6 citations · 10 across the 9 of their papers we have counts for
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cs.LG2022
Interpretable Climate Change Modeling With Progressive Cascade Networks
Charles Anderson, Jason Stock, David Anderson
Typical deep learning approaches to modeling high-dimensional data often result in complex models that do not easily reveal a new understanding of the data. Research in the deep le…
cs.LG2022★ 1 cited
Trainable Wavelet Neural Network for Non-Stationary Signals
Jason Stock, Chuck Anderson
This work introduces a wavelet neural network to learn a filter-bank specialized to fit non-stationary signals and improve interpretability and performance for digital signal proce…
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…