Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Inferring Sparsity: Compressed Sensing using Generalized Restricted
  Boltzmann Machines   In this work, we consider compressed sensing reconstruction from $M$
measurements of $K$-sparse structured signals which do not possess a writable
correlation model. Assuming that a generative statistical model, such as a
Boltzmann machine, can be trained in an unsupervised manner on example signals,
we demonstrate how this signal model can be used within a Bayesian framework of
signal reconstruction. By deriving a message-passing inference for general
distribution restricted Boltzmann machines, we are able to integrate these
inferred signal models into approximate message passing for compressed sensing
reconstruction. Finally, we show for the MNIST dataset that this approach can
be very effective, even for $M < K$.

9
The categories of this paper are: nlin
7
The categories of this paper are: hep-th
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
29
The categories of this paper are: funct-an
35
The categories of this paper are: mtrl-th
22
The categories of this paper are: chao-dyn
11
The categories of this paper are: hep-lat
23
The categories of this paper are: solv-int
18
The categories of this paper are: econ
31
The categories of this paper are: ao-sci
10
The categories of this paper are: q-bio
8
The categories of this paper are: hep-ex
25
The categories of this paper are: patt-sol
6
The categories of this paper are: astro-ph