  Deep Gaussian Processes (DGPs) are multi-layer, flexible extensions of
Gaussian processes but their training remains challenging. Sparse
approximations simplify the training but often require optimization over a
large number of inducing inputs and their locations across layers. In this
paper, we simplify the training by setting the locations to a fixed subset of
data and sampling the inducing inputs from a variational distribution. This
reduces the trainable parameters and computation cost without significant
performance degradations, as demonstrated by our empirical results on
regression problems. Our modifications simplify and stabilize DGP training
while making it amenable to sampling schemes for setting the inducing inputs.
