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School of Mathematics and Maxwell Institute for Mathematical Sciences, School of Informatics, University of Edinburgh
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Uncertainty-quantified neural surrogates can drastically enhance Bayesian inference efficiency for complex inverse problems, outperforming traditional methods in high-dimensional settings.
Predicting unseen links in knowledge graphs can often hinge on just a single half-link, revealing critical insights into model generalization.
