Adolphi, C. and Sosonkina, M. (2025) Machine Learning and Simulation Techniques for Detecting Buoy Types from LiDAR Data.

What do you wonder? By The Learning Network Look closely at this image, stripped of its caption, and join the moderated conversation about what you and other students see. By The Learning Network ...

Machine learning approaches have been ... the proposed method includes unsupervised distance-based outlier elimination with GNN, empirical mode decomposition (EMD) as feature engineering, and ...

To enhance the robustness to data uncertainty, we propose a smoothness-based graph learning framework from a distributionally robust perspective, which is equivalent to solving an inf — sup problem.

The State Key Laboratory of Robotics, Shenyang Institute of Automation, Chinese Academy of Sciences, Shenyang 110016, China Key Laboratory of Networked Control Systems, Chinese Academy of Sciences, ...

Moreover, TREE employs a co-attention mechanism, where global structural encodings of nodes, learned from network distance ... transformer-powered graph representation learning, Nature Biomedical ...

Few-shot learning addresses this by acquiring meta-knowledge. Heterogeneous graphs (HGs), rich in semantic information ... class labels and enhancing classification accuracy. Euclidean distance-based ...

ICDE'25) [27/11/2024] Contrastive Graph Condensation: Advancing Data Versatility through Self-Supervised Learning (Xinyi Gao et al. Arxiv'24) [05/09/2024] GSTAM: Efficient Graph Distillation with ...