The combination of lifelong learning algorithms with autonomous intelligent systems (AIS) is
gaining popularity due to its ability to enhance AIS performance, but the existing summaries …

Self-awareness of its own state during autonomous operation is critical for Autonomous
Underwater Vehicles (AUVs) to execute tasks and monitor their health. Automated data …

Continual learning on graph data has recently attracted paramount attention for its aim to
resolve the catastrophic forgetting problem on existing tasks while adapting the sequentially …

Open-world semi-supervised learning (Open-world SSL) for node classification, that
classifies unlabeled nodes into seen classes or multiple novel classes, is a practical but …

Memory replay based techniques have shown great success for continual learning with
incrementally accumulated Euclidean data. Directly applying them to continually expanding …

Fracture identification and prediction are of great significance for the production of tight oil
and gas reservoirs. The high angles of fractures limit their traceability and reduce drilling …

Memory replay based techniques have shown great success for continual learning with
incrementally accumulated Euclidean data. Directly applying them to continually expanding …

We consider the problem of\textit {true} open-world semi-supervised node classification, in
which nodes in a graph either belong to known or new classes, with the latter not present …

In practical scenarios, many graphs dynamically evolve over time. The new node
classification without labels and historical information is challenging. To address this …

Continual learning has emerged as a crucial paradigm for learning from sequential data
while preserving previous knowledge. In the realm of continual graph learning, where …