Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Matching Convolutional Neural Networks without Priors about Data   We propose an extension of Convolutional Neural Networks (CNNs) to
graph-structured data, including strided convolutions and data augmentation on
graphs.
  Our method matches the accuracy of state-of-the-art CNNs when applied on
images, without any prior about their 2D regular structure.
  On fMRI data, we obtain a significant gain in accuracy compared with existing
graph-based alternatives.

28
The categories of this paper are: alg-geom
7
The categories of this paper are: hep-th
3
The categories of this paper are: physics
22
The categories of this paper are: chao-dyn
37
The categories of this paper are: supr-con
29
The categories of this paper are: funct-an
26
The categories of this paper are: bayes-an
35
The categories of this paper are: mtrl-th
25
The categories of this paper are: patt-sol
33
The categories of this paper are: plasm-ph
36
The categories of this paper are: cmp-lg
8
The categories of this paper are: hep-ex
31
The categories of this paper are: ao-sci
24
The categories of this paper are: dg-ga
30
The categories of this paper are: q-alg
10
The categories of this paper are: q-bio