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PosteriorBench: From Point Estimates to Posterior Matching in Evaluating Generative Inverse Solvers

Yao, Jiachen et al.
arXiv (All) · Papers
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and-sciencecomputational-engineeringfinancemachine-learning
Generative models are increasingly used to solve scientific inverse problems, but existing evaluations still focus primarily on whether a method can produce a single plausible reconstruction. This is insufficient for ill-posed problems, where multiple solutions may be consistent with the same sparse
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