Computer Science > Machine Learning
arXiv:2311.11303 (cs)
[Submitted on 19 Nov 2023]
Title:Large Learning Rates Improve Generalization: But How Large Are We Talking About?
View a PDF of the paper titled Large Learning Rates Improve Generalization: But How Large Are We Talking About?, by Ekaterina Lobacheva and 3 other authors
View PDFAbstract:Inspired by recent research that recommends starting neural networks training with large learning rates (LRs) to achieve the best generalization, we explore this hypothesis in detail. Our study clarifies the initial LR ranges that provide optimal results for subsequent training with a small LR or weight averaging. We find that these ranges are in fact significantly narrower than generally assumed. We conduct our main experiments in a simplified setup that allows precise control of the learning rate hyperparameter and validate our key findings in a more practical setting.
| Comments: | Published in Mathematics of Modern Machine Learning Workshop at NeurIPS 2023. First two authors contributed equally |
| Subjects: | Machine Learning (cs.LG); Machine Learning (stat.ML) |
| Cite as: | arXiv:2311.11303 [cs.LG] |
| (orarXiv:2311.11303v1 [cs.LG] for this version) | |
| https://doi.org/10.48550/arXiv.2311.11303 arXiv-issued DOI via DataCite |
Submission history
From: Ekaterina Lobacheva Ms [view email][v1] Sun, 19 Nov 2023 11:36:35 UTC (2,794 KB)
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View a PDF of the paper titled Large Learning Rates Improve Generalization: But How Large Are We Talking About?, by Ekaterina Lobacheva and 3 other authors
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