We marry ideas from deep neural networks and approximate Bayesian inference to derive a generalised class of deep, directed generative models, endowed with a new algorithm for scalable inference and learning. Our algorithm introduces a recognition model to represent approximate posterior distributions, and that acts as a stochastic encoder of the data. We develop stochastic back-propagation -- rules for back-propagation through stochastic variables -- and use this to develop an algorithm that allows for joint optimisation of the parameters of both the generative and recognition model. We demonstrate on several real-world data sets that the model generates realistic samples, provides accurate imputations of missing data and is a useful tool for high-dimensional data visualisation.

本文介绍了一种利用深度神经网络和近似贝叶斯推理相结合的广义深度生成模型，并引入了用于表示近似后验分布的识别模型，并利用随机反向传播来开发算法，实现生成和识别模型参数的联合优化，最终将模型应用于实际数据集，生成更真实的分布、准确地恢复缺失数据，并在高维数据的可视化上发挥了重要作用。

深度生成模型中的随机反向传播和近似推理