A Temporally Augmented Graph Attention Network for Affordance Classification
Graph attention networks (GATs) provide one of the best frameworks for learning node representations in relational data; but, existing variants such as Graph Attention Network (GAT) mainly operate on static graphs and rely on implicit temporal aggregation when applied to sequential data. In this paper, we introduce Electroencephalography-temporal Graph Attention Network (EEG-tGAT), a temporally augmented formulation of GATv2 that is tailored for affordance classification from interaction sequences. The proposed model incorporates temporal attention to modulate the contribution of different time segments and temporal dropout to regularize learning across temporally correlated observations....
| Authors | Ami Chopra, Supriya Bordoloi, Shyamanta M. Hazarika |
|---|---|
| Primary category | cs.LG |
| Published | 11 April 2026 |
| Last updated at source | 11 April 2026 |
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