Cross Task

Cross-task learning focuses on training models to perform multiple related tasks simultaneously, leveraging shared information to improve efficiency and performance compared to training separate models for each task. Current research emphasizes developing architectures that effectively manage cross-task interactions, including transformer-based models and those incorporating mechanisms like mixture-of-experts and attention modules to selectively share information between tasks. This approach is proving valuable in diverse fields, such as healthcare prediction, scene understanding, and natural language processing, by enabling more robust and data-efficient models for complex applications.