# Efficient variational Bayesian neural network ensembles for outlier detection

arXiv: https://arxiv.org/abs/1703.06749 · HTML: https://mlsanity.com/paper/1703.06749/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 20 Mar 2017.

## Built on by (most cited first, 3 of 3)

- [ZooD: Exploiting Model Zoo for Out-of-Distribution Generalization](https://mlsanity.com/paper/2210.09236/) (2022)
- [An Informative Path Planning Framework for Active Learning in UAV-based Semantic Mapping](https://mlsanity.com/paper/2302.03347/) (2023)
- [Bayesian Estimate of Mean Proper Scores for Diversity-Enhanced Active Learning](https://mlsanity.com/paper/2312.10116/) (2023)

## Abstract (arXiv)

In this work we perform outlier detection using ensembles of neural networks obtained by variational approximation of the posterior in a Bayesian neural network setting. The variational parameters are obtained by sampling from the true posterior by gradient descent. We show our outlier detection results are comparable to those obtained using other efficient ensembling methods.