Improved Depth Estimation of Bayesian Neural Networks
Fuente:
arXiv
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| Autores principales: | , |
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| Formato: | Preprint |
| Publicado: |
2024
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| Materias: | |
| Acceso en línea: | |
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| _version_ | 1866917803670372352 |
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| author | van Erp, Bart de Vries, Bert |
| author_facet | van Erp, Bart de Vries, Bert |
| contents | This paper proposes improvements over earlier work by Nazareth and Blei (2022) for estimating the depth of Bayesian neural networks. Here, we propose a discrete truncated normal distribution over the network depth to independently learn its mean and variance. Posterior distributions are inferred by minimizing the variational free energy, which balances the model complexity and accuracy. Our method improves test accuracy on the spiral data set and reduces the variance in posterior depth estimates. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2410_10395 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Improved Depth Estimation of Bayesian Neural Networks van Erp, Bart de Vries, Bert Machine Learning This paper proposes improvements over earlier work by Nazareth and Blei (2022) for estimating the depth of Bayesian neural networks. Here, we propose a discrete truncated normal distribution over the network depth to independently learn its mean and variance. Posterior distributions are inferred by minimizing the variational free energy, which balances the model complexity and accuracy. Our method improves test accuracy on the spiral data set and reduces the variance in posterior depth estimates. |
| title | Improved Depth Estimation of Bayesian Neural Networks |
| topic | Machine Learning |
| url | https://arxiv.org/abs/2410.10395 |