  Deep Gaussian Processes (DGP) are hierarchical generalizations of Gaussian
Processes (GP) that have proven to work effectively on a multiple supervised
regression tasks. They combine the well calibrated uncertainty estimates of GPs
with the great flexibility of multilayer models. In DGPs, given the inputs, the
outputs of the layers are Gaussian distributions parameterized by their means
and covariances. These layers are realized as Sparse GPs where the training
data is approximated using a small set of pseudo points. In this work, we show
that the computational cost of DGPs can be reduced with no loss in performance
by using a separate, smaller set of pseudo points when calculating the
layerwise variance while using a larger set of pseudo points when calculating
the layerwise mean. This enabled us to train larger models that have lower cost
and better predictive performance.
