A5001176362- Advanced Graph Neural Networks
- Cancer, Hypoxia, and Metabolism
- Advanced Vision and Imaging
- Graph Theory and Algorithms
- Brain Tumor Detection and Classification
- Healthcare Systems and Public Health
- Heme Oxygenase-1 and Carbon Monoxide
- Imbalanced Data Classification Techniques
- Advanced Image and Video Retrieval Techniques
- Multimodal Machine Learning Applications
- Biomarkers in Disease Mechanisms
- Recommender Systems and Techniques
- Complex Network Analysis Techniques
- High Altitude and Hypoxia
Xiamen University of Technology
2024-2025
In previous research, the prevailing assumption was that Graph Neural Networks (GNNs) precisely depicted interconnections among nodes within graph's architecture. Nonetheless, real-world graph datasets are often rife with noise, elements can disseminate through network and ultimately affect outcome of downstream tasks. Facing complex fabric graphs myriad potential disturbances, we introduce Sparse Dynamic Attention (SDGAT) in this research. SDGAT employs L0 regularization technique to...
<title>Abstract</title> Graph Convolutional Networks (GCNs) is a dominant approach for graph representation learning through neighborhood aggregation.However, existing GCN methods rely on single structural view that only captures direct connections. This limitation overlooks important long-range dependencies and global topological patterns, leading to suboptimal node representations downstream tasks. To address these limitations, we propose Multi-Block Network (MBGCN) constructs two...
Map-free relocalization technology is crucial for applications in autonomous navigation and augmented reality, but relying on pre-built maps often impractical. It faces significant challenges due to limitations matching methods the inherent lack of scale monocular images. These issues lead substantial rotational metric errors even localization failures real-world scenarios. Large significantly impact overall process, affecting both translational accuracy. Due camera itself, recovering from a...