Summary of Radin: Souping on a Budget, by Thibaut Menes and Olivier Risser-maroix
RADIN: Souping on a Budget
by Thibaut Menes, Olivier Risser-Maroix
First submitted to arxiv on: 31 Jan 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Computer Vision and Pattern Recognition (cs.CV)
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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 a new method to speed up the process of combining models fine-tuned with different hyperparameters, known as model soups. The approach uses averaged ensemble logits performances to approximate the performance of these soups, addressing computational challenges caused by subset selection issues. Theoretical insights validate the effectiveness of this method across various mixing ratios. The proposed Resource ADjusted soups craftINg (RADIN) procedure allows for flexible evaluation budgets, enabling users to adjust their budget of exploration adapted to their resources while achieving better performance at lower budgets compared to previous greedy approaches. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Model soups combine models fine-tuned with different hyperparameters to improve performance. However, this approach is slowed down by computational challenges due to subset selection issues. To solve this problem, researchers developed a new method that uses averaged ensemble logits performances to approximate the performance of model soups. This makes it faster and more efficient. The researchers also created a new procedure called RADIN, which allows users to adjust their budget for exploration based on their resources, leading to better results at lower costs. |
Keywords
* Artificial intelligence * Logits
