Summary of Where Do Large Learning Rates Lead Us?, by Ildus Sadrtdinov et al.
Where Do Large Learning Rates Lead Us?
by Ildus Sadrtdinov, Maxim Kodryan, Eduard Pokonechny, Ekaterina Lobacheva, Dmitry Vetrov
First submitted to arxiv on: 29 Oct 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Machine Learning (stat.ML)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary In this study, researchers investigate the optimal starting learning rate (LR) for training neural networks to achieve good generalization. They find that only a specific range of initial LRs, slightly above the convergence threshold, leads to optimal results after fine-tuning or weight averaging. This narrow range allows the optimization algorithm to locate a basin containing high-quality minima. The researchers also show that models started with these optimal LRs exhibit sparse and task-relevant feature learning, unlike those initialized with too small or large LRs. By analyzing the local geometry of reached minima, they demonstrate that initial LRs affect the stability and quality of solutions, influencing generalization performance. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This study explores what happens when you start training neural networks with different learning rates (LRs). The researchers discovered that only a specific range of initial LRs helps neural networks learn well. They found that models started with these optimal LRs are more stable and focused on the most important features for the task, unlike those initialized with too small or large LRs. This means that choosing the right starting LR can help improve how well your model performs. |
Keywords
» Artificial intelligence » Fine tuning » Generalization » Optimization
