Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Deep Energy Estimator Networks   Density estimation is a fundamental problem in statistical learning. This
problem is especially challenging for complex high-dimensional data due to the
curse of dimensionality. A promising solution to this problem is given here in
an inference-free hierarchical framework that is built on score matching. We
revisit the Bayesian interpretation of the score function and the Parzen score
matching, and construct a multilayer perceptron with a scalable objective for
learning the energy (i.e. the unnormalized log-density), which is then
optimized with stochastic gradient descent. In addition, the resulting deep
energy estimator network (DEEN) is designed as products of experts. We present
the utility of DEEN in learning the energy, the score function, and in
single-step denoising experiments for synthetic and high-dimensional data. We
also diagnose stability problems in the direct estimation of the score function
that had been observed for denoising autoencoders.

20
The categories of this paper are: acc-phys
25
The categories of this paper are: patt-sol
34
The categories of this paper are: chem-ph
10
The categories of this paper are: q-bio
27
The categories of this paper are: comp-gas
3
The categories of this paper are: physics
21
The categories of this paper are: adap-org
17
The categories of this paper are: q-fin
4
The categories of this paper are: cond-mat
31
The categories of this paper are: ao-sci
8
The categories of this paper are: hep-ex
30
The categories of this paper are: q-alg
7
The categories of this paper are: hep-th
14
The categories of this paper are: math-ph
37
The categories of this paper are: supr-con
32
The categories of this paper are: atom-ph