Pruning Masks
Pruning Masks is a methodological construct in machine learning that specifies a binary or sparse pattern of weight retention and removal within a neural network, enabling structured or unstructured pruning by identifying which parameters to preserve during model compression.
13
Publications
393
Citations
52
Authors
25
Institutions
Publications per year
2017–2026
12
Parents
13
X-Pruner: eXplainable Pruning for Vision Transformers
Structured Pruning of Convolutional Neural Networks via L1 Regularization
Chen Yang, Zhenghong Yang, Abdul Mateen Khattak et al. · IEEE Access · 2019 · 37 citations · Full text
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25
Tsinghua University
Beijing, China
2 h-index
4 publications
97 citations

La Trobe University
Melbourne, Australia
1 h-index
1 publications
62 citations
The University of Tokyo
Tokyo, Japan
1 h-index
2 publications
30 citations

Chinese Academy of Sciences
Beijing, China
1 h-index
2 publications
72 citations
Peng Cheng Laboratory
Shenzhen, China
1 h-index
3 publications
192 citations
Universidad del Noreste
Tampico, Mexico
1 h-index
2 publications
50 citations
Dalian University of Technology
Dalian, China
1 h-index
1 publications
21 citations
Shahid Beheshti University
Tehran, Iran
1 h-index
2 publications
28 citations
1–8 of 25
Repository · 2 publications · 66 citations
2
h-index
1 publications · 64 citations
1
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Conference · 1 publications · 36 citations
1
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Conference · 1 publications · 22 citations
1
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1 publications · 14 citations
1
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