Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Deep Neural Networks with Random Gaussian Weights: A Universal
  Classification Strategy?   Three important properties of a classification machinery are: (i) the system
preserves the core information of the input data; (ii) the training examples
convey information about unseen data; and (iii) the system is able to treat
differently points from different classes. In this work we show that these
fundamental properties are satisfied by the architecture of deep neural
networks. We formally prove that these networks with random Gaussian weights
perform a distance-preserving embedding of the data, with a special treatment
for in-class and out-of-class data. Similar points at the input of the network
are likely to have a similar output. The theoretical analysis of deep networks
here presented exploits tools used in the compressed sensing and dictionary
learning literature, thereby making a formal connection between these important
topics. The derived results allow drawing conclusions on the metric learning
properties of the network and their relation to its structure, as well as
providing bounds on the required size of the training set such that the
training examples would represent faithfully the unseen data. The results are
validated with state-of-the-art trained networks.

22
The categories of this paper are: chao-dyn
31
The categories of this paper are: ao-sci
0
The categories of this paper are: hep-ph
14
The categories of this paper are: math-ph
13
The categories of this paper are: quant-ph
33
The categories of this paper are: plasm-ph
23
The categories of this paper are: solv-int
5
The categories of this paper are: gr-qc
9
The categories of this paper are: nlin
32
The categories of this paper are: atom-ph
10
The categories of this paper are: q-bio
35
The categories of this paper are: mtrl-th
3
The categories of this paper are: physics
18
The categories of this paper are: econ
21
The categories of this paper are: adap-org
7
The categories of this paper are: hep-th