Neural network attribution methods for problems in geoscience: A novel synthetic benchmark dataset

Despite the increasingly successful application of neural networks to many problems in the geosciences, their complex and nonlinear structure makes the interpretation of their predictions difficult, which limits model trust and does not allow scientists to gain physical insights about the problem at...

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Main Authors: Antonios Mamalakis, Imme Ebert-Uphoff, Elizabeth A. Barnes
פורמט: Article
שפה:English
יצא לאור: Cambridge University Press 2022-01-01
סדרה:Environmental Data Science
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גישה מקוונת:https://www.cambridge.org/core/product/identifier/S2634460222000073/type/journal_article