论文标题
变分神经网络
Variational Neural Networks
论文作者
论文摘要
贝叶斯神经网络（BNN）提供了一种工具来估计神经网络的不确定性，通过考虑为每个输入的权重和采样不同模型的分布。在本文中，我们提出了一种神经网络中不确定性估计的方法，该方法并没有考虑重量分布，而是从相应的高斯分布中的样本输出，这是由均值和方差子层的预测参数化的。在不确定性质量估计实验中，我们表明所提出的方法比其他单箱贝叶斯模型平均方法（例如蒙特卡洛辍学方法或贝叶斯）获得了更好的不确定性质量。
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.
