Lehmann, Nils und Gottschling, Nina Maria und Gawlikowski, Jakob und Stewart, Adam J. und Depeweg, Stefan und Nalisnick, Eric (2025) Lightning UQ Box: Uncertainty Quantification for Neural Networks. Journal of Machine Learning Research (26), Seiten 1-7. Microtome Publishing. ISSN 1532-4435.
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Offizielle URL: http://jmlr.org/papers/v26/24-2110.html
Kurzfassung
Although neural networks have shown impressive results in a multitude of application domains, the "black box" nature of deep learning and lack of confidence estimates have led to scepticism, especially in domains like medicine and physics where such estimates are critical. Research on uncertainty quantification (UQ) has helped elucidate the reliability of these models, but existing implementations of these UQ methods are sparse and difficult to reuse. To this end, we introduce Lightning UQ Box, a PyTorch-based Python library for deep learning-based UQ methods powered by PyTorch Lightning. Lightning UQ Box supports classification, regression, semantic segmentation, and pixelwise regression applications, and UQ methods from a variety of theoretical motivations. With this library, we provide an entry point for practitioners new to UQ, as well as easy-to-use components and tools for scalable deep learning applications.
elib-URL des Eintrags: | https://elib.dlr.de/213773/ | ||||||||||||||||||||||||||||
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Dokumentart: | Zeitschriftenbeitrag | ||||||||||||||||||||||||||||
Titel: | Lightning UQ Box: Uncertainty Quantification for Neural Networks | ||||||||||||||||||||||||||||
Autoren: |
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Datum: | 15 März 2025 | ||||||||||||||||||||||||||||
Erschienen in: | Journal of Machine Learning Research | ||||||||||||||||||||||||||||
Referierte Publikation: | Ja | ||||||||||||||||||||||||||||
Open Access: | Ja | ||||||||||||||||||||||||||||
Gold Open Access: | Nein | ||||||||||||||||||||||||||||
In SCOPUS: | Ja | ||||||||||||||||||||||||||||
In ISI Web of Science: | Ja | ||||||||||||||||||||||||||||
Seitenbereich: | Seiten 1-7 | ||||||||||||||||||||||||||||
Verlag: | Microtome Publishing | ||||||||||||||||||||||||||||
Name der Reihe: | Machine Learning Open Source Software | ||||||||||||||||||||||||||||
ISSN: | 1532-4435 | ||||||||||||||||||||||||||||
Status: | veröffentlicht | ||||||||||||||||||||||||||||
Stichwörter: | Uncertainty, Deep Learning, Lightning, Software | ||||||||||||||||||||||||||||
HGF - Forschungsbereich: | Luftfahrt, Raumfahrt und Verkehr | ||||||||||||||||||||||||||||
HGF - Programm: | Raumfahrt | ||||||||||||||||||||||||||||
HGF - Programmthema: | Erdbeobachtung | ||||||||||||||||||||||||||||
DLR - Schwerpunkt: | Raumfahrt | ||||||||||||||||||||||||||||
DLR - Forschungsgebiet: | R EO - Erdbeobachtung | ||||||||||||||||||||||||||||
DLR - Teilgebiet (Projekt, Vorhaben): | R - Künstliche Intelligenz | ||||||||||||||||||||||||||||
Standort: | Oberpfaffenhofen | ||||||||||||||||||||||||||||
Institute & Einrichtungen: | Institut für Methodik der Fernerkundung > EO Data Science | ||||||||||||||||||||||||||||
Hinterlegt von: | Gawlikowski, Jakob | ||||||||||||||||||||||||||||
Hinterlegt am: | 23 Apr 2025 08:30 | ||||||||||||||||||||||||||||
Letzte Änderung: | 18 Jul 2025 11:41 |
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