Attention Head Pruning

Attention Head Pruning is a model compression technique that selectively removes redundant or less informative attention heads from a transformer architecture to improve computational efficiency while preserving model performance.

16

Publications

2K

Citations

52

Authors

23

Institutions

Publications per year

2017–2026

16

Authors

52

  1. SP
    Sai Prasanna

    University of Massachusetts Lowell, United States

    2 publications · 151 citations

    2

    h-index

  2. AR
    Anna Rogers

    University of Massachusetts Lowell, United States

    2 publications · 151 citations

    2

    h-index

  3. ZZ
    Zhekai Zhang

    Massachusetts Institute of Technology, United States

    2 publications · 384 citations

    2

    h-index

  4. EV
    Elena Voita

    University of Amsterdam, Netherlands

    2 publications · 1.1K citations

    2

    h-index

  5. HW
    Hanrui Wang

    Massachusetts Institute of Technology, United States

    2 publications · 384 citations

    2

    h-index

1–5 of 52

Venues

  1. arXiv (Cornell University)

    Repository · 5 publications · 190 citations

    5

    h-index

  2. Journal of Energy Storage

    1 publications · 34 citations

    1

    h-index

  3. AI Open

    1 publications · 23 citations

    1

    h-index

  4. IEEE Transactions on Visualization and Computer Graphics

    TVCG · 1 publications · 41 citations

    1

    h-index

  5. Zurich Open Repository and Archive (University of Zurich)

    Repository · 1 publications · 70 citations

    1

    h-index

1–5