| dc.contributor.author | Coakley, Kevin | |
| dc.contributor.author | Kirkpatrick, Christine | |
| dc.contributor.author | Gundersen, Odd Erik | |
| dc.date.accessioned | 2023-02-27T12:32:00Z | |
| dc.date.available | 2023-02-27T12:32:00Z | |
| dc.date.created | 2023-02-23T10:19:38Z | |
| dc.date.issued | 2022 | |
| dc.identifier.isbn | 978-1-6654-6124-5 | |
| dc.identifier.uri | https://hdl.handle.net/11250/3054245 | |
| dc.description.abstract | Reproducing published deep learning papers to validate their conclusions can be difficult due to sources of irreproducibility. We investigate the impact that implementation factors have on the results and how they affect reproducibility of deep learning studies. Three deep learning experiments were ran five times each on 13 different hardware environments and four different software environments. The analysis of the 780 combined results showed that there was a greater than 6% accuracy range on the same deterministic examples introduced from hardware or software environment variations alone. To account for these implementation factors, researchers should run their experiments multiple times in different hardware and software environments to verify their conclusions are not affected. | en_US |
| dc.description.abstract | Examining the Effect of Implementation Factors on Deep Learning Reproducibility | en_US |
| dc.language.iso | eng | en_US |
| dc.publisher | IEEE | en_US |
| dc.relation.ispartof | 2022 IEEE 18th International Conference on e-Science (e-Science) | |
| dc.title | Examining the Effect of Implementation Factors on Deep Learning Reproducibility | en_US |
| dc.title.alternative | Examining the Effect of Implementation Factors on Deep Learning Reproducibility | en_US |
| dc.type | Chapter | en_US |
| dc.description.version | publishedVersion | en_US |
| dc.source.pagenumber | 397-398 | en_US |
| dc.identifier.cristin | 2128479 | |
| cristin.ispublished | true | |
| cristin.fulltext | original | |
| cristin.qualitycode | 1 | |
