Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Variational Auto-encoded Deep Gaussian Processes   We develop a scalable deep non-parametric generative model by augmenting deep
Gaussian processes with a recognition model. Inference is performed in a novel
scalable variational framework where the variational posterior distributions
are reparametrized through a multilayer perceptron. The key aspect of this
reformulation is that it prevents the proliferation of variational parameters
which otherwise grow linearly in proportion to the sample size. We derive a new
formulation of the variational lower bound that allows us to distribute most of
the computation in a way that enables to handle datasets of the size of
mainstream deep learning tasks. We show the efficacy of the method on a variety
of challenges including deep unsupervised learning and deep Bayesian
optimization.

10
The categories of this paper are: q-bio
11
The categories of this paper are: hep-lat
19
The categories of this paper are: eess
33
The categories of this paper are: plasm-ph
35
The categories of this paper are: mtrl-th
4
The categories of this paper are: cond-mat
7
The categories of this paper are: hep-th
3
The categories of this paper are: physics
32
The categories of this paper are: atom-ph
34
The categories of this paper are: chem-ph
29
The categories of this paper are: funct-an
9
The categories of this paper are: nlin
24
The categories of this paper are: dg-ga
13
The categories of this paper are: quant-ph
20
The categories of this paper are: acc-phys
27
The categories of this paper are: comp-gas