Amortized Factor Inference Networks for Posterior Inference
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Computer Science > Machine Learning
Title:Amortized Factor Inference Networks for Posterior Inference
Abstract:Amortized inference promises fast test-time Bayesian inference, but existing methods are inherently tied to fixed models. Extending amortization to unseen models typically requires retraining or costly test-time finetuning. In this paper, we ask: is it possible to build a single inference network capable of generalizing across varying priors, likelihoods, and dimensionality? We introduce Amortized Factor Inference Networks (AFINs), a family of encode-merge-decode inference networks built on dimension-independent modules that map a model specification and its observations to the parameters of a variational posterior. Experimentally, a single trained AFIN achieves posterior accuracy comparable to NUTS and several variational inference methods, while requiring 2 to 4 orders of magnitude less test-time compute. Code is available at this https URL.
| Subjects: | Machine Learning (cs.LG) |
| Cite as: | arXiv:2605.26419 [cs.LG] |
| (or arXiv:2605.26419v1 [cs.LG] for this version) | |
| https://doi.org/10.48550/arXiv.2605.26419
arXiv-issued DOI via DataCite (pending registration)
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