Decoding emotional states from electroencephalography (EEG) signals is a fundamental goal in affective neuroscience. This endeavor requires accurately modeling the complex spatio-temporal dynamics of ...

Hyperspectral images (HSIs) have very high dimensionality and typically lack sufficient labeled samples, which significantly challenges their processing and analysis. These challenges contribute to ...

ABSTRACT: Managing psychiatric disorders, including depression, anxiety, and bipolar disorder, during pregnancy presents significant clinical challenges due to uncertainties surrounding medication ...

Abstract: Compared with traditional neural networks, graph convolutional networks are very suitable for processing graph structured data. However, common graph convolutional network methods often have ...

Abstract: Graph Convolutional Networks (GCNs) have emerged as a leading approach for semi-supervised node classification. However, due to the uneven distribution of labeled nodes in graphs, only a ...

\textit{Graph neural networks} (GNNs) have seen widespread usage across multiple real-world applications, yet in transductive learning, they still face challenges in accuracy, efficiency, and ...

There is no model loading and test code in the tutorial, and the way it saves the model seems different with others. Now I think the API used in the tutorial is kind of outdated. The nametuple ...