Active Learning

Cost-effective Data Labelling for Graph Neural Networks
Active learning (AL), that aims to label limited data samples to effectively train the model, stands as a very cost-effective data …
GOODAT: Towards Test-time Graph Out-of-Distribution Detection
Graph neural networks (GNNs) have found widespread application in modeling graph data across diverse domains. While GNNs excel in …
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NestE: Modeling Nested Relational Structures for Knowledge Graph Reasoning
Reasoning with knowledge graphs (KGs) has primarily focused on triple-shaped facts. Recent advancements have been explored to enhance …
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Towards Model Extraction Attacks in GAN-based Image Translation via Domain Shift Mitigation
Model extraction attacks (MEAs) enable an attacker to replicate the functionality of a victim deep neural network (DNN) model by only …