Summary of Learning Morphisms with Gauss-newton Approximation For Growing Networks, by Neal Lawton et al.
Learning Morphisms with Gauss-Newton Approximation for Growing Networks
by Neal Lawton, Aram Galstyan, Greg Ver Steeg
First submitted to arxiv on: 7 Nov 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Computer Vision and Pattern Recognition (cs.CV); Neural and Evolutionary Computing (cs.NE)
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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 proposed Neural Architecture Search (NAS) method utilizes network morphisms to efficiently determine which parts of the network are best to grow. By applying a Gauss-Newton approximation of the loss function, the candidate network morphisms are learned and evaluated, leading to improved architectures with reduced computational costs. This approach is compared to state-of-the-art NAS methods on CIFAR-10 and CIFAR-100 classification tasks, demonstrating similar or better architecture quality at a smaller cost. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper presents a new way to grow neural networks using a technique called network morphisms. It starts with a small network and adds new parts in an automated way. The challenge is to figure out what parts of the network are most important to add. This method uses a special formula to quickly test different changes to the network and choose the best ones. The results show that this approach can find good networks as well as or better than other methods, but at a lower cost. |
Keywords
* Artificial intelligence * Classification * Loss function
