Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Training Deep Gaussian Processes using Stochastic Expectation
  Propagation and Probabilistic Backpropagation   Deep Gaussian processes (DGPs) are multi-layer hierarchical generalisations
of Gaussian processes (GPs) and are formally equivalent to neural networks with
multiple, infinitely wide hidden layers. DGPs are probabilistic and
non-parametric and as such are arguably more flexible, have a greater capacity
to generalise, and provide better calibrated uncertainty estimates than
alternative deep models. The focus of this paper is scalable approximate
Bayesian learning of these networks. The paper develops a novel and efficient
extension of probabilistic backpropagation, a state-of-the-art method for
training Bayesian neural networks, that can be used to train DGPs. The new
method leverages a recently proposed method for scaling Expectation
Propagation, called stochastic Expectation Propagation. The method is able to
automatically discover useful input warping, expansion or compression, and it
is therefore is a flexible form of Bayesian kernel design. We demonstrate the
success of the new method for supervised learning on several real-world
datasets, showing that it typically outperforms GP regression and is never much
worse.

8
The categories of this paper are: hep-ex
36
The categories of this paper are: cmp-lg
7
The categories of this paper are: hep-th
19
The categories of this paper are: eess
9
The categories of this paper are: nlin
32
The categories of this paper are: atom-ph
22
The categories of this paper are: chao-dyn
30
The categories of this paper are: q-alg
31
The categories of this paper are: ao-sci
18
The categories of this paper are: econ
12
The categories of this paper are: nucl-th
0
The categories of this paper are: hep-ph
33
The categories of this paper are: plasm-ph
10
The categories of this paper are: q-bio
20
The categories of this paper are: acc-phys
21
The categories of this paper are: adap-org