Summary of Fadam: Adam Is a Natural Gradient Optimizer Using Diagonal Empirical Fisher Information, by Dongseong Hwang
FAdam: Adam is a natural gradient optimizer using diagonal empirical Fisher information
by Dongseong Hwang
First submitted to arxiv on: 21 May 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI); Information Theory (cs.IT)
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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 mathematical foundation for the Adam optimizer is established by elucidating its connection to natural gradient descent through Riemannian and information geometry. The paper provides a detailed analysis of the diagonal empirical Fisher information matrix (FIM) in Adam, clarifying all detailed approximations and advocating for the use of log probability functions as loss based on discrete distributions due to the limitations of empirical FIM. Flaws in the original Adam algorithm are uncovered, leading to proposed corrections such as enhanced momentum calculations, adjusted bias corrections, adaptive epsilon, and gradient clipping. The weight decay term is refined based on the theoretical framework. A modified algorithm, Fisher Adam (FAdam), demonstrates superior performance across diverse domains including LLM, ASR, and VQ-VAE, achieving state-of-the-art results in ASR. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper helps us understand how a popular AI learning tool called Adam works. It shows that Adam is connected to other ways of training AI models. The researchers also found problems with the original Adam algorithm and suggested fixes. They even created a new version of Adam that performs better than the old one in many areas, such as language processing and speech recognition. |
Keywords
» Artificial intelligence » Gradient descent » Probability
