Summary of Clipping Improves Adam-norm and Adagrad-norm When the Noise Is Heavy-tailed, by Savelii Chezhegov et al.
Clipping Improves Adam-Norm and AdaGrad-Norm when the Noise Is Heavy-Tailed
by Savelii Chezhegov, Yaroslav Klyukin, Andrei Semenov, Aleksandr Beznosikov, Alexander Gasnikov, Samuel Horváth, Martin Takáč, Eduard Gorbunov
First submitted to arxiv on: 6 Jun 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Optimization and Control (math.OC)
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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 high-probability convergence of adaptive step-size methods, such as AdaGrad and Adam, for training large language models with heavy-tailed stochastic gradients. These methods are crucial for modern deep learning, but their performance is often limited by the presence of noise. The authors prove that without gradient clipping, these methods can have poor convergence properties when dealing with heavy-tailed noise. However, they also show that clipping improves convergence and derive new bounds for AdaGrad-Norm and Adam-Norm with clipping. Empirical evaluations demonstrate the benefits of clipped versions in handling noisy data. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper studies how to make deep learning models work better with noisy data. Noise can be a problem because it makes gradients unpredictable, which is bad for training models. The authors show that some methods, like AdaGrad and Adam, don’t always do well with heavy-tailed noise (noise that is really extreme). They also prove that adding “gradient clipping” helps fix this issue. This means that the models can learn more effectively even when there’s a lot of noisy data. |
Keywords
* Artificial intelligence * Deep learning * Probability
