Skip to yearly menu bar Skip to main content


Poster
in
Workshop: Bayesian Deep Learning

Regularizations Are All You Need: Weather Prediction Under Distributional Shift

Sankalp Gilda · Neel Bhandari · Wendy Wing Yee Mak · Andrea Panizza


Abstract:

In this paper, we present preliminary results on improving out-of-domain weather prediction and uncertainty estimation as part of the \texttt{Shifts Challenge on Robustness and Uncertainty under Real-World Distributional Shift} challenge. Our preliminary results show that by leveraging an ensemble of Bayesian models and thoughtful;y splitting the training set, we can achieve more robust and accurate results than standard libraries. We quantify our predictions using several metrics and propose several future lines of inquiry and experimentation to boost performance.

Chat is not available.