The application of Riemannian geometry in the decoding of brain-computer interfaces (BCIs)
has swiftly garnered attention because of its straightforwardness, precision, and resilience …

Data augmentation is an essential building block for learning efficient deep learning models.
Among all augmentation techniques proposed so far, linear interpolation of training data …

We study the problem of robust data augmentation for regression tasks in the presence of
noisy data. Data augmentation is essential for generalizing deep learning models, but most …

Graph convolutional networks have significant advantages in dealing with graph-structured
data, but most existing methods usually potentially assume that nodes belonging to the …

In many real-world applications such as social network analysis, knowledge graph
discovery, biological network analytics, and so on, graph data management has become …

Recent advancements in neuroimaging techniques have sparked a growing interest in
understanding the complex interactions between anatomical regions of interest (ROIs) …

Networks consist of interconnected nodes and edges that depict entities and their
relationships. In social network clustering, nodes are grouped into clusters based on their …

Mixup, which generates synthetic training samples on the data manifold, has been shown to
be highly effective in augmenting Euclidean data. However, finding a proper data manifold …

Networks consist of interconnected nodes and edges that depict entities and their
relationships. In social network clustering, nodes are grouped into clusters based on their …