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 ...

A new framework integrates graph databases with real-time machine learning to enhance fraud detection and risk control in digital finance. By ...

BingoCGN employs cross-partition message quantization to summarize inter-partition message flow, which eliminates the need for irregular off-chip memory access and utilizes a fine-grained structured ...

Researchers at the University of Geneva used Graph Neural Networks to model the complex patterns of multi-drug resistant Enterobacteriaceae transmission, aiming to advance how hospitals predict and ...

Earth Scientists have used machine learning for at least three decades and the applications span is large, from remote sensing to analysis of well log data, among many others. Although machine ...

David Beer’s book The Tensions of Algorithmic Thinking has recently been published by Bristol University Press. In 1956, during a year-long trip to London and in his early 20s, the mathematician and ...

See five surprising ways neural networks are being used today. Trump is on a collision course with millions of Americans. He's not backing down. Tom Cruise at the Oscars: His Different Looks and Loves ...

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 ...