Summary of Stepping on the Edge: Curvature Aware Learning Rate Tuners, by Vincent Roulet et al.
Stepping on the Edge: Curvature Aware Learning Rate Tuners
by Vincent Roulet, Atish Agarwala, Jean-Bastien Grill, Grzegorz Swirszcz, Mathieu Blondel, Fabian Pedregosa
First submitted to arxiv on: 8 Jul 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: None
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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 The paper investigates the relationship between learning rate tuning and curvature information in deep learning models. It finds that classical learning rate tuners can provide better one-step loss reduction but ultimately underperform when compared to constant learning rates in the long term. The authors introduce a new learning rate tuning method, Curvature Dynamics Aware Tuning (CDAT), which prioritizes long-term curvature stabilization over instantaneous progress on the objective. CDAT outperforms tuned constant learning rates in the full batch regime and is comparable in performance in the mini batch regime. The paper highlights the importance of understanding the joint dynamics of the learning rate and curvature to diagnose failures and design effective adaptive learning rate tuners. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper looks at how we adjust the speed of learning in deep learning models. It finds that traditional ways of adjusting the learning rate can be good for short-term progress but don’t do well in the long term. The authors suggest a new way to adjust the learning rate, called Curvature Dynamics Aware Tuning (CDAT), which prioritizes long-term stability over short-term gains. CDAT performs well in certain situations and helps us understand why some methods work better than others. |
Keywords
* Artificial intelligence * Deep learning
