Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Understanding the Basis of Graph Convolutional Neural Networks via an
  Intuitive Matched Filtering Approach   Graph Convolutional Neural Networks (GCNN) are becoming a preferred model for
data processing on irregular domains, yet their analysis and principles of
operation are rarely examined due to the black box nature of NNs. To this end,
we revisit the operation of GCNNs and show that their convolution layers
effectively perform matched filtering of input data with the chosen patterns
(features). This allows us to provide a unifying account of GCNNs through a
matched filter perspective, whereby the nonlinear ReLU and max-pooling layers
are also discussed within the matched filtering framework. This is followed by
a step-by-step guide on information propagation and learning in GCNNs. It is
also shown that standard CNNs and fully connected NNs can be obtained as a
special case of GCNNs. A carefully chosen numerical example guides the reader
through the various steps of GCNN operation and learning both visually and
numerically.

11
The categories of this paper are: hep-lat
24
The categories of this paper are: dg-ga
9
The categories of this paper are: nlin
17
The categories of this paper are: q-fin
25
The categories of this paper are: patt-sol
6
The categories of this paper are: astro-ph
10
The categories of this paper are: q-bio
22
The categories of this paper are: chao-dyn
36
The categories of this paper are: cmp-lg
14
The categories of this paper are: math-ph
20
The categories of this paper are: acc-phys
3
The categories of this paper are: physics
16
The categories of this paper are: stat
15
The categories of this paper are: nucl-ex
35
The categories of this paper are: mtrl-th
13
The categories of this paper are: quant-ph