Variational Auto-encoded Deep Gaussian Processes

Understand

We develop a scalable deep non-parametric generative model by augmenting deep Gaussian processes with a recognition model.

• Inference is performed in a novel scalable variational framework where the variational posterior distributions are reparametrized through a multilayer perceptron.
• The key aspect of this reformulation is that it prevents the proliferation of variational parameters which otherwise grow linearly in proportion to the sample size.
• We derive a new formulation of the variational lower bound that allows us to distribute most of the computation in a way that enables to handle datasets of the size of mainstream deep learning tasks.