Stochastic Backpropagation and Approximate Inference in Deep Generative Models (2014)
Paper Danilo Jimenez Rezende, Shakir Mohamed, Daan Wierstra, Stochastic Backpropagation and Approximate Inference in Deep Generative Models, arXiv:1401.4082 stat.ML
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.