Generalized Framework

Generalized frameworks in machine learning aim to create adaptable and efficient models applicable across diverse tasks and datasets, avoiding the need for task-specific designs. Current research focuses on developing such frameworks for various applications, including image processing (e.g., using diffusion models and graph convolutional networks), natural language processing (e.g., leveraging transformers and state-space models), and optimization problems (e.g., employing mixed-integer linear programming). These generalized approaches improve efficiency, scalability, and performance compared to task-specific methods, impacting fields ranging from medical diagnosis to autonomous driving.