Summary of Reweighting Local Mimina with Tilted Sam, by Tian Li et al.
Reweighting Local Mimina with Tilted SAM
by Tian Li, Tianyi Zhou, Jeffrey A. Bilmes
First submitted to arxiv on: 30 Oct 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: None
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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 In this paper, researchers introduce Tilted Sharpness-Aware Minimization (TSAM), an improved version of the existing Sharpness-Aware Minimization (SAM) method. SAM has been shown to enhance generalization performance by seeking flat minima on the loss landscape. However, the original formulation is computationally challenging and may not be optimal when searching for flat minima. TSAM addresses these issues by incorporating exponential tilting, which assigns higher priority to local solutions that are flatter and incur larger losses. The proposed method reduces to SAM as a specific hyperparameter approaches infinity. The authors demonstrate that TSAM has a smoother objective than SAM, making it easier to optimize, and empirically show that it achieves better test performance across various image and text tasks. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper introduces a new way to improve machine learning models by optimizing their parameters. It’s called Tilted Sharpness-Aware Minimization (TSAM). Right now, there are ways to make models better, but they can be hard to use and might not always work well. TSAM tries to fix these problems by making the model look at local solutions that are flatter and have a bigger loss. This helps the model find the best solution more easily. The paper shows that TSAM is better than other ways of optimizing models, and it can be used for different kinds of data. |
Keywords
* Artificial intelligence * Generalization * Hyperparameter * Machine learning * Sam
