ChatGPT-style models are being trained to detect what a news article really thinks about an issue – even when that stance is ...

To address these challenges, we propose AffiGrapher, a physics-driven graph neural network that integrates a physics-informed graph architecture with contrastive learning. Incorporating multiple RNA ...

By introducing a novel top-k queue-based contrastive learning strategy, the framework significantly improves the model’s accuracy in distinguishing challenging positive and negative samples and its ...

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

Our approach, based on the supervised contrastive (SupCon) learning, uses the label information available to train an image encoder in a contrastive manner from multiple modalities while also training ...

Specifically, a contrastive learning-based contextual encoder is designed, integrating sentiment information for semantics learning. Moreover, a weighted label-enhanced syntactic graph neural network ...

As illustrated in Figure 1, the functional graph contrastive learning (fGCL) encoder learns to extract embeddings of each graph as subject-invariant representations (i.e., maximizing the ...