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Variational objectives for amortized Bayesian inference in inverse problems: The role of posterior conditioning
Variational autoencoders (VAEs) offer an efficient approach to amortized Bayesian inference for inverse problems, but posterior accuracy can depend strongly on the choice of variational regularization, particularly when the inverse problem contains weakly identified parameter directions. This study investigates three objectives: a reverse Kullback--Leibler formulation (VAE-KL), an asymmetric Jensen--Shannon formulation (VAE-JS), and a Jensen--Shannon--Wasserstein formulation (VAE-JSWA), which replaces the reverse Kullback--Leibler regularizer with the squared 2-Wasserstein distance while retai
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Evidence & attribution
- arXiv · AI, language, vision and robotics · 2026-09-21T07:05:13.000Z
First collected: 2026-09-23T08:01:43.213Z. This is not the publication date.