论文标题
蒙特卡洛辍学的定性分析
Qualitative Analysis of Monte Carlo Dropout
论文作者
论文摘要
在本报告中，我们提出了用于测量神经网络（NN）模型模型不确定性的蒙特卡洛（MC）辍学方法的定性分析。我们首先考虑了NNS中不确定性的来源，并简要审查了贝叶斯神经网络（BNN），这是解决NNS不确定性的贝叶斯方法。在提出了MC辍学的数学表述之后，我们继续提出在典型NN模型中使用MC辍学的潜在收益和相关成本，这是我们实验的结果。
In this report, we present qualitative analysis of Monte Carlo (MC) dropout method for measuring model uncertainty in neural network (NN) models. We first consider the sources of uncertainty in NNs, and briefly review Bayesian Neural Networks (BNN), the group of Bayesian approaches to tackle uncertainties in NNs. After presenting mathematical formulation of MC dropout, we proceed to suggesting potential benefits and associated costs for using MC dropout in typical NN models, with the results from our experiments.
