Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: A Deep Learning Approach to Structured Signal Recovery   In this paper, we develop a new framework for sensing and recovering
structured signals. In contrast to compressive sensing (CS) systems that employ
linear measurements, sparse representations, and computationally complex
convex/greedy algorithms, we introduce a deep learning framework that supports
both linear and mildly nonlinear measurements, that learns a structured
representation from training data, and that efficiently computes a signal
estimate. In particular, we apply a stacked denoising autoencoder (SDA), as an
unsupervised feature learner. SDA enables us to capture statistical
dependencies between the different elements of certain signals and improve
signal recovery performance as compared to the CS approach.

15
The categories of this paper are: nucl-ex
35
The categories of this paper are: mtrl-th
33
The categories of this paper are: plasm-ph
10
The categories of this paper are: q-bio
29
The categories of this paper are: funct-an
22
The categories of this paper are: chao-dyn
21
The categories of this paper are: adap-org
5
The categories of this paper are: gr-qc
14
The categories of this paper are: math-ph
18
The categories of this paper are: econ
20
The categories of this paper are: acc-phys
7
The categories of this paper are: hep-th
12
The categories of this paper are: nucl-th
19
The categories of this paper are: eess
6
The categories of this paper are: astro-ph
32
The categories of this paper are: atom-ph