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

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 ...

AI is helping scientists solve complex equations in minutes - accelerating discoveries in chemistry, climate, and medicine.

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 ...

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 ...