Summary of Cap: a Context-aware Neural Predictor For Nas, by Han Ji et al.
CAP: A Context-Aware Neural Predictor for NAS
by Han Ji, Yuqi Feng, Yanan Sun
First submitted to arxiv on: 4 Jun 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: 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 context-aware neural predictor (CAP) is designed to boost the performance evaluation stage in neural architecture search (NAS). Unlike traditional methods that require extensive annotated architectures, CAP only needs a few examples. This is achieved by encoding input architectures into graphs and inferring contextual structures around nodes. The pre-trained predictor then obtains expressive representations of architectures through a context-aware self-supervised task. Experimental results demonstrate superior performance compared to state-of-the-art neural predictors, with the ability to rank architectures precisely at just 172 annotated architectures in NAS-Bench-101. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary In this paper, scientists develop a new way to help find the best architecture for building artificial intelligence models. They create a special kind of predictor that can learn from only a few examples and make accurate predictions about how well different architectures will perform. This is useful because it takes a lot of time and effort to test many different architectures. The researchers show that their method works better than other methods at finding the best architecture, which is important for making AI models more efficient. |
Keywords
» Artificial intelligence » Self supervised
