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Investigating the fidelity of explainable artificial intelligence
  methods for applications of convolutional neural networks in geoscience

Investigating the fidelity of explainable artificial intelligence methods for applications of convolutional neural networks in geoscience

7 February 2022
Antonios Mamalakis
E. Barnes
I. Ebert‐Uphoff
ArXivPDFHTML

Papers citing "Investigating the fidelity of explainable artificial intelligence methods for applications of convolutional neural networks in geoscience"

3 / 3 papers shown
Title
Tackling the Accuracy-Interpretability Trade-off in a Hierarchy of Machine Learning Models for the Prediction of Extreme Heatwaves
Tackling the Accuracy-Interpretability Trade-off in a Hierarchy of Machine Learning Models for the Prediction of Extreme Heatwaves
Alessandro Lovo
Amaury Lancelin
Corentin Herbert
Freddy Bouchet
AI4CE
23
0
0
01 Oct 2024
Distilling Machine Learning's Added Value: Pareto Fronts in Atmospheric Applications
Distilling Machine Learning's Added Value: Pareto Fronts in Atmospheric Applications
Tom Beucler
Arthur Grundner
Sara Shamekh
Peter Ukkonen
Matthew Chantry
Ryan Lagerquist
43
0
0
04 Aug 2024
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
Antonios Mamalakis
I. Ebert‐Uphoff
E. Barnes
OOD
12
75
0
18 Mar 2021
1