Summary of Grasp-gcn: Graph-shape Prioritization For Neural Architecture Search Under Distribution Shifts, by Sofia Casarin et al.
GRASP-GCN: Graph-Shape Prioritization for Neural Architecture Search under Distribution Shifts
by Sofia Casarin, Oswald Lanz, Sergio Escalera
First submitted to arxiv on: 11 May 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- 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 Neural Architecture Search (NAS) methods have successfully designed networks that outperform human-designed ones. However, conventional NAS methods are limited to single-dataset scenarios, resulting in high computational costs when dealing with new datasets. This work focuses on predictor-based algorithms and proposes an efficient way to improve prediction performance under data distribution shifts. The authors introduce a small NAS benchmark comprising networks trained on four different datasets and develop GRASP-GCN, a ranking Graph Convolutional Network that incorporates layer shapes as additional input. Trained using not-at-convergence accuracies, GRASP-GCN improves the state-of-the-art by 3.3% for Cifar-10, enhancing generalization abilities under data distribution shifts. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This research paper is about finding better ways to design computer networks that can work well with different types of data. Usually, designing these networks takes a lot of time and computation power. The authors found a shortcut by using special algorithms that can predict how good a network will be without having to train it from scratch every time. They created a small test dataset with four different kinds of data and developed a new method called GRASP-GCN that works better than before. This improvement means the networks can handle changes in the type of data more effectively. |
Keywords
» Artificial intelligence » Convolutional network » Gcn » Generalization
