Variational Auto-encoded Deep Gaussian Processes

Zhenwen Dai; Andreas Damianou; Javier González; Neil Lawrence

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arXiv · 1511.06455

Variational Auto-encoded Deep Gaussian Processes

Zhenwen Dai·Andreas Damianou·Javier González·Neil Lawrence

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Abstract

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. We show the efficacy of the method on a variety of challenges including deep unsupervised learning and deep Bayesian optimization.

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Zhenwen Dai, Andreas Damianou, Javier González, Neil Lawrence. 2016-02-29. Variational Auto-encoded Deep Gaussian Processes. https://arxiv.org/abs/1511.06455

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