Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Compressed Learning: A Deep Neural Network Approach   Compressed Learning (CL) is a joint signal processing and machine learning
framework for inference from a signal, using a small number of measurements
obtained by linear projections of the signal. In this paper we present an
end-to-end deep learning approach for CL, in which a network composed of
fully-connected layers followed by convolutional layers perform the linear
sensing and non-linear inference stages. During the training phase, the sensing
matrix and the non-linear inference operator are jointly optimized, and the
proposed approach outperforms state-of-the-art for the task of image
classification. For example, at a sensing rate of 1% (only 8 measurements of 28
X 28 pixels images), the classification error for the MNIST handwritten digits
dataset is 6.46% compared to 41.06% with state-of-the-art.

7
The categories of this paper are: hep-th
24
The categories of this paper are: dg-ga
28
The categories of this paper are: alg-geom
22
The categories of this paper are: chao-dyn
20
The categories of this paper are: acc-phys
13
The categories of this paper are: quant-ph
5
The categories of this paper are: gr-qc
33
The categories of this paper are: plasm-ph
29
The categories of this paper are: funct-an
21
The categories of this paper are: adap-org
23
The categories of this paper are: solv-int
14
The categories of this paper are: math-ph
3
The categories of this paper are: physics
12
The categories of this paper are: nucl-th
27
The categories of this paper are: comp-gas
36
The categories of this paper are: cmp-lg