Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Multi network InfoMax: A pre-training method involving graph
  convolutional networks   Discovering distinct features and their relations from data can help us
uncover valuable knowledge crucial for various tasks, e.g., classification. In
neuroimaging, these features could help to understand, classify, and possibly
prevent brain disorders. Model introspection of highly performant
overparameterized deep learning (DL) models could help find these features and
relations. However, to achieve high-performance level DL models require
numerous labeled training samples ($n$) rarely available in many fields. This
paper presents a pre-training method involving graph convolutional/neural
networks (GCNs/GNNs), based on maximizing mutual information between two
high-level embeddings of an input sample. Many of the recently proposed
pre-training methods pre-train one of many possible networks of an
architecture. Since almost every DL model is an ensemble of multiple networks,
we take our high-level embeddings from two different networks of a model --a
convolutional and a graph network--. The learned high-level graph latent
representations help increase performance for downstream graph classification
tasks and bypass the need for a high number of labeled data samples. We apply
our method to a neuroimaging dataset for classifying subjects into healthy
control (HC) and schizophrenia (SZ) groups. Our experiments show that the
pre-trained model significantly outperforms the non-pre-trained model and
requires $50\%$ less data for similar performance.

21
The categories of this paper are: adap-org
15
The categories of this paper are: nucl-ex
10
The categories of this paper are: q-bio
14
The categories of this paper are: math-ph
2
The categories of this paper are: math
16
The categories of this paper are: stat
28
The categories of this paper are: alg-geom
6
The categories of this paper are: astro-ph
35
The categories of this paper are: mtrl-th
36
The categories of this paper are: cmp-lg
29
The categories of this paper are: funct-an
25
The categories of this paper are: patt-sol
32
The categories of this paper are: atom-ph
17
The categories of this paper are: q-fin
30
The categories of this paper are: q-alg
22
The categories of this paper are: chao-dyn