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English(EN) Coverage You Can Steer: Online Conformal Calibration for RL-Driven Hardware-Aware NAS

新的校准方法提高了神经架构搜索的效率

研究人员开发了一种名为在线一致性校准(OCC)的新方法,以提高硬件感知神经架构搜索(NAS)的效率。传统的NAS方法成本高昂,因为它们需要训练每一个架构来评估其性能。OCC是conformal prediction的扩展,它使用自适应反馈控制来维持所需的一致性覆盖水平,确保指定比例的候选架构不会被错误地丢弃。这种方法在不牺牲准确性的情况下,显著减少了所需的评估次数,修剪了25-50%的候选架构,并且与静态方法或高斯过程基线相比,提供了更好的覆盖控制。 AI

影响 通过降低计算成本而不牺牲准确性来提高神经架构搜索的效率。

排序理由 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.LG 阅读 →

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新的校准方法提高了神经架构搜索的效率

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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]
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报道来源 [1]

  1. arXiv cs.LG TIER_1 English(EN) · Pedro Brandimarte, Nerea Aranjuelo, Marcos Nieto, Oihana Otaegui ·

    可控的覆盖范围:用于 RL 驱动的硬件感知 NAS 的在线一致性校准

    arXiv:2610.03127v1 Announce Type: new Abstract: Hardware-aware neural architecture search (NAS) is dominated by evaluation cost: every architecture must be trained before its reward is known. Conformal-prediction filters cut this cost by pruning candidates whose predicted-reward …