Deep GraphRAG: A Balanced Approach to Hierarchical Retrieval and Adaptive Integration Deep GraphRAG: A Balanced Approach to Hierarchical Retrieval and Adaptive IntegrationGraph-based Retrieval-Augmented Generation (GraphRAG) frameworks face a trade-off between the comprehensiveness of global search and the efficiency of local search. Existing methods are often challenged by navigating large-scale hierarchical
Topology-Aware Representation Alignment for Semi-Supervised Vision-Language Learning Topology-Aware Representation Alignment for Semi-Supervised Vision-Language LearningVision-language models have shown strong performance, but they often generalize poorly to specialized domains. While semi-supervised vision-language learning mitigates this limitation by leveraging a small set of labeled image-text pairs together with abundant unlabeled images, existing methods
안녕하세요 GUG 정이태입니다. 2026 1분기가 지나간지가 엊그제 같은데 벌써 2분기의 시작인 4월이 끝나가고 있네요. 연초에 세우셨던 계획들은 잘 수행하고 계시는지요. GUG 는 여러분들의 성원 덕분에 꾸준히 번창하고 있습니다. 지금 글을 작성하고 있는 26년 4월 27일 기준으로 857명의 구독자분들이 함께 해주고 계십니다. 늘 감사드립니다. 요새 바쁘다는 핑계로 GUG 신경을 못쓰고 있었는데,
Learning Laplacian Forms for Graph Signal Processing via the Deformed Laplacian Learning Laplacian Forms for Graph Signal Processing via the Deformed LaplacianLearning the graph Laplacian from observed data is one of the most investigated and fundamental tasks in Graph Signal Processing (GSP). Different variants of the Laplacian, such as the
