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dc.contributor.author최정욱-
dc.date.accessioned2021-11-02T07:48:44Z-
dc.date.available2021-11-02T07:48:44Z-
dc.date.issued2020-04-
dc.identifier.citationIEEE DESIGN & TEST, v. 37, no. 2, page. 30-57en_US
dc.identifier.issn2168-2356-
dc.identifier.issn2168-2364-
dc.identifier.urihttps://ieeexplore.ieee.org/document/8979377-
dc.identifier.urihttps://repository.hanyang.ac.kr/handle/20.500.11754/166153-
dc.description.abstractCurrently, machine learning (ML) techniques are at the heart of smart cyber-physical systems (CPS) and Internet-of-Things (IoT). This article discusses various challenges and probable solutions for security attacks on these ML-inspired hardware and software techniques. -Partha Pratim Pande, Washington State Universityen_US
dc.language.isoenen_US
dc.publisherIEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INCen_US
dc.subjectTraining dataen_US
dc.subjectArtificial neural networksen_US
dc.subjectReliabilityen_US
dc.subjectSmart devicesen_US
dc.subjectHardwareen_US
dc.subjectMachine learningen_US
dc.titleRobust Machine Learning Systems: Challenges,Current Trends, Perspectives, and the Road Aheaden_US
dc.typeArticleen_US
dc.relation.no2-
dc.relation.volume37-
dc.identifier.doi10.1109/MDAT.2020.2971217-
dc.relation.page30-57-
dc.relation.journalIEEE DESIGN & TEST-
dc.contributor.googleauthorShafique, Muhammad-
dc.contributor.googleauthorNaseer, Mahum-
dc.contributor.googleauthorTheocharides, Theocharis-
dc.contributor.googleauthorKyrkou, Christos-
dc.contributor.googleauthorMutlu, Onur-
dc.contributor.googleauthorOrosa, Lois-
dc.contributor.googleauthorChoi, Jungwook-
dc.relation.code2020052595-
dc.sector.campusS-
dc.sector.daehakCOLLEGE OF ENGINEERING[S]-
dc.sector.departmentSCHOOL OF ELECTRONIC ENGINEERING-
dc.identifier.pidchoij-
Appears in Collections:
COLLEGE OF ENGINEERING[S](공과대학) > ELECTRONIC ENGINEERING(융합전자공학부) > Articles
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