Robust Machine Learning Systems: Challenges,Current Trends, Perspectives, and the Road Ahead

Shafique, Muhammad; Naseer, Mahum; Theocharides, Theocharis; Kyrkou, Christos; Mutlu, Onur; Orosa, Lois; Choi, Jungwook

doi:10.1109/MDAT.2020.2971217

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Robust Machine Learning Systems: Challenges,Current Trends, Perspectives, and the Road Ahead

Shafique, Muhammad;
Naseer, Mahum;
Theocharides, Theocharis;
Kyrkou, Christos;
Mutlu, Onur;
... Choi, Jungwook;
외 1명

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Currently, 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 University

키워드

Training data; Artificial neural networks; Reliability; Smart devices; Hardware; Machine learning; DEEP NEURAL-NETWORKS; ABSTRACTION-REFINEMENT; MEMORY; RELIABILITY; ERRORS

제목: Robust Machine Learning Systems: Challenges,Current Trends, Perspectives, and the Road Ahead

저자: Shafique, Muhammad; Naseer, Mahum; Theocharides, Theocharis; Kyrkou, Christos; Mutlu, Onur; Orosa, Lois; Choi, Jungwook

DOI: 10.1109/MDAT.2020.2971217

발행일: 2020-04

유형: Article

저널명: IEEE Design and Test

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