Stochastic Backpropagation and Approximate Inference in Deep Generative\n Models

We marry ideas from deep neural networks and approximate Bayesian inference\nto derive a generalised class of deep, directed generative models, endowed with\na new algorithm for scalable inference and learning. Our algorithm introduces a\nrecognition model to represent approximate posterior distributions, and that\nacts as a stochastic encoder of the data. We develop stochastic\nback-propagation -- rules for back-propagation through stochastic variables --\nand use this to develop an algorithm that allows for joint optimisation of the\nparameters of both the generative and recognition model. We demonstrate on\nseveral real-world data sets that the model generates realistic samples,\nprovides accurate…