As an emerging technology in the field of artificial intelligence (AI), graph neural networks (GNNs) are deep learning models designed to process graph-structured data. Currently, GNNs are effective ...

As an emerging technology in the field of artificial intelligence (AI), graph neural networks (GNNs) are deep learning models ...

Graph out-of-distribution (OOD) generalization remains a major challenge in graph neural networks (GNNs). Invariant learning, aiming to extract invariant features across varied distributions, has ...

Text mining and knowledge graphs connect cell-culture parameters to glycosylation for faster bioprocess optimization.

Strike Graph, the AI-native compliance management platform, today celebrates the growing adoption of Enterprise Workspaces, a new capability that enables organizations to govern compliance across ...

To some, METR’s “time horizon plot” indicates that AI utopia—or apocalypse—is close at hand. The truth is more complicated.

Traditional computational electromagnetics (CEM) methods—such as MoM, FEM, or FDTD—offer high fidelity, but struggle to scale ...

Nuclear energy is trending in the right direction, and it’s thanks to an unlikely suspect that has been blowing up for the ...

Edgen mirrors how professional investment teams operate, but adapts the process for individuals. The platform divides ...

Learn how frameworks like Solid, Svelte, and Angular are using the Signals pattern to deliver reactive state without the ...