Improved Depth Estimation of Bayesian Neural Networks

• URL: http://arxiv.org/abs/2410.10395v2
• Date: Tue, 15 Oct 2024 07:01:02 GMT
• Title: Improved Depth Estimation of Bayesian Neural Networks
• Authors: Bart van Erp, Bert de Vries,
• Abstract summary: We propose a discrete truncated normal distribution over the network depth to learn its mean and variance. Our method improves test accuracy on the spiral data set and reduces the variance in posterior depth estimates.
• Score: 2.5782420501870296
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: 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.

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