Search Results for author:
Found 4 papers, 0 papers with code
Spatio-Temporal Attention Graph Neural Network for Remaining Useful Life Prediction
Our model combines graph neural networks and temporal convolutional neural networks for spatial and temporal feature extraction, respectively.
Design of Explainability Module with Experts in the Loop for Visualization and Dynamic Adjustment of Continual Learning
With the help of this module, experts can be more confident in decision-making regarding anomaly filtering, dynamic adjustment of hyperparameters, data backup, etc.
Adaptive Explainable Continual Learning Framework for Regression Problems with Focus on Power Forecasts
Compared with traditional deep learning techniques, continual learning enables deep neural networks to learn continually and adaptively.
CLeaR: An Adaptive Continual Learning Framework for Regression Tasks
The second one is designed with data collected from European wind farms to evaluate the CLeaR framework's performance in a real-world application.
Cannot find the paper you are looking for? You can
Submit a new open access paper.
