71 citations · 73 across the 5 of their papers we have counts for
5 papers
An Interpretable Model of Climate Change Using Correlative Learning
Charles Anderson, Jason Stock
Determining changes in global temperature and precipitation that may indicate climate change is complicated by annual variations. One approach for finding potential climate change…
Attention-Based Scattering Network for Satellite Imagery
Jason Stock, Chuck Anderson
Multi-channel satellite imagery, from stacked spectral bands or spatiotemporal data, have meaningful representations for various atmospheric properties. Combining these features in…
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…
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…
Indicator patterns of forced change learned by an artificial neural network
Elizabeth A. Barnes, Benjamin Toms, James W. Hurrell +3
Many problems in climate science require the identification of signals obscured by both the "noise" of internal climate variability and differences across models. Following previou…