This work introduces a novel probabilistic deep learning technique called
deep Gaussian mixture ensembles (DGMEs), which enables accurate quantification
of both epistemic and aleatoric uncertainty. By assuming the data generating
process follows that of a Gaussian mixture, DGMEs are capable of approximating
complex probability distributions, such as heavy-tailed or multimodal
distributions. Our contributions include the derivation of an
expectation-maximization (EM) algorithm used for learning the model parameters,
which results in an upper-bound on the log-likelihood of training data over
that of standard deep ensembles. Additionally, the proposed EM training
procedure allows for learning of mixture weights, which is not commonly done in
ensembles. Our experimental results demonstrate that DGMEs outperform
state-of-the-art uncertainty quantifying deep learning models in handling
complex predictive densities.

本文介绍了一种新颖的概率深度学习技术 —— 深高斯混合集成（DGMEs），它能够准确量化认知不确定性和 Aleatoric 不确定性，通过假设数据生成过程遵循高斯混合的模型，DGMEs 能够逼近复杂的概率分布。实验结果表明，DGMEs 在处理复杂的预测密度方面比现有的不确定性量化深度学习模型表现更好。