Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Radius-margin bounds for deep neural networks   Explaining the unreasonable effectiveness of deep learning has eluded
researchers around the globe. Various authors have described multiple metrics
to evaluate the capacity of deep architectures. In this paper, we allude to the
radius margin bounds described for a support vector machine (SVM) with hinge
loss, apply the same to the deep feed-forward architectures and derive the
Vapnik-Chervonenkis (VC) bounds which are different from the earlier bounds
proposed in terms of number of weights of the network. In doing so, we also
relate the effectiveness of techniques like Dropout and Dropconnect in bringing
down the capacity of the network. Finally, we describe the effect of maximizing
the input as well as the output margin to achieve an input noise-robust deep
architecture.

32
The categories of this paper are: atom-ph
27
The categories of this paper are: comp-gas
25
The categories of this paper are: patt-sol
17
The categories of this paper are: q-fin
15
The categories of this paper are: nucl-ex
36
The categories of this paper are: cmp-lg
7
The categories of this paper are: hep-th
8
The categories of this paper are: hep-ex
14
The categories of this paper are: math-ph
9
The categories of this paper are: nlin
26
The categories of this paper are: bayes-an
3
The categories of this paper are: physics
19
The categories of this paper are: eess
10
The categories of this paper are: q-bio
30
The categories of this paper are: q-alg
4
The categories of this paper are: cond-mat