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

The findings suggest that advanced AI models, when designed to reflect real-world system interdependencies, can reduce the digital divide in urban resilience planning. Rather than benefiting only data ...

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 ...

Read more about Artificial intelligence could change future of antimicrobial drug discovery: Here's why on Devdiscourse ...

Mastercard's Decision Intelligence Pro uses recurrent neural networks to analyze 160 billion yearly transactions in under 50 ...

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

A new topology-based method predicts atomic charges in metal-organic frameworks from bond connectivity alone, making large-scale computational screening practical.

Takeda Pharmaceutical will apply Iambic Therapeutics’ artificial intelligence (AI)-based technologies and wet lab capabilities to design and develop small molecule drugs through a multi-year tech and ...

“Every previous AI tool focused on internal development,” said Chief Executive Alex Mashrabov. “We’re solving distribution ...

As firms rely more heavily on AI tools, understanding their architectural limits is becoming a professional necessity ...

Representation learning lies at the core of modern artificial intelligence, enabling neural networks to uncover meaningful, ...

In 2025 Artprice successfully integrated all the key tools of its proprietary AI (Intuitive Artmarket®) into its internal ...