20 citations · 29 across the 12 of their papers we have counts for
8 papers · 1 filter
HC-LLM: Historical-Constrained Large Language Models for Radiology Report Generation
Tengfei Liu, Jiapu Wang, Yongli Hu +5
Radiology report generation (RRG) models typically focus on individual exams, often overlooking the integration of historical visual or textual data, which is crucial for patient f…
Adapter-Enhanced Semantic Prompting for Continual Learning
Baocai Yin, Ji Zhao, Huajie Jiang +5
Continual learning (CL) enables models to adapt to evolving data streams. A major challenge of CL is catastrophic forgetting, where new knowledge will overwrite previously acquired…
Dual-Frequency Filtering Self-aware Graph Neural Networks for Homophilic and Heterophilic Graphs
Yachao Yang, Yanfeng Sun, Jipeng Guo +4
Graph Neural Networks (GNNs) have excelled in handling graph-structured data, attracting significant research interest. However, two primary challenges have emerged: interference b…
Hierarchical Multi-modal Transformer for Cross-modal Long Document Classification
Tengfei Liu, Yongli Hu, Junbin Gao +2
Long Document Classification (LDC) has gained significant attention recently. However, multi-modal data in long documents such as texts and images are not being effectively utilize…
Query-Enhanced Adaptive Semantic Path Reasoning for Inductive Knowledge Graph Completion
Kai Sun, Jiapu Wang, Huajie Jiang +2
Conventional Knowledge graph completion (KGC) methods aim to infer missing information in incomplete Knowledge Graphs (KGs) by leveraging existing information, which struggle to pe…
Large Language Models-guided Dynamic Adaptation for Temporal Knowledge Graph Reasoning
Jiapu Wang, Kai Sun, Linhao Luo +5
Temporal Knowledge Graph Reasoning (TKGR) is the process of utilizing temporal information to capture complex relations within a Temporal Knowledge Graph (TKG) to infer new knowled…
IME: Integrating Multi-curvature Shared and Specific Embedding for Temporal Knowledge Graph Completion
Jiapu Wang, Zheng Cui, Boyue Wang +4
Temporal Knowledge Graphs (TKGs) incorporate a temporal dimension, allowing for a precise capture of the evolution of knowledge and reflecting the dynamic nature of the real world.…
DGNN: Decoupled Graph Neural Networks with Structural Consistency between Attribute and Graph Embedding Representations
Jinlu Wang, Jipeng Guo, Yanfeng Sun +4
Graph neural networks (GNNs) demonstrate a robust capability for representation learning on graphs with complex structures, showcasing superior performance in various applications.…