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Faster Uncertainty Quantification for Inverse Problems with Conditional Normalizing Flows
[article]

2020
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arXiv
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pre-print

In inverse problems, we often have access to data consisting of paired samples (x,y)∼ p_X,Y(x,y) where y are partial observations of a physical system, and x represents the unknowns of the problem. Under these circumstances, we can employ supervised training to learn a solution x and its uncertainty from the observations y. We refer to this problem as the "supervised" case. However, the data y∼ p_Y(y) collected at one point could be distributed differently than observations y'∼ p_Y'(y'),

arXiv:2007.07985v1
fatcat:eraddlxs3vdangqzziykadcuue