Search Results for author:
Found 3 papers, 2 papers with code
Carefully choose the baseline: Lessons learned from applying XAI attribution methods for regression tasks in geoscience
We highlight that different baselines can lead to different insights for different science questions and, thus, should be chosen accordingly.
Investigating the fidelity of explainable artificial intelligence methods for applications of convolutional neural networks in geoscience
Convolutional neural networks (CNNs) have recently attracted great attention in geoscience due to their ability to capture non-linear system behavior and extract predictive spatiotemporal patterns.
Neural Network Attribution Methods for Problems in Geoscience: A Novel Synthetic Benchmark Dataset
Here, we provide a framework, based on the use of additively separable functions, to generate attribution benchmark datasets for regression problems for which the ground truth of the attribution is known a priori.
Explainable artificial intelligence
Explainable Artificial Intelligence (XAI)
+1
