Representational Capacity

Representational Capacity is the inherent ability of a machine learning model to approximate a range of functions, distinguished by the complexity and diversity of patterns it can capture within its parametric structure.

296

Publications

21.5K

Citations

780

Authors

332

Institutions

Publications per year

2017–2026

138

Authors

780

  1. XD
    Xiaohan Ding

    Tsinghua University, China

    6 publications · 700 citations

    6

    h-index

  2. LS
    Ling Shao

    Inception Institute of Artificial Intelligence, United Arab Emirates

    5 publications · 248 citations

    5

    h-index

  3. JH
    Jungong Han

    Lancaster University, United Kingdom

    4 publications · 672 citations

    4

    h-index

  4. YB
    Yoshua Bengio

    Université de Montréal, Canada

    4 publications · 591 citations

    4

    h-index

  5. GD
    Guiguang Ding

    Tsinghua University, China

    4 publications · 672 citations

    4

    h-index

1–5 of 780

Venues

  1. arXiv (Cornell University)

    Repository · 37 publications · 1.8K citations

    21

    h-index

  2. 9

    h-index

  3. Child Development

    8 publications · 524 citations

    8

    h-index

  4. IEEE Access

    5 publications · 231 citations

    5

    h-index

  5. Proceedings of the AAAI Conference on Artificial Intelligence

    Conference · 5 publications · 539 citations

    5

    h-index

1–5