Efficient Variational Bayesian Neural Network Ensembles for Outlier Detection
Abstract
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.
Cite
Text
Pawlowski et al. "Efficient Variational Bayesian Neural Network Ensembles for Outlier Detection." International Conference on Learning Representations, 2017.Markdown
[Pawlowski et al. "Efficient Variational Bayesian Neural Network Ensembles for Outlier Detection." International Conference on Learning Representations, 2017.](https://mlanthology.org/iclr/2017/pawlowski2017iclr-efficient/)BibTeX
@inproceedings{pawlowski2017iclr-efficient,
title = {{Efficient Variational Bayesian Neural Network Ensembles for Outlier Detection}},
author = {Pawlowski, Nick and Jaques, Miguel and Glocker, Ben},
booktitle = {International Conference on Learning Representations},
year = {2017},
url = {https://mlanthology.org/iclr/2017/pawlowski2017iclr-efficient/}
}