Deep Gaussian Processes for Large Datasets
Big data and Bayesian non parametric modelling constitute two of the most important foci of modern machine learning research. In this preliminary work we propose a neat solution for combining the aforementioned domains into a single principled framework based on Gaussian processes. Speficically, we invistigate algorithms for training deep generative models with hidden layers connected with non linear Gaussian process (GP) mappings. Building on recent developments on (stochastic) variational approximations, the models are fitted on massive data and the hidden variables are marginalised out in a Bayesian manner to allow for efficient propagation of the uncertainty throughout the network of variables.