# Building Bayesian Neural Networks with Blocks: On Structure, Interpretability and Uncertainty

arXiv: https://arxiv.org/abs/1806.03563 · HTML: https://mlsanity.com/paper/1806.03563/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 10 Jun 2018.

## Built on by (most cited first, 2 of 2)

- [Locality defeats the curse of dimensionality in convolutional teacher-student scenarios](https://mlsanity.com/paper/2106.08619/) (2021)
- [What Can Be Learnt With Wide Convolutional Neural Networks?](https://mlsanity.com/paper/2208.01003/) (2022)

## Abstract (arXiv)

We provide simple schemes to build Bayesian Neural Networks (BNNs), block by block, inspired by a recent idea of computation skeletons. We show how by adjusting the types of blocks that are used within the computation skeleton, we can identify interesting relationships with Deep Gaussian Processes (DGPs), deep kernel learning (DKL), random features type approximation and other topics. We give strategies to approximate the posterior via doubly stochastic variational inference for such models which yield uncertainty estimates. We give a detailed theoretical analysis and point out extensions that may be of independent interest. As a special case, we instantiate our procedure to define a Bayesian em additive Neural network -- a promising strategy to identify statistical interactions and has direct benefits for obtaining interpretable models.