  Gaussian Processes (GPs) are Bayesian models that provide uncertainty
estimates associated to the predictions made. They are also very flexible due
to their non-parametric nature. Nevertheless, GPs suffer from poor scalability
as the number of training instances N increases. More precisely, they have a
cubic cost with respect to $N$. To overcome this problem, sparse GP
approximations are often used, where a set of $M \ll N$ inducing points is
introduced during training. The location of the inducing points is learned by
considering them as parameters of an approximate posterior distribution $q$.
Sparse GPs, combined with variational inference for inferring $q$, reduce the
training cost of GPs to $\mathcal{O}(M^3)$. Critically, the inducing points
determine the flexibility of the model and they are often located in regions of
the input space where the latent function changes. A limitation is, however,
that for some learning tasks a large number of inducing points may be required
to obtain a good prediction performance. To address this limitation, we propose
here to amortize the computation of the inducing points locations, as well as
the parameters of the variational posterior approximation q. For this, we use a
neural network that receives the observed data as an input and outputs the
inducing points locations and the parameters of $q$. We evaluate our method in
several experiments, showing that it performs similar or better than other
state-of-the-art sparse variational GP approaches. However, with our method the
number of inducing points is reduced drastically due to their dependency on the
input data. This makes our method scale to larger datasets and have faster
training and prediction times.
