@inproceedings{
oh2023pruning,
title={Pruning Neural Networks with Velocity-Constrained Optimization},
author={Donghyun Oh and Jinseok Chung and Namhoon Lee},
booktitle={OPT 2023: Optimization for Machine Learning},
year={2023},
url={https://openreview.net/forum?id=A05nWKTZAL}
}
Pruning has gained prominence as a way to compress over-parameterized neural networks. While
pruning can be understood as solving a sparsity-constrained optimization problem, pruning by di-
rectly solving this problem has been relatively underexplored. In this paper, we propose a method to
prune neural networks using the MJ algorithm, which interprets constrained optimization using the
framework of velocity-constrained optimization. The experimental results show that our method
can prune VGG19 and ResNet32 networks by more than 90% while preserving the high accuracy
of the dense network.
Pruning Neural Networks with Velocity-Constrained Optimization
Donghyun Oh Jinseok Chung Namhoon Lee