Summary of Revisiting the Initial Steps in Adaptive Gradient Descent Optimization, by Abulikemu Abuduweili and Changliu Liu
Revisiting the Initial Steps in Adaptive Gradient Descent Optimization
by Abulikemu Abuduweili, Changliu Liu
First submitted to arxiv on: 3 Dec 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 This paper investigates the limitations of adaptive gradient optimization methods, such as Adam, in training deep neural networks. Despite their ability to achieve faster convergence, these methods often suffer from suboptimal generalization and instability when training Transformer models. The authors identify the standard initialization of the second-order moment estimation (v_0 = 0) as a significant factor contributing to these limitations. To address this issue, they introduce simple yet effective solutions: initializing the second-order moment estimation with non-zero values using data-driven or random initialization strategies. Empirical evaluations demonstrate that their approach stabilizes convergence and enhances the final performance of adaptive gradient optimizers, achieving performance comparable to recently proposed variants. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper is about how to make a type of machine learning algorithm called Adam work better. Right now, Adam can train neural networks quickly, but it has some problems that make its results not as good as they could be. The authors figure out what’s causing these problems and come up with simple solutions to fix them. They test their ideas and show that they make Adam perform better and more reliably. This is important because many machine learning tasks use algorithms like Adam, so making it work better can help us get better results in things like language translation, image recognition, and more. |
Keywords
» Artificial intelligence » Generalization » Machine learning » Optimization » Transformer » Translation
