9 citations · 15 across the 3 of their papers we have counts for
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cs.LG2022★ 9 cited
Can we integrate spatial verification methods into neural-network loss functions for atmospheric science?
Ryan Lagerquist, Imme Ebert-Uphoff
In the last decade, much work in atmospheric science has focused on spatial verification (SV) methods for gridded prediction, which overcome serious disadvantages of pixelwise veri…
cs.LG2021★ 6 cited
CIRA Guide to Custom Loss Functions for Neural Networks in Environmental Sciences -- Version 1
Imme Ebert-Uphoff, Ryan Lagerquist, Kyle Hilburn +5
Neural networks are increasingly used in environmental science applications. Furthermore, neural network models are trained by minimizing a loss function, and it is crucial to choo…