no code implementations • 19 Aug 2022 • Antonios Mamalakis, Elizabeth A. Barnes, Imme Ebert-Uphoff
We highlight that different baselines can lead to different insights for different science questions and, thus, should be chosen accordingly.
1 code implementation • 7 Feb 2022 • Antonios Mamalakis, Elizabeth A. Barnes, Imme Ebert-Uphoff
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.
1 code implementation • 16 Apr 2021 • Elizabeth A. Barnes, Randal J. Barnes
We introduce a novel loss function, termed "abstention loss", that allows neural networks to identify forecasts of opportunity for regression problems.
1 code implementation • 16 Apr 2021 • Elizabeth A. Barnes, Randal J. Barnes
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.
1 code implementation • 18 Mar 2021 • Antonios Mamalakis, Imme Ebert-Uphoff, Elizabeth A. Barnes
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
no code implementations • 22 Jan 2021 • Christopher Irrgang, Niklas Boers, Maike Sonnewald, Elizabeth A. Barnes, Christopher Kadow, Joanna Staneva, Jan Saynisch-Wagner
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.
no code implementations • 14 Dec 2020 • Elizabeth A. Barnes, Kirsten Mayer, Benjamin Toms, Zane Martin, Emily Gordon
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
no code implementations • 4 Dec 2019 • Benjamin A. Toms, Elizabeth A. Barnes, Imme Ebert-Uphoff
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.