RAM augments relational graph models with attribute-semantic retrieval via random-walk documents and two contrastive augmentations (ATRA, ETRA) to achieve state-of-the-art results on five real-world databases.
Bootstrapped representation learning on graphs.arXiv:2102.06514
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A pre-trained interference-aware graph Transformer model for wireless resource allocation that achieves strong few-shot adaptation to new tasks and scenarios.
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From Schema to Signal: Retrieval-Augmented Modeling for Relational Data Analytics
RAM augments relational graph models with attribute-semantic retrieval via random-walk documents and two contrastive augmentations (ATRA, ETRA) to achieve state-of-the-art results on five real-world databases.
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A Graph Foundation Model for Wireless Resource Allocation
A pre-trained interference-aware graph Transformer model for wireless resource allocation that achieves strong few-shot adaptation to new tasks and scenarios.
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Geometric Deep Learning: Grids, Groups, Graphs, Geodesics, and Gauges
Geometric deep learning provides a unified mathematical framework based on grids, groups, graphs, geodesics, and gauges to explain and extend neural network architectures by incorporating physical regularities.