Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Scalable Training of Inference Networks for Gaussian-Process Models   Inference in Gaussian process (GP) models is computationally challenging for
large data, and often difficult to approximate with a small number of inducing
points. We explore an alternative approximation that employs stochastic
inference networks for a flexible inference. Unfortunately, for such networks,
minibatch training is difficult to be able to learn meaningful correlations
over function outputs for a large dataset. We propose an algorithm that enables
such training by tracking a stochastic, functional mirror-descent algorithm. At
each iteration, this only requires considering a finite number of input
locations, resulting in a scalable and easy-to-implement algorithm. Empirical
results show comparable and, sometimes, superior performance to existing sparse
variational GP methods.

30
The categories of this paper are: q-alg
19
The categories of this paper are: eess
11
The categories of this paper are: hep-lat
17
The categories of this paper are: q-fin
26
The categories of this paper are: bayes-an
36
The categories of this paper are: cmp-lg
35
The categories of this paper are: mtrl-th
10
The categories of this paper are: q-bio
20
The categories of this paper are: acc-phys
37
The categories of this paper are: supr-con
2
The categories of this paper are: math
21
The categories of this paper are: adap-org
25
The categories of this paper are: patt-sol
12
The categories of this paper are: nucl-th
32
The categories of this paper are: atom-ph
27
The categories of this paper are: comp-gas