Model Transformation

Model transformation involves modifying existing machine learning models to improve efficiency, adapt to new data, or enhance explainability. Current research focuses on optimizing large language models for faster inference, adapting models for federated learning across diverse devices, and improving model adaptation for specific tasks like image classification. These advancements are significant for reducing computational costs, improving model accuracy in heterogeneous environments, and enabling more efficient and interpretable AI systems across various applications.