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 ...

Graph neural networks achieve these feats because graph approaches focus on discerning relationships between data. Relationships in Euclidian datasets aren’t as complicated as those in ...

The 2024 Nobel Prize in Chemistry was recently granted to David Baker, Demis Hassabis and John M. Jumper, renowned for their pioneering works in ...

Graph neural networks (GNNs) have rapidly emerged as a central methodology for analysing complex datasets presented as graphs, where entities are interconnected through diverse relationships. By ...

The demand for immersive, realistic graphics in mobile gaming and AR or VR is pushing the limits of mobile hardware. Achieving lifelike simulations of fluids, cloth, and other materials historically ...

Researchers at Shanghai Jiao Tong University have made a groundbreaking discovery in the field of Temporal Knowledge Graphs (TKGs), challenging the ...

It’s been ten years since AlexNet, a deep learning convolutional neural network (CNN) model running on GPUs, displaced more traditional vision processing algorithms to win the ImageNet Large Scale ...

eSpeaks’ Corey Noles talks with Rob Israch, President of Tipalti, about what it means to lead with Global-First Finance and how companies can build scalable, compliant operations in an increasingly ...

The global neural network market was valued at $14.35 billion in 2020, and is anticipated to grow at a CAGR of 26.7% from 2021 to 2030, reaching $152.61 billion. A neural network is a simple ...