Summary of The Implicit Bias Of Adam on Separable Data, by Chenyang Zhang et al.
The Implicit Bias of Adam on Separable Data
by Chenyang Zhang, Difan Zou, Yuan Cao
First submitted to arxiv on: 15 Jun 2024
Categories
- Main: Machine Learning (stat.ML)
- Secondary: Machine Learning (cs.LG)
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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 Adam has become a popular optimizer in deep learning problems, but its theoretical understanding remains unclear despite its success in practice. This paper explores the implicit bias of Adam in linear logistic regression, showing that when training data are linearly separable, Adam converges to a linear classifier achieving the maximum _-margin. Additionally, our results demonstrate that this convergence occurs within polynomial time for a general class of diminishing learning rates. The study provides insight into the differences between Adam and gradient descent from a theoretical perspective. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Adam is an optimizer used in deep learning that works well in practice, but scientists don’t fully understand why it’s so good. This research looks at how Adam works in linear logistic regression, which helps us understand its strengths. We found that when the data are simple and easy to separate, Adam creates a linear classifier that does the best job of keeping mistakes from happening. We also showed that Adam can do this quickly using a special type of learning rate. This research helps us understand how Adam compares to other optimizers. |
Keywords
* Artificial intelligence * Deep learning * Gradient descent * Logistic regression
