On Feynman--Kac training of partial Bayesian neural networks

• URL: http://arxiv.org/abs/2310.19608v3
• Date: Tue, 27 Feb 2024 09:35:00 GMT
• Title: On Feynman--Kac training of partial Bayesian neural networks
• Authors: Zheng Zhao and Sebastian Mair and Thomas B. Sch\"on and Jens Sj\"olund
• Abstract summary: Partial Bayesian neural networks (pBNNs) were shown to perform competitively with full Bayesian neural networks. We propose an efficient sampling-based training strategy, wherein the training of a pBNN is formulated as simulating a Feynman--Kac model. We show that our proposed training scheme outperforms the state of the art in terms of predictive performance.
• Score: 1.6474447977095783
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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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