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, 23-24 June 2020, virtueller Event
Issue Date: 23-Jun-2020
Publisher / Ed. Institution: WEKA
Language: English
Subjects: Functional safety; Dependability; Redundancy; Machine learning
Subject (DDC): 004: Computer science
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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