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Found 8 papers, 1 papers with code
DiffObs: Generative Diffusion for Global Forecasting of Satellite Observations
This work presents an autoregressive generative diffusion model (DiffObs) to predict the global evolution of daily precipitation, trained on a satellite observational product, and assessed with domain-specific diagnostics.
An Interpretable Model of Climate Change Using Correlative Learning
To find these optimal patterns, a new way of interpreting what the neural network has learned is explored.
Attention-Based Scattering Network for Satellite Imagery
Multi-channel satellite imagery, from stacked spectral bands or spatiotemporal data, have meaningful representations for various atmospheric properties.
Interpretable Climate Change Modeling With Progressive Cascade Networks
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.
Trainable Wavelet Neural Network for Non-Stationary Signals
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 processing.
CIRA Guide to Custom Loss Functions for Neural Networks in Environmental Sciences -- Version 1
Standard loss functions do not cover all the needs of the environmental sciences, which makes it important for scientists to be able to develop their own custom loss functions so that they can implement many of the classic performance measures already developed in environmental science, including measures developed for spatial model verification.
Who's a Good Boy? Reinforcing Canine Behavior in Real-Time using Machine Learning
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 in real-time.
Strategies for Robust Image Classification
In this work we evaluate the impact of digitally altered images on the performance of artificial neural networks.
