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dc.contributor.authorZhang, Jeff Junen
dc.contributor.authorLiu, Kangen
dc.contributor.authorKhalid, Faiqen
dc.contributor.authorHanif, Muhammad Abdullahen
dc.contributor.authorRehman, Semeenen
dc.contributor.authorTheocharides, Theocharisen
dc.contributor.authorArtussi, Alessandroen
dc.contributor.authorShafique, Muhammaden
dc.contributor.authorGarg, Siddharthen
dc.coverage.spatialLas Vegas NV USAen
dc.creatorZhang, Jeff Junen
dc.creatorLiu, Kangen
dc.creatorKhalid, Faiqen
dc.creatorHanif, Muhammad Abdullahen
dc.creatorRehman, Semeenen
dc.creatorTheocharides, Theocharisen
dc.creatorArtussi, Alessandroen
dc.creatorShafique, Muhammaden
dc.creatorGarg, Siddharthen
dc.description.abstractMachine learning, in particular deep learning, is being used in almost all the aspects of life to facilitate humans, specifically in mobile and Internet of Things (IoT)-based applications. Due to its state-of-the-art performance, deep learning is also being employed in safety-critical applications, for instance, autonomous vehicles. Reliability and security are two of the key required characteristics for these applications because of the impact they can have on human's life. Towards this, in this paper, we highlight the current progress, challenges and research opportunities in the domain of robust systems for machine learning-based applications.en
dc.sourceProceedings of the 56th Annual Design Automation Conference 2019en
dc.titleBuilding Robust Machine Learning Systems: Current Progress, Research Challenges, and Opportunitiesen
dc.description.endingpage4Πολυτεχνική Σχολή / Faculty of EngineeringΤμήμα Ηλεκτρολόγων Μηχανικών και Μηχανικών Υπολογιστών / Department of Electrical and Computer Engineering
dc.type.uhtypeConference Objecten
dc.contributor.orcidTheocharides, Theocharis [0000-0001-7222-9152]

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