# Deep Multi-task Representation Learning: A Tensor Factorisation Approach

arXiv: https://arxiv.org/abs/1605.06391 · HTML: https://mlsanity.com/paper/1605.06391/

## Key facts

- Cited by 69 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 20 May 2016.

## Built on by (most cited first, 30 of 69)

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- [SHELS: Exclusive Feature Sets for Novelty Detection and Continual Learning Without Class Boundaries](https://mlsanity.com/paper/2206.13720/) (2022)
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- [Multi-Task Learning for Visual Scene Understanding](https://mlsanity.com/paper/2203.14896/) (2022)
- [When Multi-Task Learning Meets Partial Supervision: A Computer Vision Review](https://mlsanity.com/paper/2307.14382/) (2023)
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- [Marginalizable Density Models](https://mlsanity.com/paper/2106.04741/) (2021)

## Abstract (arXiv)

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.