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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. Experiments demonstrate the efficacy of our deep multi-task representation learning in terms of both higher accuracy and fewer design choices.
|Title of host publication||International Conference on Learning Representations (ICLR 2017)|
|Number of pages||12|
|Publication status||E-pub ahead of print - 26 Apr 2017|
|Event||5th International Conference on Learning Representations - Palais des Congrès Neptune, Toulon, France|
Duration: 24 Apr 2017 → 26 Apr 2017
|Conference||5th International Conference on Learning Representations|
|Abbreviated title||ICLR 2017|
|Period||24/04/17 → 26/04/17|
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