By transforming movement into data, Timothy Dunn is reshaping how scientists can study behavior and the brain.

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

Abstract: Missing node attributes pose a common problem in real-world graphs, impacting the performance of graph neural networks’ representation learning. Existing GNNs often struggle to effectively ...

Abstract: Graph Neural Networks (GNNs) have become a powerful tool in order to learn from graph-structured data. Their ability to capture complex relationships and dependencies within graph structures ...

If you have a health insurance plan, you’ve probably come across the terms “in-network” and “out-of-network.” Simply put, in-network means the doctors or hospitals you visit contract with your ...

According to mathematical legend, Peter Sarnak and Noga Alon made a bet about optimal graphs in the late 1980s. They’ve now both been proved wrong. It started with a bet. In the late 1980s, at a ...

For the fastest way to join Tom's Guide Club enter your email below. We'll send you a confirmation and sign you up to our newsletter to keep you updated on all the latest news. By submitting your ...

Artificial intelligence might now be solving advanced math, performing complex reasoning, and even using personal computers, but today’s algorithms could still learn a thing or two from microscopic ...

NATICK, Mass.--(BUSINESS WIRE)--MathWorks, the leading developer of mathematical computing software, today announced the availability of a hardware support package for the Qualcomm® Hexagon™ Neural ...

“Neural networks are currently the most powerful tools in artificial intelligence,” said Sebastian Wetzel, a researcher at the Perimeter Institute for Theoretical Physics. “When we scale them up to ...