Late Pruning
Late Pruning is a pruning strategy in machine learning model optimization that belongs to the category of structured sparsity methods, distinguished by its execution phase occurring after the completion of the training process, thereby removing redundant parameters based on post-training saliency analysis.
13
Publications
305
Citations
43
Authors
20
Institutions
Publications per year
2017–2026
9
Variants
Post-training Pruning
Parents
13
Matteo Gatti, F.J. Pirez, Tommaso Frioni et al. · Australian Journal of Grape and Wine Research · 2018 · 32 citations · Full text
1–5 of 13
43
South Australian Research and Development Institute, Australia
3 publications · 100 citations
3
h-index
South Australian Research and Development Institute, Australia
3 publications · 100 citations
3
h-index
South Australian Research and Development Institute, Australia
3 publications · 100 citations
3
h-index
2
h-index
2
h-index
1–5 of 43
20

The University of Adelaide
Adelaide, Australia
3 h-index
7 publications
226 citations
Australian Wine Research Institute
Adelaide, Australia
3 h-index
8 publications
270 citations
South Australian Research and Development Institute
Adelaide, Australia
3 h-index
9 publications
300 citations

Ariel University
Ariel, Israel
2 h-index
4 publications
54 citations
Ben-Gurion University of the Negev
Beersheba, Israel
2 h-index
3 publications
41 citations
Carmel Medical Center
Haifa, Israel
2 h-index
5 publications
66 citations
Hebrew University of Jerusalem
Jerusalem, Israel
1 h-index
3 publications
39 citations
Jiangxi University of Finance and Economics
Nanchang, China
1 h-index
4 publications
80 citations
1–8 of 20
6 publications · 176 citations
6
h-index
1 publications · 18 citations
1
h-index
1 publications · 14 citations
1
h-index
AJEV · 1 publications · 28 citations
1
h-index
1 publications · 18 citations
1
h-index
1–5