OIL-AD: An Anomaly Detection Framework for Sequential Decision Sequences
Anomaly detection in decision-making sequences is a challenging problem due to the
complexity of normality representation learning and the sequential nature of the task. Most …
complexity of normality representation learning and the sequential nature of the task. Most …
Transfer-learning-based representation learning for trajectory similarity search
D Lai, J Qu, Y Sang, X Chen - GeoInformatica, 2024 - Springer
Trajectory similarity search is one of the most fundamental tasks in spatial-temporal data
analysis. Classical methods are based on predefined trajectory similarity measures …
analysis. Classical methods are based on predefined trajectory similarity measures …
[PDF][PDF] Detecting Anomalous Agent Decision Sequences Based on Offline Imitation Learning
Anomaly detection in decision-making sequences is a challenging problem due to the
complexity of normality representation learning, the sequential nature of the task and the …
complexity of normality representation learning, the sequential nature of the task and the …