Flexible Framework

Flexible frameworks in machine learning aim to create adaptable and reusable architectures for diverse tasks, overcoming limitations of task-specific designs. Current research focuses on developing such frameworks for various applications, including image and video processing (using techniques like implicit neural representations and ControlNets), biosignal generation, and large language model prompting and evaluation (employing methods such as recursive search and mixture-of-experts). These advancements enhance efficiency, improve model performance across different datasets and scenarios, and facilitate broader accessibility of advanced machine learning techniques for both researchers and practitioners.