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

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...

書目詳細資料
Main Authors:	Antonios Mamalakis, Imme Ebert-Uphoff, Elizabeth A. Barnes
格式:	Article
語言:	English
出版:	Cambridge University Press 2022-01-01
叢編:	Environmental Data Science
主題:	attribution benchmark eXplainable Artificial Intelligence geosciences ground truth neural networks regression problems
在線閱讀:	https://www.cambridge.org/core/product/identifier/S2634460222000073/type/journal_article

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