    h-index

  4. Zenodo (CERN European Organization for Nuclear Research)

    Repository · 1 publications · 12 citations

    1

    h-index

  5. Annals of Telecommunications

    1 publications · 76 citations

    1

    h-index

1–5