Scientists at UCL, Google DeepMind and Intrinsic have developed a powerful new AI algorithm that enables large sets of ...

Recently, Tianjin Yingjie Technology Development Co., Ltd. announced the application for a patent titled "An Optimization Method for Multi-Robot Collaborative Task Scheduling Based on Graph Neural ...

Based on our results, we recommend Graph Neural Networks for physics simulation workloads. The TF-GNN API has a steep initial learning curve for using its data layer, especially if there is no data ...

Abstract: “Here, we develop a framework for the prediction and screening of native defects and functional impurities in a chemical space of Group IV, III-V, and II-VI zinc blende (ZB) semiconductors, ...

BingoCGN, a scalable and efficient graph neural network accelerator that enables inference of real-time, large-scale graphs through graph partitioning, has been developed by researchers at ...

Researchers have developed a new tool, bimodularity, that adds directionality to community detection in networks.

Lead author Matthew Lai, a PhD researcher at UCL and Google DeepMind, said, “RoboBallet transforms industrial robotics into a ...

I co-created Graph Neural Networks while at Stanford. I recognized early on that this technology was incredibly powerful. Every data point, every observation, every piece of knowledge doesn’t exist in ...

Shenzhen Xister New Energy Technology Co., Ltd. recently announced that it has applied for a patent titled "An AI-Based Circuit Wiring System, Method, Medium, and Device," with publication number ...

Neo4j also trumpeted the value of graphs as vector databases used in generative artificial intelligence. AI training requires ...