Variational Neural Networks

Illia Oleksiienko, Dat Thanh Tran, Alexandros Iosifidis

Abstract: Bayesian Neural Networks provide a tool to estimate the uncertainty of a neural network by considering a distribution over weights and sampling different models for each input. In this paper, we propose a method for uncertainty estimation in neural networks which, instead of considering a distribution over weights, samples outputs of each layer from a corresponding Gaussian distribution, parametrized by the predictions of mean and variance sub-layers. In uncertainty quality estimation experiments, we show that the proposed method achieves better uncertainty quality than other single-bin Bayesian Model Averaging methods, such as Monte Carlo Dropout or Bayes By Backpropagation methods.

External IDs:dblp:conf/inns-dlia/OleksiienkoTI23