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Found 3 papers, 2 papers with code
Bias Amplification Enhances Minority Group Performance
Neural networks produced by standard training are known to suffer from poor accuracy on rare subgroups despite achieving high accuracy on average, due to the correlations between certain spurious features and labels.
Interpretable Sparsification of Brain Graphs: Better Practices and Effective Designs for Graph Neural Networks
Based on these insights, we propose a new model, Interpretable Graph Sparsification (IGS), which enhances graph classification performance by up to 5. 1% with 55. 0% fewer edges.
Size Generalization of Graph Neural Networks on Biological Data: Insights and Practices from the Spectral Perspective
Our empirical results reveal that our proposed size-insensitive attention strategy substantially enhances graph classification performance on large test graphs, which are 2-10 times larger than the training graphs, resulting in an improvement in F1 scores by up to 8%.
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