On Feynman--Kac training of partial Bayesian neural networks
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arXiv
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| Main Authors: | , , , |
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| Format: | Preprint |
| Published: |
2023
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| Subjects: | |
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| _version_ | 1866909120739672064 |
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| author | Zhao, Zheng Mair, Sebastian Schön, Thomas B. Sjölund, Jens |
| author_facet | Zhao, Zheng Mair, Sebastian Schön, Thomas B. Sjölund, Jens |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2310_19608 |
| institution | arXiv |
| publishDate | 2023 |
| record_format | arxiv |
| spellingShingle | On Feynman--Kac training of partial Bayesian neural networks Zhao, Zheng Mair, Sebastian Schön, Thomas B. Sjölund, Jens Machine Learning 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. |
| title | On Feynman--Kac training of partial Bayesian neural networks |
| topic | Machine Learning |
| url | https://arxiv.org/abs/2310.19608 |