A single invertible map constructed from two triangular normalizing flows can conditionally sample both the likelihood and the posterior in Bayesian inverse problems.
Learned primal-dual reconstruction
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An invertible generative model for forward and inverse problems
A single invertible map constructed from two triangular normalizing flows can conditionally sample both the likelihood and the posterior in Bayesian inverse problems.