Branched Multi-Task Networks: Deciding What Layers To Share
In the context of multi-task learning, neural networks with branched architectures have often been employed to jointly tackle the tasks at hand. Such ramified networks typically start with a number of shared layers, after which different tasks branch out into their own sequence of layers. Understandably, as the number of possible network configurations is combinatorially large, deciding what layers to share and where to branch out becomes cumbersome. Prior works have either relied on ad hoc methods to determine the level of layer sharing, which is suboptimal, or utilized neural architecture search techniques to establish the network design, which is considerably expensive. In this paper, we go beyond these limitations and propose a principled approach to automatically construct branched multi-task networks, by leveraging the employed tasks* affinities. Given a specific budget, i.e. number of learnable parameters, the proposed approach generates architectures, in which shallow layers are task-agnostic, whereas deeper ones gradually grow more task-specific. Extensive experimental analysis across numerous, diverse multi-tasking datasets shows that, for a given budget, our method consistently yields networks with the highest performance, while for a certain performance threshold it requires the least amount of learnable parameters.
This paper proposes a novel soft parameter sharing Multi-task Learning framework based on a tree-like structure. The idea is interesting. However, the technique details, the experimental results and the analysis are not as attractive as the idea. The proposed method is a simple combination of existing works without any creative improvement. Furthermore, comparing with the MTL baseline, the experimental performance of proposed method does not get obvious improvement while the computation cost increasing significantly. Besides, there is not enough analysis about the idea this paper proposed. The intuition that more similar tasks share more parameters probably cannot always ensure the improvement of MTL.
You are a member of the scientific community tasked with peer review. Review the following paper title and abstract.
### Title + Abstract
Branched Multi-Task Networks: Deciding What Layers To Share
In the context of multi-task learning, neural networks with branched architectures have often been employed to jointly tackle the tasks at hand. Such ramified networks typically start with a number of shared layers, after which different tasks branch out into their own sequence of layers. Understandably, as the number of possible network configurations is combinatorially large, deciding what layers to share and where to branch out becomes cumbersome. Prior works have either relied on ad hoc methods to determine the level of layer sharing, which is suboptimal, or utilized neural architecture search techniques to establish the network design, which is considerably expensive. In this paper, we go beyond these limitations and propose a principled approach to automatically construct branched multi-task networks, by leveraging the employed tasks* affinities. Given a specific budget, i.e. number of learnable parameters, the proposed approach generates architectures, in which shallow layers are task-agnostic, whereas deeper ones gradually grow more task-specific. Extensive experimental analysis across numerous, diverse multi-tasking datasets shows that, for a given budget, our method consistently yields networks with the highest performance, while for a certain performance threshold it requires the least amount of learnable parameters.
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>



This paper proposes a novel soft parameter sharing Multi-task Learning framework based on a tree-like structure. The idea is interesting. However, the technique details, the experimental results and the analysis are not as attractive as the idea. The proposed method is a simple combination of existing works without any creative improvement. Furthermore, comparing with the MTL baseline, the experimental performance of proposed method does not get obvious improvement while the computation cost increasing significantly. Besides, there is not enough analysis about the idea this paper proposed. The intuition that more similar tasks share more parameters probably cannot always ensure the improvement of MTL.
assistant
This paper proposes a novel soft parameter sharing Multi-task Learning framework based on a tree-like structure. The idea is interesting. However, the technique details, the experimental results and the analysis are not as attractive as the idea. The proposed method is a simple combination of existing works without any creative improvement. Furthermore, comparing with the MTL baseline, the experimental performance of proposed method does not get obvious improvement while the computation cost increasing significantly. Besides, there is not enough analysis about the idea this paper proposed. The intuition that more similar tasks share more parameters probably cannot always ensure the improvement of MTL.