Abstract: Graph neural networks (GNNs), a class of deep learning models designed for performing information interaction on non-Euclidean graph data, have been successfully applied to node ...

Graph Neural Networks (GNNs) have become a powerful tool in order to learn from graph-structured data. Their ability to capture complex relationships and dependencies within graph structures, allows ...

The Supporting Information is available free of charge at https://pubs.acs.org/doi/10.1021/acs.jcim.5c01525. Efficiency analysis of different normalization strategies ...

Innovation Institute for Artificial Intelligence in Medicine of Zhejiang University, College of Pharmaceutical Sciences, Zhejiang University, Hangzhou 310058 ,Zhejiang ,China Atomistic Simulations, ...

The simplified approach makes it easier to see how neural networks produce the outputs they do. A tweak to the way artificial neurons work in neural networks could make AIs easier to decipher.

Companies in the Technology sector have received a lot of coverage today as analysts weigh in on Salesforce (CRM – Research Report) and Ubiquiti Networks (UI – Research Report). Salesforce (CRM) ...

Graph-structured data appears frequently in domains including chemistry, natural language semantics, social networks, and knowledge bases. In this work, we study feature learning techniques for ...