Most contemporary multi-task learning methods assume linear models. This setting is considered shallow in the era of deep learning. In this paper, we present a new deep multi-task representation learning framework that learns cross-task sharing structure at every layer in a deep network. Our approach is based on generalising the matrix factorisation techniques explicitly or implicitly used by many conventional MTL algorithms to tensor factorisation, to realise automatic learning of end-to-end knowledge sharing in deep networks. This is in contrast to existing deep learning approaches that need a user-defined multi-task sharing strategy. Our approach applies to both homogeneous and heterogeneous MTL, as well as multi-domain learning (MDL). Experiments demonstrate the efficacy of our deep multi-task representation learning in terms of both higher accuracy and fewer design choices.
In the field of multi-task learning, REF applied tensor factorization to realize automatic learning of end-to-end knowledge sharing in deep neural networks.
Deep Multi-task Representation Learning: A Tensor Factorisation Approach
ArXiv
ArXiv
Mathematics Computer Science