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Apr, 2021
神经架构搜索的泛化性保证与训练集-验证集分离
Generalization Guarantees for Neural Architecture Search with Train-Validation Split
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Samet Oymak, Mingchen Li, Mahdi Soltanolkotabi
TL;DR
本文探讨了神经架构搜索中基于双层优化的训练-验证分割问题,通过研究验证的风险和超梯度等优化指标,该方法能有效选择最具普适性的模型,并避免过度拟合。同时,文章还建立了一般化界限,探讨了神经架构搜索和多核学习之间的联系。
Abstract
neural architecture search
(NAS) is a popular method for automatically designing optimized architectures for high-performance deep learning. In this approach, it is common to use
bilevel optimization
where one op
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