Variational Neural Networks

• URL: http://arxiv.org/abs/2207.01524v1
• Date: Mon, 4 Jul 2022 15:41:02 GMT
• Title: Variational Neural Networks
• Authors: Illia Oleksiienko, Dat Thanh Tran and Alexandros Iosifidis
• Abstract summary: We propose a method for uncertainty estimation in neural networks called Variational Neural Network (VNN) VNN generates parameters for the output distribution of a layer by transforming its inputs with learnable sub-layers. In uncertainty quality estimation experiments, we show that VNNs achieve better uncertainty quality than Monte Carlo Dropout or Bayes By Backpropagation methods.
• Score: 88.24021148516319
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Bayesian Neural Networks (BNNs) 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 called Variational Neural Network that, instead of considering a distribution over weights, generates parameters for the output distribution of a layer by transforming its inputs with learnable sub-layers. In uncertainty quality estimation experiments, we show that VNNs achieve better uncertainty quality than Monte Carlo Dropout or Bayes By Backpropagation methods.

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