Author

Charlotte Debus

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#artificial intelligence Preprint Sep 2026

4D Parallelism Unlocks Exascale Bayesian Neural Networks for High-Fidelity Atmospheric Modeling

We present BEAST, the first-ever Bayesian Swin Transformer for atmospheric forecasting on 0.25$^\circ$ global resolution able to accurately quantify both aleatoric and epistemic uncertainty. To overcome the associated computational bottlenecks, we devise an orthogonal 4D-parallelization scheme that introduces a unique...

Deifilia Kieckhefen, J. P. G. H. Muriedas, L. Heyen et al. · 0 citations

Jul 2026

Rethinking Likelihood distributions: Student's t Likelihood Boosts Bayesian Neural Network Performance

In Bayesian neural networks (BNNs), variational inference is a widely adopted framework for modeling uncertainty in a distributional way, with the evidence lower bound (ELBO) serving as the standard objective function. Several distributions contribute to the ELBO loss, such as the prior, approximated posterior, and lik...

Pei-Hsuan Hsia, L. Heyen, Arvid Weyrauch et al. · arXiv.org · 0 citations