Examining redundancy in the context of safe machine learning

Doran, Hans Dermot; Reif, Monika Ulrike

doi:10.21256/zhaw-20248

Please use this identifier to cite or link to this item: https://doi.org/10.21256/zhaw-20248

Publication type:	Conference paper
Type of review:	Peer review (abstract)
Title:	Examining redundancy in the context of safe machine learning
Authors:	Doran, Hans Dermot Reif, Monika Ulrike
et. al:	No
DOI:	10.21256/zhaw-20248
Proceedings:	Proceedings of the Forum for Safety & Security 2020
Conference details:	Forum Safety & Security, online, 23-24 June 2020
Issue Date:	23-Jun-2020
Publisher / Ed. Institution:	WEKA
Language:	English
Subjects:	Functional safety; Dependability; Redundancy; Machine learning
Subject (DDC):	006: Special computer methods
Abstract:	This paper describes a set of experiments with neural network classifiers on the MNIST database of digits. The purpose is to investigate naïve implementations of redundant architectures as a first step towards safe and dependable machine learning. We report on a set of measurements using the MNIST database which ultimately serve to underline the expected difficulties in using NN classifiers in safe and dependable systems.
URI:	https://arxiv.org/abs/2007.01900 https://digitalcollection.zhaw.ch/handle/11475/20248
Fulltext version:	Published version
License (according to publishing contract):	Licence according to publishing contract
Departement:	School of Engineering
Organisational Unit:	Institute of Applied Mathematics and Physics (IAMP) Institute of Embedded Systems (InES)
Appears in collections:	Publikationen School of Engineering

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Doran, H. D., & Reif, M. U. (2020, June 23). Examining redundancy in the context of safe machine learning. Proceedings of the Forum for Safety & Security 2020. https://doi.org/10.21256/zhaw-20248

Doran, H.D. and Reif, M.U. (2020) ‘Examining redundancy in the context of safe machine learning’, in Proceedings of the Forum for Safety & Security 2020. WEKA. Available at: https://doi.org/10.21256/zhaw-20248.

H. D. Doran and M. U. Reif, “Examining redundancy in the context of safe machine learning,” in Proceedings of the Forum for Safety & Security 2020, Jun. 2020. doi: 10.21256/zhaw-20248.

DORAN, Hans Dermot und Monika Ulrike REIF, 2020. Examining redundancy in the context of safe machine learning. In: Proceedings of the Forum for Safety & Security 2020 [online]. Conference paper. WEKA. 23 Juni 2020. Verfügbar unter: https://arxiv.org/abs/2007.01900

Doran, Hans Dermot, and Monika Ulrike Reif. 2020. “Examining Redundancy in the Context of Safe Machine Learning.” Conference paper. In Proceedings of the Forum for Safety & Security 2020. WEKA. https://doi.org/10.21256/zhaw-20248.

Doran, Hans Dermot, and Monika Ulrike Reif. “Examining Redundancy in the Context of Safe Machine Learning.” Proceedings of the Forum for Safety & Security 2020, WEKA, 2020, https://doi.org/10.21256/zhaw-20248.