Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Learning Local Receptive Fields and their Weight Sharing Scheme on
  Graphs   We propose a simple and generic layer formulation that extends the properties
of convolutional layers to any domain that can be described by a graph. Namely,
we use the support of its adjacency matrix to design learnable weight sharing
filters able to exploit the underlying structure of signals in the same fashion
as for images. The proposed formulation makes it possible to learn the weights
of the filter as well as a scheme that controls how they are shared across the
graph. We perform validation experiments with image datasets and show that
these filters offer performances comparable with convolutional ones.

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The categories of this paper are: atom-ph
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The categories of this paper are: mtrl-th
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The categories of this paper are: q-alg
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The categories of this paper are: quant-ph
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The categories of this paper are: ao-sci
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The categories of this paper are: cond-mat
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The categories of this paper are: nucl-th
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The categories of this paper are: econ
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The categories of this paper are: math-ph
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The categories of this paper are: gr-qc
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The categories of this paper are: math
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The categories of this paper are: nlin
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The categories of this paper are: hep-lat
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The categories of this paper are: physics
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The categories of this paper are: hep-ex
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The categories of this paper are: q-fin