Summary of Design Principle Transfer in Neural Architecture Search Via Large Language Models, by Xun Zhou et al.
Design Principle Transfer in Neural Architecture Search via Large Language Models
by Xun Zhou, Xingyu Wu, Liang Feng, Zhichao Lu, Kay Chen Tan
First submitted to arxiv on: 21 Aug 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Computation and Language (cs.CL)
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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 novel transfer paradigm, design principle transfer, aims to reduce the search space in neural architecture search (NAS) by leveraging linguistic descriptions of structural components’ effects on architectural performance. This approach is designed to enhance the practical applicability of NAS in real-world scenarios. A large language model (LLM)-assisted framework, LAPT, is devised to automatically reason design principles from a set of given architectures and refine them progressively based on new search results. Experimental results show that LAPT outperforms state-of-the-art TNAS methods on most tasks. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper introduces a new way to make neural architecture search more efficient. It’s called design principle transfer. This means that we can use what we’ve learned from previous searches to help us find good architectures faster. We don’t have to search through as many possibilities, which makes it quicker and more practical. The researchers developed a special system that uses large language models to learn these principles and improve them over time. |
Keywords
» Artificial intelligence » Large language model
