We investigate the potential of graph neural networks (GNNs) for transfer learning and improved molecular property prediction in the context of funnels or screening cascades characteristic of drug ...

Learning speed depends on both task structure and neural dynamics prior to learning, yet a theory connecting them has been missing. Inspired by the fluctuation-response relation, we derive two ...

Engineers have uncovered an unexpected pattern in how neural networks -- the systems leading today's AI revolution -- learn, suggesting an answer to one of the most important unanswered questions in ...

Spiking neural networks (SNNs) are artificial intelligence (AI) models inspired by how biological neurons communicate with ...

Learn about the most prominent types of modern neural networks such as feedforward, recurrent, convolutional, and transformer networks, and their use cases in modern AI. Neural networks are the ...

Researchers build fleeting memory transformers with human-like memory decay, proving memory limits help AI learn grammar ...

The Power of Attention Networks: Dr. Feng isolated baseline connectivity within attention and cognitive control networks as ...

Researchers have devised a way to make computer vision systems more efficient by building networks out of computer chips’ logic gates. Networks programmed directly into computer chip hardware can ...

As artificial intelligence explodes in popularity, two of its pioneers have nabbed the 2024 Nobel Prize in physics. The prize surprised many, as these developments are typically associated with ...

Penn Engineers have uncovered an unexpected pattern in how neural networks — the systems leading today’s AI revolution — learn, suggesting an answer to one of the most important unanswered questions ...