Reduction Cuts

Reduction Cuts is a pruning technique in machine learning that belongs to the class of structured sparsity methods, distinguished by its strategy of iteratively removing redundant or less significant network components based on predefined structural criteria to achieve model compression and improved computational efficiency.

12

Publications

353

Citations

41

Authors

23

Institutions

Publications per year

2017–2026

2

Authors

41

  1. VB
    Valentina Bosetti

    Bocconi University, Italy

    1 publications · 28 citations

    1

    h-index

  2. EF
    Edward F. Gilman

    University of Florida, United States

    1 publications · 11 citations

    1

    h-index

  3. GY
    Gavin Yamey

    Duke University, United States

    1 publications · 15 citations

    1

    h-index

  4. KH
    Karen Harker

    University of North Texas, United States

    1 publications · 18 citations

    1

    h-index

  5. AM
    Adi Magna Patriadi Nuhriawangsa

    Sebelas Maret University, Indonesia

    1 publications · 12 citations

    1

    h-index

1–5 of 41

Venues

  1. 1

    h-index

  2. The Serials Librarian

    SL · 1 publications · 18 citations

    1

    h-index

  3. BMJ

    1 publications · 15 citations

    1

    h-index

  4. PubMed

    Repository · 1 publications · 14 citations

    1

    h-index

  5. Review of Environmental Economics and Policy

    1 publications · 28 citations

    1

    h-index

1–5