Nature

Transfer learning with graph neural networks for improved molecular property prediction in the multi-fidelity setting

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 ...
Nature

Structure-aware graph neural network based deep transfer learning framework for enhanced predictive analytics on diverse materials datasets

Modern data mining methods have demonstrated effectiveness in comprehending and predicting materials properties. An essential component in the process of materials discovery is to know which ...
The Tech Edvocate

How to create neural network

Spread the love“`html Understanding how to create a neural network can be a game-changer in the fields of artificial intelligence and machine learning. As industries increasingly rely on data-driven ...
EurekAlert!

MSC-mediated mitochondrial transfer restores mitochondrial DNA and function in neural progenitor cells of Leber's hereditary optic neuropathy

Patch-clamp recordings demonstrated that LHON neurons, which typically exhibit impaired sodium and potassium currents, showed restored electrophysiological function after co-culture with MSCs.
InfoWorld

The most popular neural network styles and how they work

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 ...
Quanta Magazine

Novel Architecture Makes Neural Networks More Understandable

“Neural networks are currently the most powerful tools in artificial intelligence,” said Sebastian Wetzel, a researcher at the Perimeter Institute for Theoretical Physics. “When we scale them up to ...