Academic Journal
Lightning UQ Box: Uncertainty Quantification for Neural Networks.
| Τίτλος: | Lightning UQ Box: Uncertainty Quantification for Neural Networks. |
|---|---|
| Συγγραφείς: | Lehmann, Nils1 N.LEHMANN@TUM.DE, Gottschling, Nina Maria2 NINA-MARIA.GOTTSCHLING@DLR.DE, Gawlikowski, Jakob2 JAKOB.GAWLIKOWSKI@DLR.DE, Stewart, Adam J.1 ADAM.STEWART@TUM.DE, Depeweg, Stefan STEFAN.DEPEWEG@SIEMENS.COM, Nalisnick, Eric3 NALISNICK@JHU.EDU |
| Πηγή: | Journal of Machine Learning Research. Jan-Dec2025, Vol. 26, p1-7. 7p. |
| Θεματικοί όροι: | *Uncertainty (Information theory), *Software libraries (Computer programming), *Artificial neural networks, *Regression analysis, Image segmentation, Deep learning, Classification |
| Περίληψη: | 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. [ABSTRACT FROM AUTHOR] |
| Copyright of Journal of Machine Learning Research is the property of Microtome Publishing and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.) | |
| Βάση Δεδομένων: | Business Source Index |
καταχωρήστε σχόλιο πρώτοι!