Modeling Output-Level Task Relatedness in Multi-Task Learning with Feedback Mechanism

arXiv:2404.00885v1 Announce Type: new
Abstract: Multi-task learning (MTL) is a paradigm that simultaneously learns multiple tasks by sharing information at different levels, enhancing the performance of each individual task. While previous research has primarily focused on feature-level or parameter-level task relatedness, and proposed various model architectures and learning algorithms to improve learning performance, we aim to explore output-level task relatedness. This approach introduces a posteriori information into the model, considering that different tasks may produce correlated outputs with mutual influences. …

abstract algorithms architectures arxiv cs.lg feature feedback information modeling multiple multi-task learning paradigm performance research tasks type