Energy-based Generative Models

Energy-Based Generative Models is a class of generative models that defines the probability distribution of data through an energy function, distinguishing itself by modeling unnormalized probability densities whose values are derived from the negative exponential of energies assigned to configurations.

81

Publications

7.5K

Citations

199

Authors

78

Institutions

Publications per year

2017–2026

66

Authors

199

  1. YW
    Ying Wu

    Northwestern University, United States

    14 publications · 843 citations

    14

    h-index

  2. SZ
    Song‐Chun Zhu

    University of California, Los Angeles, United States

    13 publications · 782 citations

    13

    h-index

  3. JX
    Jianwen Xie

    University of California, Los Angeles, United States

    9 publications · 518 citations

    9

    h-index

  4. EN
    Erik Nijkamp

    University of California, Los Angeles, United States

    6 publications · 304 citations

    6

    h-index

  5. YL
    Yang Lu

    University of California, Los Angeles, United States

    6 publications · 450 citations

    6

    h-index

1–5 of 199

Venues

  1. arXiv (Cornell University)

    Repository · 41 publications · 3.1K citations

    23

    h-index

  2. 4

    h-index

  3. Proceedings of the AAAI Conference on Artificial Intelligence

    Conference · 3 publications · 119 citations

    3

    h-index

  4. Entropy

    3 publications · 135 citations

    3

    h-index

  5. Neural Information Processing Systems

    Conference · 2 publications · 106 citations

    2

    h-index

1–5