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 ...

The team proposed propose a novel entity-type-enriched cascaded neural network (E 2 CNN) that considers the overlap triple problem and entity-type information to construct a Chinese financial ...

Learn about the most prominent types of modern neural networks such as feedforward, recurrent, convolutional, and transformer networks, and their use cases in modern AI. Neural networks are the ...

Suppose you have a thousand-page book, but each page has only a single line of text. You’re supposed to extract the information contained in the book using a scanner, only this particular scanner ...

An MIT spinoff co-founded by robotics luminary Daniela Rus aims to build general-purpose AI systems powered by a relatively new type of AI model called a liquid neural network. The spinoff, aptly ...

“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 ...

Learn what CNN is in deep learning, how they work, and why they power modern image recognition AI and computer vision ...

Ryan Lee has received funding from the Air Force Office of Science Research . The new material is a type of architected material, which gets its properties mainly from the geometry and specific traits ...

A Japanese research team has successfully reproduced the human neural circuit in vitro using multi-region miniature organs known as assembloids, which are derived from induced pluripotent stem (iPS) ...

Confused about activation functions in neural networks? This video breaks down what they are, why they matter, and the most common types — including ReLU, Sigmoid, Tanh, and more! #NeuralNetworks ...