Machine Learning For Art

Machine Learning for Art is a scholarly contribution in artificial intelligence that integrates machine learning techniques with artistic processes or outputs, distinguished by its focus on generating, analyzing, or enhancing art through computational models trained on aesthetic or creative data.

195

Publications

15.6K

Citations

563

Authors

267

Institutions

Publications per year

2017–2026

146

Authors

563

  1. AE
    Ahmed Elgammal

    Rutgers, The State University of New Jersey, United States

    10 publications · 1.2K citations

    10

    h-index

  2. MM
    Marian Mazzone

    College of Charleston, United States

    7 publications · 940 citations

    7

    h-index

  3. BL
    Bingchen Liu

    Rutgers Sexual and Reproductive Health and Rights, Netherlands

    5 publications · 470 citations

    5

    h-index

  4. ME
    Mohamed Elhoseiny

    King Abdullah University of Science and Technology, Saudi Arabia

    4 publications · 457 citations

    4

    h-index

  5. YN
    Yuta Nakashima

    The University of Osaka, Japan

    4 publications · 155 citations

    4

    h-index

1–5 of 563

Venues

  1. arXiv (Cornell University)

    Repository · 21 publications · 819 citations

    16

    h-index

  2. Arts

    5 publications · 552 citations

    5

    h-index

  3. Neural Computing and Applications

    4 publications · 161 citations

    4

    h-index

  4. Neurocomputing

    3 publications · 92 citations

    3

    h-index

  5. IEEE Access

    3 publications · 169 citations

    3

    h-index

1–5