Summary of Adagrad Under Anisotropic Smoothness, by Yuxing Liu et al.
AdaGrad under Anisotropic Smoothness
by Yuxing Liu, Rui Pan, Tong Zhang
First submitted to arxiv on: 21 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 This paper bridges the gap between theory and practice by providing a deeper understanding of adaptive gradient methods, particularly Adagrad, in large-scale deep neural networks. The authors propose an anisotropic generalized smoothness assumption and corresponding analyses, demonstrating that Adagrad can achieve faster convergence guarantees with better dimensional dependence compared to algorithms using uniform step sizes. Theoretical results are supported by experiments in logistic regression and instruction following fine-tuning tasks. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper helps us understand how deep neural networks learn by studying a special type of learning method called adaptive gradient methods. These methods help big models like large foundation models train better than smaller ones. Researchers wanted to know why this is the case, especially when using a lot of data at once. They came up with a new idea about how smoothness works and used it to study Adagrad, another popular learning method. Their results show that Adagrad can learn faster and better than other methods, which is important for building smarter AI models. |
Keywords
» Artificial intelligence » Fine tuning » Logistic regression
