Generative Modeling

Generative Modeling is a machine learning paradigm that belongs to the class of statistical modeling approaches, distinguished by its objective of learning the underlying probability distribution of data in order to generate new, synthetically similar instances.

12K

Publications

1.1M

Citations

34.9K

Authors

4.3K

Institutions

Publications per year

2017–2026

10.3K

Authors

34.9K

  1. YB
    Yoshua Bengio

    Université de Montréal, Canada

    73 publications · 31.1K citations

    41

    h-index

  2. GE
    Geoffrey E. Hinton

    University of Toronto, Canada

    44 publications · 27.3K citations

    40

    h-index

  3. RS
    Ruslan Salakhutdinov

    Carnegie Mellon University, United States

    44 publications · 11.5K citations

    33

    h-index

  4. SE
    Stefano Ermon

    Stanford University, United States

    53 publications · 6.9K citations

    33

    h-index

  5. JZ
    Jun-Yan Zhu

    Carnegie Mellon University, United States

    43 publications · 34.8K citations

    32

    h-index

1–5 of 34.9K

Venues

  1. arXiv (Cornell University)

    Repository · 2.6K publications · 256.8K citations

    212

    h-index

  2. Proceedings of the AAAI Conference on Artificial Intelligence

    Conference · 236 publications · 18.3K citations

    67

    h-index

  3. IEEE Transactions on Pattern Analysis and Machine Intelligence

    TPAMI · 165 publications · 16.8K citations

    62

    h-index

  4. IEEE Access

    287 publications · 14.3K citations

    61

    h-index

  5. 49

    h-index

1–5