3 accepted papers
Retrieval-Augmented Generation (RAG) enhances the response quality and domain-specific performance of large language models (LLMs) by incorporating external knowledge to combat hallucinations. In recent research, graph structures have been integrated into RAG to enhance the capture of semantic relat
Cross-modal brain networks characterize the complex connections between different brain regions from both functional and structural perspectives, which is of significant importance for brain network analysis and the diagnosis of brain diseases. However, existing methods have failed to fully exploit
Multivariate Time-Series (MTS) analysis is crucial across various domains. Considering the spatial and temporal consistency of MTS, existing methods leverage graph structures with temporal augmentation and contrastive learning to achieve robust learning of spatial dependencies and temporal patterns.