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Active Foundational Models for Fault Diagnosis of Electrical Motors
To overcome this limitation, we propose a foundational model-based Active Learning framework that utilizes less amount of labeled samples, which are most informative and harnesses a large amount of available unlabeled data by effectively combining Active Learning and Contrastive Self-Supervised Learning techniques.
Foundational Models for Fault Diagnosis of Electrical Motors
However, the data distribution can vary across different operating conditions during real-world operating scenarios of electrical motors.
Challenges and Opportunities in Deep Reinforcement Learning with Graph Neural Networks: A Comprehensive review of Algorithms and Applications
Deep reinforcement learning (DRL) has empowered a variety of artificial intelligence fields, including pattern recognition, robotics, recommendation-systems, and gaming.
A General Framework for quantifying Aleatoric and Epistemic uncertainty in Graph Neural Networks
Graph Neural Networks (GNN) provide a powerful framework that elegantly integrates Graph theory with Machine learning for modeling and analysis of networked data.
Addressing practical challenges in Active Learning via a hybrid query strategy
Inspired by these practical challenges, we present a hybrid query strategy-based AL framework that addresses three practical challenges simultaneously: cold-start, oracle uncertainty and performance evaluation of Active Learner in the absence of ground truth.
