Please use this identifier to cite or link to this item:
https://doi.org/10.21256/zhaw-20248Full metadata record
| DC Field | Value | Language |
|---|---|---|
| dc.contributor.author | Doran, Hans Dermot | - |
| dc.contributor.author | Reif, Monika Ulrike | - |
| dc.date.accessioned | 2020-07-13T08:22:19Z | - |
| dc.date.available | 2020-07-13T08:22:19Z | - |
| dc.date.issued | 2020-06-23 | - |
| dc.identifier.uri | https://arxiv.org/abs/2007.01900 | de_CH |
| dc.identifier.uri | https://digitalcollection.zhaw.ch/handle/11475/20248 | - |
| dc.description.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. | de_CH |
| dc.language.iso | en | de_CH |
| dc.publisher | WEKA | de_CH |
| dc.rights | Licence according to publishing contract | de_CH |
| dc.subject | Functional safety | de_CH |
| dc.subject | Dependability | de_CH |
| dc.subject | Redundancy | de_CH |
| dc.subject | Machine learning | de_CH |
| dc.subject.ddc | 006: Spezielle Computerverfahren | de_CH |
| dc.title | Examining redundancy in the context of safe machine learning | de_CH |
| dc.type | Konferenz: Paper | de_CH |
| dcterms.type | Text | de_CH |
| zhaw.departement | School of Engineering | de_CH |
| zhaw.organisationalunit | Institut für Angewandte Mathematik und Physik (IAMP) | de_CH |
| zhaw.organisationalunit | Institute of Embedded Systems (InES) | de_CH |
| dc.identifier.doi | 10.21256/zhaw-20248 | - |
| zhaw.conference.details | Forum Safety & Security, online, 23-24 June 2020 | de_CH |
| zhaw.funding.eu | No | de_CH |
| zhaw.originated.zhaw | Yes | de_CH |
| zhaw.publication.status | publishedVersion | de_CH |
| zhaw.publication.review | Peer review (Abstract) | de_CH |
| zhaw.title.proceedings | Proceedings of the Forum for Safety & Security 2020 | de_CH |
| zhaw.webfeed | Information Engineering | de_CH |
| zhaw.author.additional | No | de_CH |
| zhaw.display.portrait | Yes | de_CH |
| Appears in collections: | Publikationen School of Engineering | |
Files in This Item:
| File | Description | Size | Format | |
|---|---|---|---|---|
| 2020_Doran-Reif_Redundancy-Machine-Learning_FSS20-Paper.pdf | 445.39 kB | Adobe PDF |  View/Open |
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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.
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