2023/11/08 by Wang Qinsi, Qinsi, Wang, Jinghan, Ke +3 · 2 citations
Computer Science · Materials Science · #Advanced Neural Network Applications #Artificial Intelligence (cs.AI) #FOS: Computer and information sciences #Machine Learning (cs.LG) #Machine Learning in Materials Science #Neural Networks and Applications
paper · pdf · doi:10.48550/arxiv.2311.04943
openalex publication_date 2023/11/08 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
Neural Architecture Search (NAS) has emerged as a favoured method for unearthing effective neural architectures. Recent development of large models has intensified the demand for faster search speeds and more accurate search results. However, designing large models by NAS is challenging due to the dramatical increase of search space and the associated huge performance evaluation cost. Consider a typical modular search space widely used in NAS, in which a neural architecture consists of m block nodes and a block node has n alternative blocks. Facing the space containing nm candidate networks, existing NAS methods attempt to find the best one by searching and evaluating candidate networks directly.Different from the general strategy that takes architecture search as a whole problem, we propose a novel divide-and-conquer strategy by making use of the modular nature of the search space.Here, we introduce MathNAS, a general NAS framework based on mathematical programming.In MathNAS, the performances of the m*n possible building blocks in the search space are calculated first, and then the performance of a network is directly predicted based on the performances of its building blocks. Although estimating block performances involves network training, just as what happens for network performance evaluation in existing NAS methods, predicting network performance is completely training-free and thus extremely fast. In contrast to the nm candidate networks to evaluate in existing NAS methods, which require training and a formidable computational burden, there are only m*n possible blocks to handle in MathNAS. Therefore, our approach effectively reduces the complexity of network performance evaluation.Our code is available at https://github.com/wangqinsi1/MathNAS.