4 accepted papers
Fair dynamic graph embedding is crucial for real-world systems, such as recommendation and social networks. Prior studies impose a single-axis fairness formulation, treating attribute and structural bias as separable artifacts. This overlooks their coupling relationship, under which debiasing along
Unmanned aerial vehicle swarms in pursuit-evasion requires encirclement efficiency while maintaining safety constraints, facing a critical safety-efficiency trade-off. Existing safe multi-agent reinforcement learning (MARL) methods often yield either unsafe task policies or conservative policies. Th
Graph contrastive learning (GCL) has drawn much research attention for its ability to learn node representations in a self-supervised manner. However, the homophily assumption inherent in GNN encoders limits the direction (macro-level) and the process (micro-level) of message passing in current GCL…
This paper studies a practically meaningful ship detection problem from synthetic aperture radar (SAR) images by the neural network. We broadly extract different types of SAR image features and raise the intriguing question that whether these extracted features are beneficial to (1) suppress data va…