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Surrogate Lagrangian Relaxation: A Path To Retrain-free Deep Neural
  Network Pruning

Surrogate Lagrangian Relaxation: A Path To Retrain-free Deep Neural Network Pruning

8 April 2023
Shangli Zhou
Mikhail A. Bragin
Lynn Pepin
Deniz Gurevin
Fei Miao
Caiwen Ding
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Papers citing "Surrogate Lagrangian Relaxation: A Path To Retrain-free Deep Neural Network Pruning"

3 / 3 papers shown
Title
Survey on Lagrangian Relaxation for MILP: Importance, Challenges,
  Historical Review, Recent Advancements, and Opportunities
Survey on Lagrangian Relaxation for MILP: Importance, Challenges, Historical Review, Recent Advancements, and Opportunities
Mikhail A. Bragin
14
11
0
02 Jan 2023
MLP-Mixer: An all-MLP Architecture for Vision
MLP-Mixer: An all-MLP Architecture for Vision
Ilya O. Tolstikhin
N. Houlsby
Alexander Kolesnikov
Lucas Beyer
Xiaohua Zhai
...
Andreas Steiner
Daniel Keysers
Jakob Uszkoreit
Mario Lucic
Alexey Dosovitskiy
286
2,606
0
04 May 2021
Group Sparsity: The Hinge Between Filter Pruning and Decomposition for
  Network Compression
Group Sparsity: The Hinge Between Filter Pruning and Decomposition for Network Compression
Yawei Li
Shuhang Gu
Christoph Mayer
Luc Van Gool
Radu Timofte
137
189
0
19 Mar 2020
1