Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Learning Graph Neural Networks with Noisy Labels   We study the robustness to symmetric label noise of GNNs training procedures.
By combining the nonlinear neural message-passing models (e.g. Graph
Isomorphism Networks, GraphSAGE, etc.) with loss correction methods, we present
a noise-tolerant approach for the graph classification task. Our experiments
show that test accuracy can be improved under the artificial symmetric noisy
setting.

36
The categories of this paper are: cmp-lg
7
The categories of this paper are: hep-th
34
The categories of this paper are: chem-ph
11
The categories of this paper are: hep-lat
35
The categories of this paper are: mtrl-th
9
The categories of this paper are: nlin
15
The categories of this paper are: nucl-ex
31
The categories of this paper are: ao-sci
32
The categories of this paper are: atom-ph
23
The categories of this paper are: solv-int
33
The categories of this paper are: plasm-ph
2
The categories of this paper are: math
27
The categories of this paper are: comp-gas
13
The categories of this paper are: quant-ph
12
The categories of this paper are: nucl-th
3
The categories of this paper are: physics