Researchers at the University of Oklahoma combined density functional theory calculations with graph neural networks to ...

A dual-graph deep learning network that hard-codes petroleum physics alongside learned statistics cuts oil production prediction error to about 20 percent, far outperforming conventional models.

Graph neural networks (GNNs) are a type of neural network architecture and deep learning method that can help users analyze graphs, enabling them to make predictions based on the data described by a ...

Graph neural networks (GNNs) have emerged as a versatile class of machine-learning models designed to process data structured as graphs, capturing relationships among entities through iterative ...

USC Engineering Insights - When a major earthquake hits Los Angeles, our best traffic models will be reading a map that no longer exists. USC Viterbi professor Cyrus Shahabi is building the system ...

Flahy uses knowledge graphs to connect biological and clinical data, supporting personalized decisions in AI-powered ...

Stephane is a tech enthusiast and AI advocate with a deep-seated passion for leveraging technology to solve real-world problems. With a background in Chemistry and hands-on experience in AI,... We ...

Called Dynamic Clustering for Channel Pruning (DCCP), the method reduced the floating-point operations of ResNet-50 by 68.52% ...

Machine learning and neural networks are two common terms in AI -- but what do they mean, and how do they differ? What exactly is machine learning? Machine learning is a subset of AI. ML uses an ...

Most of you have used a navigation app like Google Maps for your travels at some point. These apps rely on algorithms that compute shortest paths through vast networks. Now imagine scaling that task ...