Task Relatedness

Task relatedness explores how the relationships between different machine learning tasks influence model performance and learning efficiency. Current research focuses on leveraging these relationships through various methods, including attention mechanisms to select task-relevant information, meta-learning algorithms that adapt to new tasks based on their similarity to previously learned ones, and multi-task learning frameworks that share information across tasks via feature, parameter, or output levels. Understanding and effectively utilizing task relatedness improves model generalization, reduces training data requirements, and enhances the performance of individual tasks, impacting diverse applications from robotics and anomaly detection to natural language processing and web navigation.