Metadata only
Date
2023Type
- Conference Paper
ETH Bibliography
yes
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Abstract
Injecting noise within gradient descent has several desirable features, such as smoothing and regularizing properties. In this paper, we investigate the effects of injecting noise before computing a gradient step. We demonstrate that small perturbations can induce explicit regularization for simple models based on the L1-norm, group L1-norms, or nuclear norms. However, when applied to overparametrized neural networks with large widths, we show that the same perturbations can cause variance explosion. To overcome this, we propose using independent layer-wise perturbations, which provably allow for explicit regularization without variance explosion. Our empirical results show that these small perturbations lead to improved generalization performance compared to vanilla gradient descent. Show more
Publication status
publishedExternal links
Book title
Proceedings of The 26th International Conference on Artificial Intelligence and StatisticsJournal / series
Proceedings of Machine Learning ResearchVolume
Pages / Article No.
Publisher
PMLREvent
Organisational unit
09462 - Hofmann, Thomas / Hofmann, Thomas
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ETH Bibliography
yes
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