Ensemble Averaging

Ensemble averaging is a technique that combines predictions from multiple models to improve accuracy, robustness, and uncertainty quantification. Current research focuses on applying this method across diverse fields, including improving the performance of memristor-based neural networks, enhancing the interpretability of deep learning models through techniques like class activation map ensembles, and increasing the reliability of predictions in areas such as climate modeling and disease diagnosis. This approach is proving valuable for mitigating the effects of model variability and hardware imperfections, leading to more reliable and trustworthy results in various scientific and engineering applications.