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Measuring deep learning performance - an empirical study of performance distributions across architectures and tasks

Published Online: 2026-05-04

Authors

Coakley, Kevin L.

Gundersen, Odd Erik
License Information

Text and Data Mining valid from 2026-05-04

Accepted Manuscript valid from 2026-05-04
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Article History

Received: 19 February 2026

Accepted: 16 April 2026

First Online: 4 May 2026

Declarations

:

: The authors declare no competing interests.

: This article does not contain any studies with human or animal participants.

: There are no human participants in this article and informed consent is not required.

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