Unified Framework

Unified frameworks in machine learning aim to consolidate diverse approaches to a specific problem into a single, coherent architecture, improving efficiency and facilitating comparative analysis. Current research focuses on developing such frameworks for various tasks, including recommendation systems, video understanding, and natural language processing, often leveraging transformer models, diffusion models, and recurrent neural networks. These unified approaches enhance model performance, enable more robust comparisons between methods, and offer improved interpretability and controllability, ultimately advancing both theoretical understanding and practical applications across numerous domains.