English(EN) QuaNAS: Uncertainty-Aware Reuse of Earlier Searches for Query-Efficient Neural Architecture Search

QuaNAS 利用先验知识提高神经架构搜索效率

作者 PulseAugur 编辑部 · [1 个来源] · 2026-10-23 04:00

研究人员开发了 QuaNAS，一种新颖的神经架构搜索 (NAS) 方法，该方法利用先前的搜索知识来提高效率。与从头开始的传统方法不同，QuaNAS 使用先前搜索的库，将它们不确定的可靠性视为明确的知识。该方法将设计决策的主效应和零成本代理作为先验，并在每次评估后估计对这些先验的信任度。在跨各种数据集的测试中，QuaNAS 与基线方法相比显著降低了遗憾，并在最终模型选择中取得了相当或更优的性能。 AI

影响这项研究通过降低架构搜索的计算成本，可能导致更高效的 AI 模型开发。

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论文。[lever_c_demoted from research: ic=1 ai=1.0]

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arXiv cs.LG TIER_1 English(EN) · Brama Seta Putra Satria, Dwi Sunaryono, Misbakhul Munir Irfan Subakti, Irzal Ahmad Sabilla, Laras Suciningtyas · 2026-10-23 04:00

QuaNAS: Uncertainty-Aware Reuse of Earlier Searches for Query-Efficient Neural Architecture Search

arXiv:2610.31251v1 Announce Type: new Abstract: Neural architecture search (NAS) on a new dataset often affords only tens of short training runs, so what the search knows before its first evaluation decides its efficiency. Yet most NAS methods start from scratch, guided at best b…

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QuaNAS: Uncertainty-Aware Reuse of Earlier Searches for Query-Efficient Neural Architecture Search

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