Abstract: The presence of asynchronous absolute and relative measurements has posed a great challenge to the current multisensor positioning method in robotic systems. Although traditional factor ...

Abstract: Anomaly detection needs to learn one-class classifiers from normal instances in observation or feature spaces. In the Neyman–Pearson criterion, the design of one-class classifiers boils down ...

Pre-training Graph Model Phase. In the pre-training phase, we employ link prediction as the self-supervised task for pre-training the graph model. Producer Phase. In the Producer phase, we employ LLM ...

A professionally curated list of awesome resources (paper, code, data, etc.) on Deep Graph Anomaly Detection (DGAD), which is the first work to comprehensively and systematically summarize the recent ...