Graph Neural Networks (GNNs) and GraphRAG don’t “reason”—they navigate complex, open-world financial graphs with traceable, ...
The application of neural network models to semiconductor device simulation has emerged as a transformative approach in the field of electronics. These models offer significant speed improvements over ...
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 ...
Join our daily and weekly newsletters for the latest updates and exclusive content on industry-leading AI coverage. Learn More A team of chemistry, life science, and AI researchers are using graph ...
Expect to hear increasing buzz around graph neural network use cases among hyperscalers in the coming year. Behind the scenes, these are already replacing existing recommendation systems and traveling ...
Having incorporated AI technology in a myriad of ways in the development of drugs, Creative Biolabs remains an active ...
The artificial intelligence models inside machines such as industrial robots require the ability to interact safely and efficiently with their environment. But training an AI in a real-world setting, ...
The shift from slow, manual simulation to fast, automated optimisation using deep learning is unlocking better designs and faster time to market, says Jacomo Corbo, CEO and Co-Founder of PhysicsX.
Some results have been hidden because they may be inaccessible to you
Show inaccessible results
Feedback