Summary of Soap: Improving and Stabilizing Shampoo Using Adam, by Nikhil Vyas et al.
SOAP: Improving and Stabilizing Shampoo using Adam
by Nikhil Vyas, Depen Morwani, Rosie Zhao, Mujin Kwun, Itai Shapira, David Brandfonbrener, Lucas Janson, Sham Kakade
First submitted to arxiv on: 17 Sep 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI)
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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 explores the advantages and limitations of Shampoo, a higher-order preconditioning method for deep learning optimization tasks. While Shampoo outperforms Adam, it has additional hyperparameters and computational overhead. The study reveals a formal connection between Shampoo and Adafactor, showing that Shampoo is equivalent to running Adafactor in the eigenbasis of its preconditioner. This insight leads to the design of a simpler and more efficient algorithm: SOAP (ShampoO with Adam in the Preconditioner’s eigenbasis). |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper looks at a new way to help computers learn, called Shampoo. It compares Shampoo to another popular method called Adam and shows that Shampoo can be even better. However, Shampoo needs more settings and uses up more computer power than Adam does. The researchers found out why Shampoo is so good: it’s like running another method called Adafactor, but in a special way. This helps them make an even simpler and faster version of Shampoo that works just as well. |
Keywords
» Artificial intelligence » Deep learning » Optimization
