Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: DNNs as Layers of Cooperating Classifiers   A robust theoretical framework that can describe and predict the
generalization ability of deep neural networks (DNNs) in general circumstances
remains elusive. Classical attempts have produced complexity metrics that rely
heavily on global measures of compactness and capacity with little
investigation into the effects of sub-component collaboration. We demonstrate
intriguing regularities in the activation patterns of the hidden nodes within
fully-connected feedforward networks. By tracing the origin of these patterns,
we show how such networks can be viewed as the combination of two information
processing systems: one continuous and one discrete. We describe how these two
systems arise naturally from the gradient-based optimization process, and
demonstrate the classification ability of the two systems, individually and in
collaboration. This perspective on DNN classification offers a novel way to
think about generalization, in which different subsets of the training data are
used to train distinct classifiers; those classifiers are then combined to
perform the classification task, and their consistency is crucial for accurate
classification.

4
The categories of this paper are: cond-mat
3
The categories of this paper are: physics
12
The categories of this paper are: nucl-th
26
The categories of this paper are: bayes-an
6
The categories of this paper are: astro-ph
9
The categories of this paper are: nlin
20
The categories of this paper are: acc-phys
10
The categories of this paper are: q-bio
31
The categories of this paper are: ao-sci
5
The categories of this paper are: gr-qc
8
The categories of this paper are: hep-ex
37
The categories of this paper are: supr-con
28
The categories of this paper are: alg-geom
30
The categories of this paper are: q-alg
15
The categories of this paper are: nucl-ex
24
The categories of this paper are: dg-ga