GD Learning

General Distribution Learning (GD learning) focuses on estimating the underlying probability distribution of data to improve model accuracy and stability in various machine learning tasks. Current research explores GD learning's application in diverse areas, including deep learning model optimization, autonomous vehicle control (e.g., using coupled PDE-ODE models for efficient platooning), and knowledge updating in large language models (e.g., through "forgetting before learning" techniques). This framework offers a theoretical foundation for understanding and addressing challenges like non-convex optimization and overparameterization in deep learning, while also enabling the development of more robust and efficient algorithms for various applications.