In order to understand the structure and functioning of the brain, neuroscientists need to study the complex, ...

Researchers at Forschungszentrum Jülich in Germany have published two new studies offering fresh insight into a protein ...

In this paper, we study the generalization capabilities of geometric graph neural networks (GNNs). We consider GNNs over a geometric graph constructed from a finite set of randomly sampled points over ...

While these traditional methods yield highly accurate results, they have been too resource-heavy to run real-time on mobile. But as mobile hardware advances, Machine Learning (ML) techniques, ...

This style of neural network is also known as a cyclical graph. The backward movement opens up a variety of more sophisticated learning techniques, and also makes RNNs more complex than some other ...

Graph neural networks (GNNs) are promising machine learning architectures designed to analyze data that can be represented as graphs. These architectures achieved very promising results on a variety ...

Graph drawing techniques have been developed in the last few years with the purpose of producing esthetically pleasing node-link layouts. Recently, the employment of differentiable loss functions has ...

Graph neural networks (GNNs), ... dissemination and aggregation between nodes and proposes a framework by defining a general form of the aggregation function. Mixture model networks defines a ...