Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Infinitely Wide Graph Convolutional Networks: Semi-supervised Learning
  via Gaussian Processes   Graph convolutional neural networks~(GCNs) have recently demonstrated
promising results on graph-based semi-supervised classification, but little
work has been done to explore their theoretical properties. Recently, several
deep neural networks, e.g., fully connected and convolutional neural networks,
with infinite hidden units have been proved to be equivalent to Gaussian
processes~(GPs). To exploit both the powerful representational capacity of GCNs
and the great expressive power of GPs, we investigate similar properties of
infinitely wide GCNs. More specifically, we propose a GP regression model via
GCNs~(GPGC) for graph-based semi-supervised learning. In the process, we
formulate the kernel matrix computation of GPGC in an iterative analytical
form. Finally, we derive a conditional distribution for the labels of
unobserved nodes based on the graph structure, labels for the observed nodes,
and the feature matrix of all the nodes. We conduct extensive experiments to
evaluate the semi-supervised classification performance of GPGC and demonstrate
that it outperforms other state-of-the-art methods by a clear margin on all the
datasets while being efficient.

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The categories of this paper are: q-bio
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The categories of this paper are: dg-ga
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The categories of this paper are: math-ph
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The categories of this paper are: math
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The categories of this paper are: physics
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The categories of this paper are: quant-ph
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The categories of this paper are: patt-sol
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The categories of this paper are: hep-lat
37
The categories of this paper are: supr-con
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The categories of this paper are: comp-gas
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The categories of this paper are: bayes-an
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The categories of this paper are: solv-int
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The categories of this paper are: eess
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The categories of this paper are: acc-phys
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The categories of this paper are: cmp-lg
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The categories of this paper are: cond-mat