The generalization of neural networks is a central challenge in machine
...
Structured pruning can simplify network architecture and improve inferen...
Despite achieving great success, graph neural networks (GNNs) are vulner...
Node injection attacks against Graph Neural Networks (GNNs) have receive...
Training generative adversarial networks (GANs) with limited data is val...
Structured pruning compresses neural networks by reducing channels (filt...
Conditional generative models aim to learn the underlying joint distribu...
Recently, transformation-based self-supervised learning has been applied...
Network embedding is aimed at mapping nodes in a network into low-dimens...
Generative adversarial networks (GANs) have achieved remarkable progress...
Graph neural networks (GNNs) achieve remarkable success in graph-based
s...
Despite achieving strong performance in the semi-supervised node
classif...
Graph or network data is ubiquitous in the real world, including social
...