Pruning Thresholds

Pruning Thresholds is a criterion—a quantifiable value or condition that determines the extent or selectivity of parameter removal in model compression—distinguishing it from other pruning mechanisms by specifying the precise point at which weights, neurons, or connections are eliminated based on magnitude, sensitivity, or saliency metrics.

13

Publications

732

Citations

39

Authors

27

Institutions

Publications per year

2017–2026

9

Authors

39

  1. SP
    Sidak Pal Singh

    ETH Zurich, Switzerland

    2 publications · 51 citations

    2

    h-index

  2. DA
    Dan Alistarh

    Institute of Science and Technology Austria, Austria

    2 publications · 51 citations

    2

    h-index

  3. DY
    Daniel Yeung

    Hong Kong Polytechnic University, Hong Kong SAR China

    1 publications · 92 citations

    1

    h-index

  4. FL
    Fuyuan Lyu

    McGill University, Canada

    1 publications · 30 citations

    1

    h-index

  5. LS
    L. Sunil Chandran

    Indian Institute of Science Bangalore, India

    1 publications · 18 citations

    1

    h-index

1–5 of 39

Venues

  1. Neurocomputing

    2 publications · 117 citations

    2

    h-index

  2. arXiv (Cornell University)

    Repository · 2 publications · 51 citations

    2

    h-index

  3. Machine Vision and Applications

    1 publications · 10 citations

    1

    h-index

  4. Pattern Recognition Letters

    1 publications · 24 citations

    1

    h-index

  5. ArXiv.org

    Repository · 1 publications · 267 citations

    1

    h-index

1–5