Classic Graph Convolutional Networks (GCNs) often learn node representation holistically, which would ignore the distinct impacts from different neighbors when aggregating their features to update a ...

Researchers at Chiba University in Japan have developed a new artificial intelligence framework capable of decoding complex brain activity with significantly improved accuracy, marking an important ...

Motor imagery electroencephalography (EEG) signals depict changes in brain activity during imagined limb movements. Conventional methods, however, often fail to capture these spatiotemporal variations ...

Giulia Livieri sets out remarkable new research with results that clarify how learning works on complex graphs and how quickly any method (including Graph Convolutional Networks) can learn from them, ...