arXiv stat.ML
· Papers
An invertible generative model for forward and inverse problems
arXiv:2509.03910v2 Announce Type: replace Abstract: We formulate inverse problems in a Bayesian framework and aim to train an invertible generative model that is capable of simulation (i.e., sampling from the likelihood) and inference (i.e., sampling from the posterior). We call such a generative model a Reversible Sim