Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Non-parametric Bayesian Learning with Deep Learning Structure and Its
  Applications in Wireless Networks   In this paper, we present an infinite hierarchical non-parametric Bayesian
model to extract the hidden factors over observed data, where the number of
hidden factors for each layer is unknown and can be potentially infinite.
Moreover, the number of layers can also be infinite. We construct the model
structure that allows continuous values for the hidden factors and weights,
which makes the model suitable for various applications. We use the
Metropolis-Hastings method to infer the model structure. Then the performance
of the algorithm is evaluated by the experiments. Simulation results show that
the model fits the underlying structure of simulated data.

37
The categories of this paper are: supr-con
17
The categories of this paper are: q-fin
26
The categories of this paper are: bayes-an
2
The categories of this paper are: math
5
The categories of this paper are: gr-qc
34
The categories of this paper are: chem-ph
10
The categories of this paper are: q-bio
30
The categories of this paper are: q-alg
23
The categories of this paper are: solv-int
13
The categories of this paper are: quant-ph
21
The categories of this paper are: adap-org
28
The categories of this paper are: alg-geom
14
The categories of this paper are: math-ph
15
The categories of this paper are: nucl-ex
9
The categories of this paper are: nlin
11
The categories of this paper are: hep-lat