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...
المؤلفون الرئيسيون: | Antonios Mamalakis, Imme Ebert-Uphoff, Elizabeth A. Barnes |
---|---|
التنسيق: | مقال |
اللغة: | English |
منشور في: |
Cambridge University Press
2022-01-01
|
سلاسل: | Environmental Data Science |
الموضوعات: | |
الوصول للمادة أونلاين: | https://www.cambridge.org/core/product/identifier/S2634460222000073/type/journal_article |
مواد مشابهة
-
Physically Interpretable Neural Networks for the Geosciences: Applications to Earth System Variability
حسب: Benjamin A. Toms, وآخرون
منشور في: (2020-09-01) -
Utilization of Augmented and Virtual Reality in Geoscience
حسب: Věroslav HOLUŠA, وآخرون
منشور في: (2022-06-01) -
Knowledge Graphs and Explainable AI in Healthcare
حسب: Enayat Rajabi, وآخرون
منشور في: (2022-09-01) -
A report on gender diversity and equality in the geosciences: an analysis of the Swiss Geoscience Meetings from 2003 to 2019
حسب: Francesca Piccoli, وآخرون
منشور في: (2021-01-01) -
What Pattern of Progression in Geoscience Fieldwork can be Recognised by Geoscience Educators?
حسب: Chris J.H. King
منشور في: (2019-04-01)