Graph neural networks (GNNs) have emerged as a powerful framework for analyzing and learning from structured data represented as graphs. GNNs operate directly on graphs, as opposed to conventional ...

A scientist combines attention neural networks with graph neural networks to better understand and design proteins. The approach couples the strengths of geometric deep learning with those of language ...

In the past, the intellectual property issues in AI were generally overlooked. The technology moved very fast, most systems published in academic literature rarely progressed beyond proof of concept, ...

DynIMTS replaces static graphs with instance-attention that updates edge weights on the fly, delivering SOTA imputation and P12 classification ...