Dynamic Ensemble

Dynamic ensembles leverage multiple models to improve prediction accuracy and uncertainty quantification across diverse applications. Current research focuses on developing efficient algorithms, such as those based on generative deep learning (e.g., diffusion models, variational autoencoders) and dynamic model selection, to create ensembles that adapt to changing data or computational constraints. These advancements are significantly impacting fields ranging from weather forecasting and climate modeling to protein structure prediction and resource-constrained machine learning on IoT devices, enabling more robust and efficient analyses.