More technically, they are graphs. The attributes of the nodes ... where the adversary acts as the loss function. Neural networks are a powerful way of thinking about problems and applying machine ...

Graph neural networks (GNNs ... Simple element-wise operations or more complex functions like neural networks can be used for this purpose. Once messages for all neighbors have been computed ...

and modify it slightly to move the whole graph a bit closer to the ideal solution. The idea here is that you consider the entire neural network and its loss function as a multivariate ...

GNN, a framework to train robust GNNs under noisy conditions. Soft-GNN mitigates label noise impact through dynamic ...

James McCaffrey explains what neural network ... activation function discussed is the logistic sigmoid function. The function is also called log-sigmoid, or just plain sigmoid. The function is defined ...

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 ...