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arXiv · 2310.19608

On Feynman--Kac training of partial Bayesian neural networks

Zheng Zhao·Sebastian Mair·Thomas B. Schön·Jens Sjölund
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Abstract

Recently, partial Bayesian neural networks (pBNNs), which only consider a subset of the parameters to be stochastic, were shown to perform competitively with full Bayesian neural networks. However, pBNNs are often multi-modal in the latent variable space and thus challenging to approximate with parametric models. To address this problem, we propose an efficient sampling-based training strategy, wherein the training of a pBNN is formulated as simulating a Feynman--Kac model. We then describe variations of sequential Monte Carlo samplers that allow us to simultaneously estimate the parameters and the latent posterior distribution of this model at a tractable computational cost. Using various synthetic and real-world datasets we show that our proposed training scheme outperforms the state of the art in terms of predictive performance.

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Zheng Zhao, Sebastian Mair, Thomas B. Schön, Jens Sjölund. 2023-10-30. On Feynman--Kac training of partial Bayesian neural networks. https://arxiv.org/abs/2310.19608

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