Model Merge

Model Merge is a methodological contribution in machine learning research that integrates multiple pre-trained models into a unified architecture by systematically combining their parameters, representations, or outputs to enhance performance, generalization, or efficiency.

40

Publications

2.1K

Citations

133

Authors

66

Institutions

Publications per year

2017–2026

17

Authors

133

  1. MC
    Marsha Chećhik

    University of Toronto, Canada

    3 publications · 243 citations

    3

    h-index

  2. CR
    Colin Raffel

    Google (United States), United States

    2 publications · 43 citations

    2

    h-index

  3. MS
    Mehrdad Sabetzadeh

    University of Luxembourg, Luxembourg

    2 publications · 171 citations

    2

    h-index

  4. SE
    Steve Easterbrook

    University of Toronto, Canada

    2 publications · 171 citations

    2

    h-index

  5. SM
    Stephen M. Omohundro

    International Computer Science Institute, United States

    2 publications · 157 citations

    2

    h-index

1–5 of 133

Venues

  1. arXiv (Cornell University)

    Repository · 4 publications · 146 citations

    4

    h-index

  2. 1

    h-index

  3. Journal of Systems Integration

    1 publications · 11 citations

    1

    h-index

  4. Software & Systems Modeling

    SoSyM · 1 publications · 38 citations

    1

    h-index

  5. The Journal of Open Source Software

    JOSS · 1 publications · 64 citations

    1

    h-index

1–5