Energy-based Models

Energy-Based Models is a probabilistic machine learning framework that belongs to the class of undirected graphical models, distinguished by its specification of a scalar energy function whose minima correspond to the most probable states of the system, with probability densities derived via the Boltzmann distribution.

438

Publications

35.1K

Citations

1.4K

Authors

532

Institutions

Publications per year

2017–2026

198

Authors

1.4K

  1. SZ
    Song‐Chun Zhu

    University of California, Los Angeles, United States

    16 publications · 935 citations

    13

    h-index

  2. YW
    Ying Wu

    Northwestern University, United States

    16 publications · 853 citations

    13

    h-index

  3. EN
    Erik Nijkamp

    University of California, Los Angeles, United States

    9 publications · 390 citations

    9

    h-index

  4. YB
    Yoshua Bengio

    Université de Montréal, Canada

    8 publications · 724 citations

    8

    h-index

  5. JX
    Jianwen Xie

    University of California, Los Angeles, United States

    8 publications · 442 citations

    8

    h-index

1–5 of 1.4K

Venues

  1. arXiv (Cornell University)

    Repository · 62 publications · 3.6K citations

    25

    h-index

  2. Energy and Buildings

    8 publications · 429 citations

    8

    h-index

  3. Proceedings of the AAAI Conference on Artificial Intelligence

    Conference · 5 publications · 253 citations

    5

    h-index

  4. 5

    h-index

  5. Energies

    5 publications · 171 citations

    5

    h-index

1–5