Deep neural networks (DNNs), the machine learning algorithms underpinning the functioning of large language models (LLMs) and ...

If you rotate an image of a molecular structure, a human can tell the rotated image is still the same molecule, but a machine ...

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 ...

While quantum computing provides new capabilities to AI, the relationship is reciprocal. The study explains how AI helps ...

In graph neural networks, the same sort of processing arises again; the message passing you see there is again something very natural,” he said. Ultimately, Blundell is excited about the ...

Using this information, the model can then tell us the probability of a drug-protein interaction that we did not previously have in the database, as the algorithms can efficiently analyse large ...

Graph neural networks (GNNs) are a relatively recent development in the field of machine learning. Like traditional graphs, a core principle of GNNs is that they model the dependencies and ...

Training algorithm breaks barriers to deep physical neural networks Date: December 7, 2023 Source: Ecole Polytechnique Fédérale de Lausanne Summary: Researchers have developed an algorithm to ...

Arriving at this graph neural network destination took the combined work of Google as well as Amazon, Waymo, and Sea AI Lab, but now provides Google Maps with a far more accurate ETA and the ability ...