MLCommons' AI training tests show that the more chips you have, the more critical the network that's between them.

These challenges have been addressed by formulating a Graph Convolutional Networks with ... algorithm into a neural network structure. Subsequently, it replaces the topology proximal projection with a ...

By integrating Monte Carlo/Molecular Dynamics simulations to predict surface segregation with a graph neural network (GNN) to assess site-specific activity, this approach establishes a crucial ...

A review by researchers at Tongji University and the University of Technology Sydney published in Frontiers of Computer Science, highlights the powerful role of graph neural networks (GNNs) in ...

Abstract: Graph convolutional network (GCN) has garnered significant attention in hyperspectral image (HSI) classification due to their ability to model non-Euclidean structured data. Compared with ...

The accurate description of electrostatic interactions remains a challenging problem for classical potential-energy functions. The commonly used fixed partial-charge approximation fails to reproduce ...

3. Graph Neural Networks GNNs have a broad range of uses in various domains due to the prevalence of graph-structured data, where the lack of an Euclidean structure makes it challenging to use DNNs in ...