Sparsity Induction

Sparsity Induction is a model regularization technique that belongs to the genus of structural optimization methods in machine learning, distinguished by its deliberate enforcement of reduced parameter connectivity or feature activation to enhance interpretability, efficiency, and generalization in neural network pruning.

476

Publications

38.4K

Citations

1.8K

Authors

571

Institutions

Publications per year

2017–2026

231

Authors

1.8K

  1. FB
    Francis Bach

    Institut national de recherche en sciences et technologies du numérique, France

    9 publications · 1.4K citations

    9

    h-index

  2. EP
    Eric P. Xing

    Carnegie Mellon University, United States

    8 publications · 928 citations

    8

    h-index

  3. SY
    Se Young Kim

    Catholic University of Korea, South Korea

    6 publications · 896 citations

    6

    h-index

  4. GO
    Guillaume Obozinski

    Institut national de recherche en sciences et technologies du numérique, France

    5 publications · 361 citations

    5

    h-index

  5. JY
    Jieping Ye

    Arizona State University, United States

    5 publications · 530 citations

    5

    h-index

1–5 of 1.8K

Venues

  1. arXiv (Cornell University)

    Repository · 63 publications · 3.9K citations

    29

    h-index

  2. Signal Processing

    9 publications · 264 citations

    9

    h-index

  3. IEEE Access

    7 publications · 181 citations

    7

    h-index

  4. IEEE Transactions on Image Processing

    7 publications · 1.8K citations

    7

    h-index

  5. Journal of the American Statistical Association

    7 publications · 1.2K citations

    7

    h-index

1–5