Summary of Building Optimal Neural Architectures Using Interpretable Knowledge, by Keith G. Mills et al.
Building Optimal Neural Architectures using Interpretable Knowledge
by Keith G. Mills, Fred X. Han, Mohammad Salameh, Shengyao Lu, Chunhua Zhou, Jiao He, Fengyu Sun, Di Niu
First submitted to arxiv on: 20 Mar 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Artificial Intelligence (cs.AI); 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 The proposed AutoBuild scheme learns to align the latent embeddings of operations and architecture modules with their ground-truth performance, allowing for the assignment of interpretable importance scores. This enables the construction of high-performance neural networks without search, outperforming both labeled ones and search baselines on image classification, segmentation, and Stable Diffusion models. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary AutoBuild is a new way to build good neural networks. It does this by learning how different parts of the network work together. The network then uses this knowledge to find the right combination of parts to use. This means that you don’t need to try lots of different combinations to see what works best, which saves time and computing power. |
Keywords
* Artificial intelligence * Image classification
