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.

ai shorts applications artificial intelligence attention computational context costs data dependencies editors pick graph machine learning mamba modeling novel scalability space staff state tech news technology transformers

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