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arXiv Machine Learning By Fabian Schneider, Tapio Helin, Leila Taghizadeh

A Convex Approximation Framework for Neural Likelihood-Based Bayesian Inverse Problems

Read the original on arXiv Machine Learning →

arXiv:2607. 06252v1 Announce Type: cross Abstract: Many problems in science and engineering are difficult to model accurately, either due to unknown physical mechanisms, poorly quantified measurement uncertainty, or prohibitive computational costs of high-fidelity simulations.

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