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Open Access 06-05-2024 | Retraction Note

Retraction Note: Robust adversarial uncertainty quantification for deep learning fine-tuning

Authors: Usman Ahmed, Jerry Chun-Wei Lin

Published in: The Journal of Supercomputing | Issue 12/2024

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Notes
The original article can be found online at https://​doi.​org/​10.​1007/​s11227-023-05087-5.

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Correction to: The Journal of Supercomputing (2023) 79:11355–11386 https://doi.org/10.1007/s11227-023-05087-5
The Editor-in-Chief has retracted this article at the author's request. An investigation by the Western Norway University of Applied Sciences found that some of the contents of the article show significant similarity with previously published articles [1] by different authors.
All authors agree to this retraction.
Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://​creativecommons.​org/​licenses/​by/​4.​0/​.

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Metadata
Title
Retraction Note: Robust adversarial uncertainty quantification for deep learning fine-tuning
Authors
Usman Ahmed
Jerry Chun-Wei Lin
Publication date
06-05-2024
Publisher
Springer US
Published in
The Journal of Supercomputing / Issue 12/2024
Print ISSN: 0920-8542
Electronic ISSN: 1573-0484
DOI
https://doi.org/10.1007/s11227-024-06156-z

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