Pruning-stabilization Trade-off

Pruning-Stabilization Trade-Off is a theoretical framework in neural network optimization that characterizes the inherent conflict between the extent of parameter removal during model pruning and the consequent degradation in training dynamics or post-pruning model performance stability.

18

Publications

770

Citations

52

Authors

35

Institutions

Publications per year

2017–2026

12

Authors

52

  1. JF
    Jonathan Frankle

    Massachusetts Institute of Technology, United States

    2 publications · 246 citations

    2

    h-index

  2. MC
    Michael Carbin

    Massachusetts Institute of Technology, United States

    2 publications · 246 citations

    2

    h-index

  3. GK
    Gintare Karolina Dziugaite

    University of Toronto, Canada

    2 publications · 246 citations

    2

    h-index

  4. DM
    Daniel M. Roy

    University of Toronto, Canada

    2 publications · 246 citations

    2

    h-index

  5. AB
    Adrian Barbu

    Florida State University, United States

    1 publications · 31 citations

    1

    h-index

1–5 of 52

Venues

  1. Applied Soft Computing

    3 publications · 54 citations

    3

    h-index

  2. arXiv (Cornell University)

    Repository · 3 publications · 277 citations

    3

    h-index

  3. Neural Networks

    3 publications · 45 citations

    3

    h-index

  4. Neurocomputing

    2 publications · 50 citations

    2

    h-index

  5. ACM Transactions on Software Engineering and Methodology

    TOSEM · 1 publications · 198 citations

    1

    h-index

1–5