RAMP: Recognition parametrisation by Amortised Message Passing
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Computer Science > Machine Learning
Title:RAMP: Recognition parametrisation by Amortised Message Passing
Abstract:A central aim of unsupervised learning is to uncover latent factors that explain dependencies among observations. Probabilistic models typically achieve this by introducing multiple latent variables linked through a graph of conditional relationships, with distributional parameters and their dependence learnt from data. Learning relies either on distributional choices that allow tractable belief propagation, or on approximations that scale poorly with model size and complexity. We build on the recently developed recognition-parametrised modelling paradigm to propose an alternative approach: RAMP, a method that implicitly defines latent structure by learning a flexible, nonlinear, amortised message-passing framework. We show that RAMP enables efficient likelihood-based recovery of latent-variable distributions within expressive nonlinear models acting on complex high-dimensional data.
| Comments: | In the proceedings of the Symposium on Probabilistic Machine Learning 2026 (ProbML 2026) |
| Subjects: | Machine Learning (cs.LG); Artificial Intelligence (cs.AI) |
| Cite as: | arXiv:2607.18883 [cs.LG] |
| (or arXiv:2607.18883v1 [cs.LG] for this version) | |
| https://doi.org/10.48550/arXiv.2607.18883
arXiv-issued DOI via DataCite (pending registration)
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