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...
| Päätekijät: | , , |
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| Aineistotyyppi: | Artikkeli |
| Kieli: | English |
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Cambridge University Press
2022-01-01
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| Sarja: | Environmental Data Science |
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| Linkit: | https://www.cambridge.org/core/product/identifier/S2634460222000073/type/journal_article |