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

“Graph” refers to the idea that the architecture of a deep neural network can be thought of as a mathematical graph — a collection of points, or nodes, connected by lines, or edges. Here the nodes ...

For instance, the application of NAS to Graph Neural Networks (GNNs) is explored in depth, with the authors discussing the unique challenges and opportunities presented by non-Euclidean data.

While DeepMind’s original implementation uses an older TensorFlow 1.0 framework, which lacks compatibility with recent libraries, we adapt their architecture to TensorFlow 2, exploring the newly ...

Other than giving us an appreciation how little difference going eight miles an hour over the speed limit makes, that ETA service is a remarkable invention — and one that takes a hell of a lot of ...

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

The number of scientific papers that rely on AI has quadrupled, and the scope of problems AI can tackle is expanding by the ...

This review examines AI and ML's role in transforming thermoelectric materials design, focusing on defect engineering and ...