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
Parents
476
1–5 of 476
1.8K
Institut national de recherche en sciences et technologies du numérique, France
9 publications · 1.4K citations
9
h-index
8
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6
h-index
Institut national de recherche en sciences et technologies du numérique, France
5 publications · 361 citations
5
h-index
5
h-index
1–5 of 1.8K
571
Tsinghua University
Beijing, China
14 h-index
47 publications
2.1K citations
Carnegie Mellon University
Pittsburgh, United States
12 h-index
32 publications
3.1K citations
Institut national de recherche en sciences et technologies du numérique
Le Chesnay-Rocquencourt, France
12 h-index
24 publications
3.4K citations
Massachusetts Institute of Technology
Cambridge, United States
11 h-index
22 publications
1.3K citations

Chinese Academy of Sciences
Beijing, China
10 h-index
24 publications
2.5K citations
Stanford University
Stanford, United States
10 h-index
32 publications
10K citations
University of Michigan
Ann Arbor, United States
10 h-index
17 publications
947 citations
University of Illinois Urbana-Champaign
Urbana, United States
9 h-index
17 publications
2K citations
1–8 of 571
Repository · 63 publications · 3.9K citations
29
h-index
9 publications · 264 citations
9
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7 publications · 181 citations
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7 publications · 1.8K citations
7
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7 publications · 1.2K citations
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