Single All in One Model

"All-in-one" models represent a growing trend in machine learning, aiming to integrate multiple functionalities or optimize several aspects of a system within a single architecture. Research focuses on developing unified frameworks that simultaneously handle diverse tasks (e.g., compression and quantization of multiple speech recognition systems, heterogeneous interaction modeling in recommender systems, or multimodal pre-training), often leveraging advanced techniques like adapter modules or novel optimization algorithms (e.g., CoRe). This approach promises increased efficiency, reduced computational costs, and improved performance compared to training and deploying separate models, impacting various fields from speech processing and recommendation systems to image generation and biomedical natural language processing.