Feb. 12, 2024, 6:17 a.m. | Nikhil

MarkTechPost www.marktechpost.com

Graph Transformers need help with scalability in graph sequence modeling due to high computational costs, and existing attention sparsification methods fail to adequately address data-dependent contexts. State space models (SSMs) like Mamba are effective and efficient in modeling long-range dependencies in sequential data, but adapting them to non-sequential graph data is challenging. Many sequence models […]

The post Meet Graph-Mamba: A Novel Graph Model that Leverages State Space Models SSM for Efficient Data-Dependent Context Selection appeared first on MarkTechPost.

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