Parameter Averaging

Parameter averaging, a technique for combining the weights or parameters of multiple neural networks, aims to improve model performance, robustness, and efficiency. Current research focuses on developing sophisticated averaging strategies, such as hierarchical or Fisher-weighted averaging, and applying them to diverse tasks including incremental learning, ensemble methods, and feature ranking. These advancements offer potential benefits across various applications by enhancing generalization, reducing computational costs, and mitigating the impact of model initialization variability.