Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Implicit Density Estimation by Local Moment Matching to Sample from
  Auto-Encoders   Recent work suggests that some auto-encoder variants do a good job of
capturing the local manifold structure of the unknown data generating density.
This paper contributes to the mathematical understanding of this phenomenon and
helps define better justified sampling algorithms for deep learning based on
auto-encoder variants. We consider an MCMC where each step samples from a
Gaussian whose mean and covariance matrix depend on the previous state, defines
through its asymptotic distribution a target density. First, we show that good
choices (in the sense of consistency) for these mean and covariance functions
are the local expected value and local covariance under that target density.
Then we show that an auto-encoder with a contractive penalty captures
estimators of these local moments in its reconstruction function and its
Jacobian. A contribution of this work is thus a novel alternative to
maximum-likelihood density estimation, which we call local moment matching. It
also justifies a recently proposed sampling algorithm for the Contractive
Auto-Encoder and extends it to the Denoising Auto-Encoder.

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The categories of this paper are: hep-ph
29
The categories of this paper are: funct-an
8
The categories of this paper are: hep-ex
28
The categories of this paper are: alg-geom
19
The categories of this paper are: eess
25
The categories of this paper are: patt-sol
15
The categories of this paper are: nucl-ex
37
The categories of this paper are: supr-con
26
The categories of this paper are: bayes-an
34
The categories of this paper are: chem-ph
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The categories of this paper are: cond-mat
35
The categories of this paper are: mtrl-th
5
The categories of this paper are: gr-qc
12
The categories of this paper are: nucl-th
3
The categories of this paper are: physics
11
The categories of this paper are: hep-lat