Decoupled Weight Decay for Any $p$ Norm
<|reference_start|>Decoupled Weight Decay for Any $p$ Norm: 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.<|reference_end|>
@article{outmezguine2024decoupled,
    title={Decoupled Weight Decay for Any $p$ Norm},
    author={Nadav Joseph Outmezguine, Noam Levi},
    journal={arXiv preprint arXiv:2404.10824},
    year={2024},
    archivePrefix={arXiv},
    eprint={2404.10824},
    primaryClass={cs.LG cs.AI cs.NE math.OC}
}