Deep Multi-task Representation Learning: A Tensor Factorisation Approach
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.
The paper proposed a nice framework leveraging Tucker and Tensor train low-rank tensor factorization to induce parameter sharing for multi-task learning. The framework is nice and appealing. However, MTL is a very well studied problem and the paper considers simple task for different classification, and it is not clear if we really need ``Deep Learning* for these simple datasets. A comparison with existing shallow MTL is necessary to show the benefits of the proposed methods (and in particular being deep) on the dataset. The authors ignore them on the basis of speculation and it is not clear if the proposed framework is really superior to simple regularizations like the nuclear norm. The idea of nuclear norm regularization can also be extended to deep learning as gradient descent are popular in all methods.
You are a member of the scientific community tasked with peer review. Review the following paper title and abstract.
### Title + Abstract
Deep Multi-task Representation Learning: A Tensor Factorisation Approach
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.
user
<think>Okay, to do a decent peer review, I should start with summarizing what the work is about. This includes novelty, contributions, clarity, and impact. I might include suggestions to issues that I find critical to the usefulness of the work. Starting with novelty, there are different types of novelty. The first aspect of novelty is scope and scale. Is the work covering a broader perspective than previous works? The second aspect is methodology. Is the proposed methodology novel compared to previous methods? Do they provide any significant advantage over previous methodologies? The third aspect is results. Are the results different compared previous work? Does this lead to any novel theories or an extension of current theories? Next is clarity, I will be reading the work in depth and analyze if any sentences can be potentially ambiguous or misleading. If any sentences are ambiguous, I will state them and give a suggestion on how to rephrase such sentence or how to make more clear to the audience. Lastly, impact is important. I will attempt to predict how such work will impact the scientific community. This might include the applications of such work and how the work might transform the way we study a certain topic. Finally, I will might only mention issues that are critical. This is to keep the review as concise and helpful as possible.</think>



The paper proposed a nice framework leveraging Tucker and Tensor train low-rank tensor factorization to induce parameter sharing for multi-task learning. The framework is nice and appealing. However, MTL is a very well studied problem and the paper considers simple task for different classification, and it is not clear if we really need ``Deep Learning* for these simple datasets. A comparison with existing shallow MTL is necessary to show the benefits of the proposed methods (and in particular being deep) on the dataset. The authors ignore them on the basis of speculation and it is not clear if the proposed framework is really superior to simple regularizations like the nuclear norm. The idea of nuclear norm regularization can also be extended to deep learning as gradient descent are popular in all methods.
assistant
The paper proposed a nice framework leveraging Tucker and Tensor train low-rank tensor factorization to induce parameter sharing for multi-task learning. The framework is nice and appealing. However, MTL is a very well studied problem and the paper considers simple task for different classification, and it is not clear if we really need ``Deep Learning* for these simple datasets. A comparison with existing shallow MTL is necessary to show the benefits of the proposed methods (and in particular being deep) on the dataset. The authors ignore them on the basis of speculation and it is not clear if the proposed framework is really superior to simple regularizations like the nuclear norm. The idea of nuclear norm regularization can also be extended to deep learning as gradient descent are popular in all methods.