Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Deep Data Density Estimation through Donsker-Varadhan Representation   Estimating the data density is one of the challenging problems in deep
learning. In this paper, we present a simple yet effective method for
estimating the data density using a deep neural network and the
Donsker-Varadhan variational lower bound on the KL divergence. We show that the
optimal critic function associated with the Donsker-Varadhan representation on
the KL divergence between the data and the uniform distribution can estimate
the data density. We also present the deep neural network-based modeling and
its stochastic learning. The experimental results and possible applications of
the proposed method demonstrate that it is competitive with the previous
methods and has a lot of possibilities in applied to various applications.

11
The categories of this paper are: hep-lat
27
The categories of this paper are: comp-gas
25
The categories of this paper are: patt-sol
34
The categories of this paper are: chem-ph
16
The categories of this paper are: stat
20
The categories of this paper are: acc-phys
21
The categories of this paper are: adap-org
31
The categories of this paper are: ao-sci
33
The categories of this paper are: plasm-ph
35
The categories of this paper are: mtrl-th
9
The categories of this paper are: nlin
7
The categories of this paper are: hep-th
23
The categories of this paper are: solv-int
36
The categories of this paper are: cmp-lg
28
The categories of this paper are: alg-geom
22
The categories of this paper are: chao-dyn