Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: An Energy-Based View of Graph Neural Networks   Graph neural networks are a popular variant of neural networks that work with
graph-structured data. In this work, we consider combining graph neural
networks with the energy-based view of Grathwohl et al. (2019) with the aim of
obtaining a more robust classifier. We successfully implement this framework by
proposing a novel method to ensure generation over features as well as the
adjacency matrix and evaluate our method against the standard graph
convolutional network (GCN) architecture (Kipf & Welling (2016)). Our approach
obtains comparable discriminative performance while improving robustness,
opening promising new directions for future research for energy-based graph
neural networks.

29
The categories of this paper are: funct-an
20
The categories of this paper are: acc-phys
22
The categories of this paper are: chao-dyn
23
The categories of this paper are: solv-int
14
The categories of this paper are: math-ph
8
The categories of this paper are: hep-ex
13
The categories of this paper are: quant-ph
18
The categories of this paper are: econ
27
The categories of this paper are: comp-gas
15
The categories of this paper are: nucl-ex
19
The categories of this paper are: eess
36
The categories of this paper are: cmp-lg
17
The categories of this paper are: q-fin
21
The categories of this paper are: adap-org
10
The categories of this paper are: q-bio
32
The categories of this paper are: atom-ph