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Trends in Neural Architecture Search: Towards the Acceleration of Search

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dc.contributor.authorKim, Youngkee-
dc.contributor.authorYun, Won Joon-
dc.contributor.authorLee, Youn Kyu-
dc.contributor.authorJung, Soyi-
dc.contributor.authorKim, Joongheon-
dc.date.accessioned2023-12-11T07:06:40Z-
dc.date.available2023-12-11T07:06:40Z-
dc.date.issued2021-
dc.identifier.issn2162-1233-
dc.identifier.urihttps://scholarworks.bwise.kr/hongik/handle/2020.sw.hongik/31956-
dc.description.abstractIn modern deep learning research, finding optimal (or near optimal) neural network models is one of major research directions and it is widely studied in many applications. In this paper, the main research trends of neural architecture search (NAS) are classified as neuro-evolutionary algorithms, reinforcement learning based algorithms, and one-shot architecture search approaches. Furthermore, each research trend is introduced and finally all the major three trends are compared. Lastly, the future research directions of NAS research trends are discussed.-
dc.format.extent4-
dc.language영어-
dc.language.isoENG-
dc.publisherIEEE-
dc.titleTrends in Neural Architecture Search: Towards the Acceleration of Search-
dc.typeArticle-
dc.publisher.location미국-
dc.identifier.doi10.1109/ICTC52510.2021.9621130-
dc.identifier.scopusid2-s2.0-85122944833-
dc.identifier.wosid000790235800101-
dc.identifier.bibliographicCitation12TH INTERNATIONAL CONFERENCE ON ICT CONVERGENCE (ICTC 2021): BEYOND THE PANDEMIC ERA WITH ICT CONVERGENCE INNOVATION, v.2021-October, pp 421 - 424-
dc.citation.title12TH INTERNATIONAL CONFERENCE ON ICT CONVERGENCE (ICTC 2021): BEYOND THE PANDEMIC ERA WITH ICT CONVERGENCE INNOVATION-
dc.citation.volume2021-October-
dc.citation.startPage421-
dc.citation.endPage424-
dc.type.docTypeProceedings Paper-
dc.description.isOpenAccessN-
dc.relation.journalResearchAreaEngineering-
dc.relation.journalWebOfScienceCategoryEngineering, Electrical & Electronic-
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