Summary of Mada: Meta-adaptive Optimizers Through Hyper-gradient Descent, by Kaan Ozkara et al.
MADA: Meta-Adaptive Optimizers through hyper-gradient Descent
by Kaan Ozkara, Can Karakus, Parameswaran Raman, Mingyi Hong, Shoham Sabach, Branislav Kveton, Volkan Cevher
First submitted to arxiv on: 17 Jan 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 Meta-Adaptive Optimizers (MADA) is a unified optimizer framework that generalizes several known optimizers and dynamically learns the most suitable one during training. By parameterizing the space of optimizers and searching through it using hyper-gradient descent, MADA consistently outperforms Adam and other popular optimizers on vision and language tasks. This meta-optimizer achieves a greater validation performance improvement over Adam compared to other popular optimizers during GPT-2 training and fine-tuning. Additionally, AVGrad, a modification of AMSGrad that replaces the maximum operator with averaging, is proposed for hyper-gradient optimization. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary MADA is a new way to help computers learn from data. It’s like having a super smart computer that can figure out which method is best to use in any situation. This helps MADA do better than other methods on certain tasks. The creators of MADA also came up with a new version called AVGrad, which makes it even better at finding the right approach. |
Keywords
* Artificial intelligence * Fine tuning * Gpt * Gradient descent * Optimization
