Summary of Why Warmup the Learning Rate? Underlying Mechanisms and Improvements, by Dayal Singh Kalra and Maissam Barkeshli
Why Warmup the Learning Rate? Underlying Mechanisms and Improvements
by Dayal Singh Kalra, Maissam Barkeshli
First submitted to arxiv on: 13 Jun 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Disordered Systems and Neural Networks (cond-mat.dis-nn); 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 A deep learning paper explores the effectiveness of warming up the learning rate (η) in stochastic gradient descent (SGD) and Adam optimization algorithms. The study finds that the primary benefit of warm-up arises from allowing networks to operate in more well-conditioned areas of the loss landscape, enabling larger target η values. This makes hyperparameter tuning more robust and improves final performance. The paper identifies different regimes during the warmup period, depending on initialization and parameterization. It also proposes an initialization for Adam’s variance that provides benefits similar to warm-up. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary In a deep learning experiment, researchers find that warming up the learning rate helps networks work better. They tested two types of optimization algorithms: SGD and Adam. The results show that allowing networks to adjust quickly at first helps them learn more effectively in the end. This means that hyperparameter tuning becomes easier and the final performance improves. The study also finds different patterns during the warm-up process, depending on how the network is started. |
Keywords
» Artificial intelligence » Deep learning » Hyperparameter » Optimization » Stochastic gradient descent
