Summary of Efficient Sharpness-aware Minimization For Molecular Graph Transformer Models, by Yili Wang et al.
Efficient Sharpness-Aware Minimization for Molecular Graph Transformer Models
by Yili Wang, Kaixiong Zhou, Ninghao Liu, Ying Wang, Xin Wang
First submitted to arxiv on: 19 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 The paper proposes a new algorithm, GraphSAM, which reduces the training cost of Sharpness-aware Minimization (SAM) and improves the generalization performance of graph transformer models. SAM eliminates sharp local minima from the training trajectory, but it doubles the time overhead due to additional gradient computations. GraphSAM achieves efficiency gains by approximating perturbation gradients using updating gradients from previous steps, while maintaining generalization performance through loss landscape approximation. Theoretical guarantees are provided for both approaches. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper introduces a new algorithm called GraphSAM that makes computer vision training more efficient and effective. SAM was previously used to improve the accuracy of models by avoiding sharp local minima, but it took longer because it needed to calculate extra gradients. GraphSAM solves this problem by approximating some of these gradients, making training faster without sacrificing performance. This is important for applications like image recognition and object detection. |
Keywords
» Artificial intelligence » Generalization » Object detection » Sam » Transformer
