6 citations · 10 across the 9 of their papers we have counts for
9 papers
SRViT: Vision Transformers for Estimating Radar Reflectivity from Satellite Observations at Scale
Jason Stock, Kyle Hilburn, Imme Ebert-Uphoff +1
We introduce a transformer-based neural network to generate high-resolution (3km) synthetic radar reflectivity fields at scale from geostationary satellite imagery. This work aims…
DiffObs: Generative Diffusion for Global Forecasting of Satellite Observations
Jason Stock, Jaideep Pathak, Yair Cohen +5
This work presents an autoregressive generative diffusion model (DiffObs) to predict the global evolution of daily precipitation, trained on a satellite observational product, and…
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…
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…
Who's a Good Boy? Reinforcing Canine Behavior in Real-Time using Machine Learning
Jason Stock, Tom Cavey
In this paper we outline the development methodology for an automatic dog treat dispenser which combines machine learning and embedded hardware to identify and reward dog behaviors…
Strategies for Robust Image Classification
Jason Stock, Andy Dolan, Tom Cavey
In this work we evaluate the impact of digitally altered images on the performance of artificial neural networks. We explore factors that negatively affect the ability of an image…