Manual Pruning
Manual Pruning is a pruning technique in which human experts explicitly inspect and remove nodes, branches, features, or other structural elements from a system, model, or dataset on the basis of domain knowledge or qualitative judgment rather than automated or algorithmic criteria.
7
Publications
141
Citations
26
Authors
9
Institutions
Publications per year
2017–2026
4
7
Deep Learning Based Method for Pruning Deep Neural Networks
Lianqiang Li, Jie Zhu, Ming–Ting Sun · 2019 · 19 citations
1–5 of 7
26
1
h-index
1
h-index
1
h-index
Institute of Animal Science and Pastures, Brazil
1 publications · 15 citations
1
h-index
1
h-index
1–5 of 26
9

Xidian University
Xi'an, China
1 h-index
5 publications
150 citations

Shanghai Jiao Tong University
Shanghai, China
1 h-index
2 publications
38 citations
Universitat Politècnica de València
Valencia, Spain
1 h-index
1 publications
20 citations

Universidade Federal de Pernambuco
Recife, Brazil
1 h-index
2 publications
56 citations
Swinburne University of Technology
Melbourne, Australia
1 h-index
1 publications
30 citations

Universidad Politécnica de Cartagena
Cartagena, Spain
1 h-index
2 publications
40 citations
Instytut Ogrodnictwa – Państwowy Instytut Badawczy
Skierniewice, Poland
1 h-index
3 publications
54 citations
University of Washington
Seattle, United States
1 h-index
1 publications
19 citations
1–8 of 9
1 publications · 15 citations
1
h-index
Repository · 1 publications · 18 citations
1
h-index
1 publications · 28 citations
1
h-index
1 publications · 20 citations
1
h-index
1 publications · 30 citations
1
h-index
1–5