Activation-based Pruning
Activation-Based Pruning is a model compression technique that belongs to the class of structured pruning methods, distinguished by its reliance on the magnitude of neuron activations to identify and remove less informative components of a neural network.
6
Publications
280
Citations
15
Authors
8
Institutions
Publications per year
2017–2026
5
Parents
6
Extracting Rules from Neural Networks by Pruning and Hidden-Unit Splitting
Rudy Setiono · Neural Computation · 1997 · 155 citations
Computing, Biology, Computer Science +13
Efficient DNN Neuron Pruning by Minimizing Layer-wise Nonlinear Reconstruction Error
Chunhui Jiang, Guiying Li, Chao Qian et al. · 2018 · 45 citations · Full text
Plug-and-Play: An Efficient Post-training Pruning Method for Large Language Models
Yingtao Zhang, Haoli Bai, Haokun Lin et al. · Preprints.org · 2024 · 17 citations · Full text
1–5 of 6
15
1
h-index
1
h-index
1
h-index
1
h-index
1
h-index
1–5 of 15
8
University of Science and Technology of China
Hefei, China
1 h-index
3 publications
135 citations

McGill University
Montreal, Canada
1 h-index
3 publications
36 citations

Southern University of Science and Technology
Shenzhen, China
1 h-index
1 publications
45 citations
National University of Singapore
Singapore, Singapore
1 h-index
1 publications
155 citations
Nanjing University
Nanjing, China
1 h-index
2 publications
82 citations

Lawrence Livermore National Laboratory
Livermore, United States
1 h-index
2 publications
24 citations
Georgia Institute of Technology
Atlanta, United States
1 h-index
2 publications
30 citations
University of Nebraska–Lincoln
Lincoln, United States
1 h-index
1 publications
15 citations
1–8 of 8
Repository · 1 publications · 12 citations
1
h-index
1 publications · 155 citations
1
h-index
1 publications · 41 citations
1
h-index
JETCAS · 1 publications · 15 citations
1
h-index
Repository · 1 publications · 17 citations
1–5