Activation-based Pruning

Activation-Based Pruning is a model compression technique that belongs to the class of structured pruning methods, distinguished by its reliance on the magnitude of neuron activations to identify and remove less informative components of a neural network.

6

Publications

280

Citations

15

Authors

8

Institutions

Publications per year

2017–2026

5

Authors

15

  1. AA
    Arash Ardakani

    McGill University, Canada

    1 publications · 12 citations

    1

    h-index

  2. BK
    Bhavya Kailkhura

    Lawrence Livermore National Laboratory, United States

    1 publications · 12 citations

    1

    h-index

  3. HY
    Hao Yu

    Hangzhou Dianzi University, China

    1 publications · 41 citations

    1

    h-index

  4. CC
    Carlo Condo

    McGill University, Canada

    1 publications · 12 citations

    1

    h-index

  5. MW
    Marilyn Wolf

    Georgia Institute of Technology, United States

    1 publications · 15 citations

    1

    h-index

1–5 of 15

Venues

  1. arXiv (Cornell University)

    Repository · 1 publications · 12 citations

    1

    h-index

  2. Neural Computation

    1 publications · 155 citations

    1

    h-index

  3. Science China Information Sciences

    1 publications · 41 citations

    1

    h-index

  4. 1

    h-index

  5. Preprints.org

    Repository · 1 publications · 17 citations

1–5