Variational meta-learning inference for low dimensional neural system identification
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Computer Science > Machine Learning
arXiv:2607.18965 (cs)
[Submitted on 21 Jul 2026]
Title:Variational meta-learning inference for low dimensional neural system identification
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Abstract:Deep learning has proven highly effective for nonlinear system identification, but heavily parameterized neural networks are prone to overfitting in low-data regimes and lack reliable uncertainty quantification. The recently developed manifold meta-learning framework addresses the data efficiency problem by restricting the model parameters to a meta-learned low-dimensional manifold. However, that method is purely deterministic. We propose a fully probabilistic extension of the manifold meta-learning framework, based on amortized Variational Inference, where a generative prior over the low-dimensional parameter manifold is learned. During task-specific adaptation, we combine Maximum A Posteriori estimation with the Laplace approximation to yield a mathematically grounded posterior approximation. Evaluated on a static regression task and the Bouc--Wen dynamical system benchmark, the proposed approach achieves predictive accuracy comparable to its deterministic counterpart while successfully providing calibrated uncertainty bounds in severely low-data regimes.
| Subjects: | Machine Learning (cs.LG); Artificial Intelligence (cs.AI); Systems and Control (eess.SY) |
| Cite as: | arXiv:2607.18965 [cs.LG] |
| (or arXiv:2607.18965v1 [cs.LG] for this version) | |
| https://doi.org/10.48550/arXiv.2607.18965
arXiv-issued DOI via DataCite (pending registration)
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