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Robustifying and Boosting Training-Free Neural Architecture Search
March 13, 2024, 4:42 a.m. | Zhenfeng He, Yao Shu, Zhongxiang Dai, Bryan Kian Hsiang Low
cs.LG updates on arXiv.org arxiv.org
Abstract: Neural architecture search (NAS) has become a key component of AutoML and a standard tool to automate the design of deep neural networks. Recently, training-free NAS as an emerging paradigm has successfully reduced the search costs of standard training-based NAS by estimating the true architecture performance with only training-free metrics. Nevertheless, the estimation ability of these metrics typically varies across different tasks, making it challenging to achieve robust and consistently good search performance on diverse …
abstract architecture arxiv automate automl become boosting costs cs.lg design free key nas networks neural architecture search neural networks paradigm performance search standard tool training true type
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