Gradient Compression

Gradient Compression is a method in scalable machine learning that reduces the communication overhead during model updates by selectively retaining or quantizing the most informative components of gradient vectors while discarding less significant information.

151

Publications

9K

Citations

490

Authors

160

Institutions

Publications per year

2017–2026

130

Authors

490

  1. PR
    Peter Richtárik

    University of Edinburgh, United Kingdom

    10 publications · 378 citations

    10

    h-index

  2. SU
    Sebastian U. Stich

    École Polytechnique Fédérale de Lausanne, Switzerland

    8 publications · 401 citations

    8

    h-index

  3. SP
    Sai Praneeth Karimireddy

    École Polytechnique Fédérale de Lausanne, Switzerland

    5 publications · 425 citations

    5

    h-index

  4. MC
    Marco Canini

    King Abdullah University of Science and Technology, Saudi Arabia

    5 publications · 144 citations

    5

    h-index

  5. AM
    Ahmed M. Abdelmoniem

    Queen Mary University of London, United Kingdom

    5 publications · 144 citations

    5

    h-index

1–5 of 490

Venues

  1. arXiv (Cornell University)

    Repository · 55 publications · 3.5K citations

    29

    h-index

  2. Proceedings of the AAAI Conference on Artificial Intelligence

    Conference · 6 publications · 865 citations

    6

    h-index

  3. IEEE Transactions on Communications

    4 publications · 118 citations

    4

    h-index

  4. PubMed

    Repository · 3 publications · 76 citations

    3

    h-index

  5. 3

    h-index

1–5