The potential for these kinds of machines to reshape computer processing, increase energy efficiency, and revolutionize medical testing has scientists excited. But when do we consider these cells to ...

Google's new Graph Foundation Model delivers up to 40 times greater precision and has been tested at scale on spam detection.

Deep neural networks are at the heart of artificial intelligence, ranging from pattern recognition to large language and reasoning models like ChatGPT. The principle: during a training phase, the ...

Researchers from the University of Campinas (Campinas, Brazil) and the Waters Research Center (Budapest, Hungary) introduced ...

Methods based on graph neural networks for solving combinatorial optimization (CO) problems have exhibited promising results in tackling a range of NP-hard problems, eliminating the necessity for ...

Recent research has employed chemical reaction networks (CRNs), which harness biochemical processes for computations that translate interactions involving biochemical species into graphical form.

By learning the relevant features of clinical images along with the relationships between them, the neural network can ...

For decades, scientists have looked to light as a way to speed up computing. Photonic neural networks—systems that use light ...

When someone starts a new job, early training may involve shadowing a more experienced worker and observing what they do ...

BingoCGN, a scalable and efficient graph neural network accelerator that enables inference of real-time, large-scale graphs ...

Zhang et al. (2017) examined the influence law of the dosing of cementitious materials such as fly ash and silica fume on the mechanical properties of UHPC using artificial neural network model.

Here, molecular graphs derived from the one-electron density matrix are introduced within a more general effort to explore whether incorporating electronic structure awareness allows a single model to ...