By folding normalization into a KL-based objective over un-normalized potentials, neural likelihood approximation becomes a strictly convex problem with provable consistency.
Optical tomography in medical imaging
1 Pith paper cite this work. Polarity classification is still indexing.
1
Pith paper citing it
fields
stat.ML 1years
2026 1verdicts
CONDITIONAL 1representative citing papers
citing papers explorer
-
A Convex Approximation Framework for Neural Likelihood-Based Bayesian Inverse Problems
By folding normalization into a KL-based objective over un-normalized potentials, neural likelihood approximation becomes a strictly convex problem with provable consistency.