Neural architecture search: Difference between revisions

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{{Short description|Machine learning-powered structure design}}

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'''Neural architecture search''' ('''NAS''')<ref name="survey">{{Cite journal|url=http://jmlr.org/papers/v20/18-598.html|title=Neural Architecture Search: A Survey|first1=Thomas|last1=Elsken|first2=Jan Hendrik|last2=Metzen|first3=Frank|last3=Hutter|date=August 8, 2019|journal=Journal of Machine Learning Research|volume=20|issue=55|pages=1–21|arxiv=1808.05377}}</ref><ref name="survey2">{{cite arXiv|last1=Wistuba|first1=Martin|last2=Rawat|first2=Ambrish|last3=Pedapati|first3=Tejaswini|date=2019-05-04|title=A Survey on Neural Architecture Search|eprint=1905.01392|class=cs.LG}}</ref> is a technique for automating the design of [[artificial neural network]]s (ANN), a widely used model in the field of [[machine learning]]. NAS has been used to design networks that are on par with or outperform hand-designed architectures.<ref name="Zoph 2016" /><ref name="Zoph 2017">{{cite arXiv|last1=Zoph|first1=Barret|last2=Vasudevan|first2=Vijay|last3=Shlens|first3=Jonathon|last4=Le|first4=Quoc V.|date=2017-07-21|title=Learning Transferable Architectures for Scalable Image Recognition|eprint=1707.07012|class=cs.CV}}</ref> Methods for NAS can be categorized according to the search space, search strategy and performance estimation strategy used:<ref name="survey" />

* The ''search space'' defines the type(s) of ANN that can be designed and optimized.