Summary of Adaptive Stochastic Weight Averaging, by Caglar Demir et al.
Adaptive Stochastic Weight Averaging
by Caglar Demir, Arnab Sharma, Axel-Cyrille Ngonga Ngomo
First submitted to arxiv on: 27 Jun 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 propose Adaptive Stochastic Weight Averaging (ASWA), a technique that updates the running average of model parameters only when generalization performance improves on the validation dataset. This approach combines SWA with early stopping, allowing for more efficient training while maintaining robustness to overfitting. The authors demonstrate ASWA’s effectiveness across 11 benchmark datasets and 7 baseline models, showing statistically better generalization compared to traditional techniques. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary ASWA is a new way to make machine learning models work better together. It’s like averaging the predictions of many models, but only updates the average when it gets better on a test set. This helps avoid problems like overfitting and makes training faster. The authors tested ASWA on lots of datasets and showed that it works well across different types of tasks. |
Keywords
» Artificial intelligence » Early stopping » Generalization » Machine learning » Overfitting
