# Improved Depth Estimation of Bayesian Neural Networks

arXiv: https://arxiv.org/abs/2410.10395 · HTML: https://mlsanity.com/paper/2410.10395/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 6 of its 17 references that are in the map.
- First posted on arXiv 14 Oct 2024.

## Builds on (most cited first, 6 of 6)

- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [Weight Uncertainty in Neural Networks](https://mlsanity.com/paper/1505.05424/) (2015)
- [Variational Dropout and the Local Reparameterization Trick](https://mlsanity.com/paper/1506.02557/) (2015)
- [Depth Uncertainty in Neural Networks](https://mlsanity.com/paper/2006.08437/) (2020)
- [Variational Inference for Infinitely Deep Neural Networks](https://mlsanity.com/paper/2209.10091/) (2022)
- [Principled Pruning of Bayesian Neural Networks through Variational Free Energy Minimization](https://mlsanity.com/paper/2210.09134/) (2022)

## Abstract (arXiv)

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.