Search Results for author:
Found 8 papers, 4 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.
Controlled abstention neural networks for identifying skillful predictions for regression problems
We introduce a novel loss function, termed "abstention loss", that allows neural networks to identify forecasts of opportunity for regression problems.
Controlled abstention neural networks for identifying skillful predictions for classification problems
The NotWrong loss introduces an abstention class that allows the network to identify the more confident samples and abstain (say "I don't know") on the less confident samples.
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
Will Artificial Intelligence supersede Earth System and Climate Models?
We outline a perspective of an entirely new research branch in Earth and climate sciences, where deep neural networks and Earth system models are dismantled as individual methodological approaches and reassembled as learning, self-validating, and interpretable Earth system model-network hybrids.
Identifying Opportunities for Skillful Weather Prediction with Interpretable Neural Networks
For Earth scientists, these relevant regions for the neural network's prediction are by far the most important product of our study: they provide scientific insight into the physical mechanisms that lead to enhanced weather predictability.
Atmospheric and Oceanic Physics
Physically Interpretable Neural Networks for the Geosciences: Applications to Earth System Variability
As such, neural networks have often been used within the geosciences to most accurately identify a desired output given a set of inputs, with the interpretation of what the network learns used as a secondary metric to ensure the network is making the right decision for the right reason.
