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Found 2 papers, 1 papers with code
Utilizing variational autoencoders in the Bayesian inverse problem of photoacoustic tomography
In this work, we study a machine learning based approach for the Bayesian inverse problem of photoacoustic tomography.
On Learned Operator Correction in Inverse Problems
We discuss the possibility to learn a data-driven explicit model correction for inverse problems and whether such a model correction can be used within a variational framework to obtain regularised reconstructions.
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