Pruning Masks

Pruning Masks is a methodological construct in machine learning that specifies a binary or sparse pattern of weight retention and removal within a neural network, enabling structured or unstructured pruning by identifying which parameters to preserve during model compression.

13

Publications

393

Citations

52

Authors

25

Institutions

Publications per year

2017–2026

12

Authors

52

  1. WS
    Wonyong Sung

    Seoul National University, South Korea

    2 publications · 52 citations

    2

    h-index

  2. SA
    Sajid Anwar

    Institute of Management Sciences Peshawar, Pakistan

    2 publications · 52 citations

    2

    h-index

  3. XS
    Xinglong Sun

    The University of Texas at Austin, United States

    1 publications · 22 citations

    1

    h-index

  4. SS
    Saku Sugawara

    National Institute of Informatics, Japan

    1 publications · 15 citations

    1

    h-index

  5. TC
    Tianlong Chen

    The University of Texas at Austin, United States

    1 publications · 29 citations

    1

    h-index

1–5 of 52

Venues

  1. arXiv (Cornell University)

    Repository · 2 publications · 66 citations

    2

    h-index

  2. 1

    h-index

  3. Proceedings of the AAAI Conference on Artificial Intelligence

    Conference · 1 publications · 36 citations

    1

    h-index

  4. 1

    h-index

  5. Medical Physics

    1 publications · 14 citations

    1

    h-index

1–5