Decoupled Weight Decay for Any $p$ Norm

• URL: http://arxiv.org/abs/2404.10824v2
• Date: Mon, 22 Apr 2024 20:31:04 GMT
• Title: Decoupled Weight Decay for Any $p$ Norm
• Authors: Nadav Joseph Outmezguine, Noam Levi,
• Abstract summary: We consider a simple yet effective approach to sparsification, based on the Bridge, $L_p$ regularization during training. We introduce a novel weight decay scheme, which generalizes the standard $L$ weight decay to any $p$ norm. We empirically demonstrate that it leads to highly sparse networks, while maintaining performance comparable to standard $L$ regularization.
• Score: 1.1510009152620668
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: With the success of deep neural networks (NNs) in a variety of domains, the computational and storage requirements for training and deploying large NNs have become a bottleneck for further improvements. Sparsification has consequently emerged as a leading approach to tackle these issues. In this work, we consider a simple yet effective approach to sparsification, based on the Bridge, or $L_p$ regularization during training. We introduce a novel weight decay scheme, which generalizes the standard $L_2$ weight decay to any $p$ norm. We show that this scheme is compatible with adaptive optimizers, and avoids the gradient divergence associated with $0<p<1$ norms. We empirically demonstrate that it leads to highly sparse networks, while maintaining generalization performance comparable to standard $L_2$ regularization.

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