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 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.

翻译：贝叶斯神经网络通过考虑权重的分布并为每个输入采样不同的模型，提供了一种估计神经网络不确定性的工具。本文提出了一种神经网络不确定性估计方法，该方法不依赖权重分布，而是从相应的高斯分布中采样每层的输出，该分布由均值和方差子层的预测进行参数化。在不确定性质量估计实验中，我们证明所提出的方法相比其他单箱贝叶斯模型平均方法（如蒙特卡洛Dropout或反向传播贝叶斯方法），能够实现更优的不确定性质量。