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pro vyhledávání: '"Gundersen, Odd Erik"'
Autor:
Kjærnli, Håkon Hanisch, Mas-Ribas, Lluis, Ashrafi, Aida, Sizov, Gleb, Langseth, Helge, Gundersen, Odd Erik
A common issue for machine learning models applied to time-series forecasting is the temporal evolution of the data distributions (i.e., concept drift). Because most of the training data does not reflect such changes, the models present poor performa
Externí odkaz:
http://arxiv.org/abs/2403.03508
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 learn
Externí odkaz:
http://arxiv.org/abs/2312.06633
Autor:
Kapoor, Sayash, Cantrell, Emily, Peng, Kenny, Pham, Thanh Hien, Bail, Christopher A., Gundersen, Odd Erik, Hofman, Jake M., Hullman, Jessica, Lones, Michael A., Malik, Momin M., Nanayakkara, Priyanka, Poldrack, Russell A., Raji, Inioluwa Deborah, Roberts, Michael, Salganik, Matthew J., Serra-Garcia, Marta, Stewart, Brandon M., Vandewiele, Gilles, Narayanan, Arvind
Machine learning (ML) methods are proliferating in scientific research. However, the adoption of these methods has been accompanied by failures of validity, reproducibility, and generalizability. These failures can hinder scientific progress, lead to
Externí odkaz:
http://arxiv.org/abs/2308.07832
Background: Many published machine learning studies are irreproducible. Issues with methodology and not properly accounting for variation introduced by the algorithm themselves or their implementations are attributed as the main contributors to the i
Externí odkaz:
http://arxiv.org/abs/2204.07610
Publikováno v:
In Expert Systems With Applications 15 August 2024 248
Autor:
Gundersen, Odd Erik
Reproducibility is a confused terminology. In this paper, I take a fundamental view on reproducibility rooted in the scientific method. The scientific method is analysed and characterised in order to develop the terminology required to define reprodu
Externí odkaz:
http://arxiv.org/abs/2011.10098
Akademický článek
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Akademický článek
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Autor:
Fleten, Kristine Klock, Aasgård, Ellen Krohn, Xing, Liyuan, Grøttum, Hanne Høie, Fleten, Stein-Erik, Gundersen, Odd Erik
Publikováno v:
Computational Management Science; 11/16/2024, Vol. 21 Issue 2, p1-24, 24p