English(EN) Structuring Open-Ended NAS: Semi-Automated Design Knowledge Structuring with LLMs for Efficient Neural Architecture Search

LLM为高效神经架构搜索构建设计知识

作者 PulseAugur 编辑部 · [1 个来源] · 2026-10-19 04:00

研究人员开发了一种新的神经架构搜索（NAS）方法，该方法利用大型语言模型（LLM）来构建设计知识，从而能够更有效地探索开放式搜索空间。所提出的方法FairNAD为架构属性定义了一个高级结构模板，LLM通过分析研究论文来填充该模板。这创建了一个丰富的搜索空间，指导NAS过程，从而在CIFAR-10、CIFAR-100和ImageNet16-120等基准数据集上提高了性能。 AI

影响通过构建知识以实现自动化架构发现，提高了AI模型设计的效率。

排序理由该集群包含一篇详细介绍神经架构搜索新方法的学术论文。[lever_c_demoted from research: ic=1 ai=1.0]

在 arXiv cs.CV 阅读 →

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报道来源 [1]

arXiv cs.CV TIER_1 English(EN) · Yuiko Sakuma, Masakazu Yoshimura, Marcel Gr\"opl, Zitang Sun, Junji Otsuka, Atsushi Irie, Takeshi Ohashi · 2026-10-19 04:00

结构化开放式NAS：利用LLM进行半自动设计知识结构化以实现高效神经架构搜索

arXiv:2605.19247v2 Announce Type: replace Abstract: Current neural architecture search (NAS) methods are often limited by their predefined, restrictive search spaces. While recent large language model (LLM)-assisted NAS methods enable open-ended search spaces, they often suffer f…

本文如何被排名

报道来源 [1]

结构化开放式NAS：利用LLM进行半自动设计知识结构化以实现高效神经架构搜索

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