Identifying the progression stages of Alzheimer’s disease (AD) can be considered as an imbalanced multi-class classification problem in machine learning. It is challenging due to the class imbalance ...

By learning the relevant features of clinical images along with the relationships between them, the neural network can outperform more traditional methods.

GREmLN focuses on the “molecular logic” that defines how genes interact and influence each other. This illustration highlights how the model uniquely captures gene interaction and the influence of ...

To address this issue, we introduce a novel MDA prediction method named MVCL-MDA, based on heterogeneous graph meta-path views and network structure view for graph contrastive learning. This ...

Inspired by self-supervised learning, it uses a cross-view contrastive learning technique, splitting the graph into spatial and temporal views, designing specific graph neural networks, and using ...

As the field of representation learning grows, there has been a proliferation of different loss functions to solve different classes of problems. We introduce a single information-theoretic equation ...

Graph Contrastive Learning (GCL) plays a crucial role in multimedia applications due to its effectiveness in analyzing graph-structured data. Existing GCL methods focus on maximizing the agreement of ...

CLR, a novel contrastive learning method using graph-based sample relationships. This approach outperformed traditional ...

L2CL: Embarrassingly Simple Layer-to-Layer Contrastive Learning for Graph Collaborative Filtering ...

Therefore, we propose a graph contrastive learning strategy, which considers the consistency between graph and node representations. Specifically, we propose to maximize the similarity between the ...