Neural architecture search: Difference between revisions

Revision as of 02:11, 7 March 2022

Revision as of 13:37, 11 March 2022

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* The ''performance estimation strategy'' evaluates the performance of a possible ANN from its design (without constructing and training it).

NAS is closely related to [[hyperparameter optimization]]<ref>Matthias Feurer and Frank Hutter. [https://link.springer.com/content/pdf/10.1007%2F978-3-030-05318-5_1.pdf Hyperparameter optimization]. In: ''AutoML: Methods, Systems, Challenges'', pages 3–38.</ref> and [[Meta learning (computer science)|meta-learning]]<ref>{{Cite book|chapter-url=https://link.springer.com/chapter/10.1007/978-3-030-05318-5_2|doi = 10.1007/978-3-030-05318-5_2|chapter = Meta-Learning|title = Automated Machine Learning|series = The Springer Series on Challenges in Machine Learning|year = 2019|last1 = Vanschoren|first1 = Joaquin|pages = 35–61|isbn = 978-3-030-05317-8|s2cid = 239362577}}</ref> and is a subfield of [[automated machine learning]] (AutoML).<ref>{{Cite journal|author1=He, X. |author2=Zhao, K. |author3=Chu, X|date=2021-01-05|title=AutoML: A survey of the state-of-the-art|url=https://www.sciencedirect.com/science/article/abs/pii/S0950705120307516|journal=Knowledge-Based Systems|language=en|volume=212|pages=106622|doi=10.1016/j.knosys.2020.106622|issn=0950-7051|via=|arxiv=1908.00709|s2cid=199405568}}</ref>

==Reinforcement learning==