GelGT proposes collaborative sampling and Gaussian attention on subgraphs to model long-range structural, semantic, and temporal dependencies in relational graphs, reporting up to 13.8% gains on downstream tasks.
Transformers with stochastic competition for tabular data modelling
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Gaussian Relational Graph Transformer
GelGT proposes collaborative sampling and Gaussian attention on subgraphs to model long-range structural, semantic, and temporal dependencies in relational graphs, reporting up to 13.8% gains on downstream tasks.