Early Pruning

Early Pruning is a model optimization technique in machine learning wherein redundant or less significant parameters are removed during the initial stages of training, distinguishing it from later pruning strategies by its timing and its emphasis on reducing computational burden from the outset.

48

Publications

2.4K

Citations

173

Authors

97

Institutions

Publications per year

2017–2026

14

Authors

173

  1. UY
    Unil Yun

    Sejong University, South Korea

    2 publications · 81 citations

    2

    h-index

  2. QC
    Qi Chen

    Chinese Academy of Sciences, China

    2 publications · 291 citations

    2

    h-index

  3. HB
    Huong Bui

    Vietnam National University Ho Chi Minh City, Vietnam

    2 publications · 47 citations

    2

    h-index

  4. BV
    Bay Vo

    Ho Chi Minh City University of Technology, Vietnam

    1 publications · 20 citations

    1

    h-index

  5. CM
    C. Magnanini

    University of Parma, Italy

    1 publications · 35 citations

    1

    h-index

1–5 of 173

Venues

  1. HAL (Le Centre pour la Communication Scientifique Directe)

    Repository · 2 publications · 105 citations

    2

    h-index

  2. Information Sciences

    1 publications · 61 citations

    1

    h-index

  3. 1

    h-index

  4. PLoS Biology

    1 publications · 267 citations

    1

    h-index

  5. Knowledge-Based Systems

    1 publications · 27 citations

    1

    h-index

1–5