Routing Networks: Adaptive Selection of Non-Linear Functions for Multi-Task Learning
Multi-task learning (MTL) with neural networks leverages commonalities in tasks to improve performance, but often suffers from task interference which reduces the benefits of transfer. To address this issue we introduce the routing network paradigm, a novel neural network and training algorithm. A routing network is a kind of self-organizing neural network consisting of two components: a router and a set of one or more function blocks. A function block may be any neural network – for example a fully-connected or a convolutional layer. Given an input the router makes a routing decision, choosing a function block to apply and passing the output back to the router recursively, terminating when a fixed recursion depth is reached. In this way the routing network dynamically composes different function blocks for each input. We employ a collaborative multi-agent reinforcement learning (MARL) approach to jointly train the router and function blocks. We evaluate our model against cross-stitch networks and shared-layer baselines on multi-task settings of the MNIST, mini-imagenet, and CIFAR-100 datasets. Our experiments demonstrate a significant improvement in accuracy, with sharper convergence. In addition, routing networks have nearly constant per-task training cost while cross-stitch networks scale linearly with the number of tasks. On CIFAR100 (20 tasks) we obtain cross-stitch performance levels with an 85% average reduction in training time.
 In this paper, the authors present a novel formulation for learning the optimal architecture of a neural network in a multi-task learning framework. Using multi-agent reinforcement learning to find a policy — a mapping from the input layer (and task indicator) to the function block that must be used at the next layer, the paper shows improvement over hard-coded architectures with shared layers. The idea is very interesting and the paper is well-written. The extensive review of the existing literature, along with systematically presented qualitative and quantitative results make for a clear and communicative read. I do think that further scrutiny and research would benefit the work since there are instances when the presented approach enjoys some benefits when solving the problems considered. For example, the more general formulation that uses the dispatcher does not appear to work ver well (Fig. 5), and the situation is only improved by using a task specific router. Overall, I think the idea is worthy of wide dissemination and has great potential for furthering the applications of multi-task learning.
You are a member of the scientific community tasked with peer review. Review the following paper title and abstract.
### Title + Abstract
Routing Networks: Adaptive Selection of Non-Linear Functions for Multi-Task Learning
Multi-task learning (MTL) with neural networks leverages commonalities in tasks to improve performance, but often suffers from task interference which reduces the benefits of transfer. To address this issue we introduce the routing network paradigm, a novel neural network and training algorithm. A routing network is a kind of self-organizing neural network consisting of two components: a router and a set of one or more function blocks. A function block may be any neural network – for example a fully-connected or a convolutional layer. Given an input the router makes a routing decision, choosing a function block to apply and passing the output back to the router recursively, terminating when a fixed recursion depth is reached. In this way the routing network dynamically composes different function blocks for each input. We employ a collaborative multi-agent reinforcement learning (MARL) approach to jointly train the router and function blocks. We evaluate our model against cross-stitch networks and shared-layer baselines on multi-task settings of the MNIST, mini-imagenet, and CIFAR-100 datasets. Our experiments demonstrate a significant improvement in accuracy, with sharper convergence. In addition, routing networks have nearly constant per-task training cost while cross-stitch networks scale linearly with the number of tasks. On CIFAR100 (20 tasks) we obtain cross-stitch performance levels with an 85% average reduction in training time.
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>



 In this paper, the authors present a novel formulation for learning the optimal architecture of a neural network in a multi-task learning framework. Using multi-agent reinforcement learning to find a policy — a mapping from the input layer (and task indicator) to the function block that must be used at the next layer, the paper shows improvement over hard-coded architectures with shared layers. The idea is very interesting and the paper is well-written. The extensive review of the existing literature, along with systematically presented qualitative and quantitative results make for a clear and communicative read. I do think that further scrutiny and research would benefit the work since there are instances when the presented approach enjoys some benefits when solving the problems considered. For example, the more general formulation that uses the dispatcher does not appear to work ver well (Fig. 5), and the situation is only improved by using a task specific router. Overall, I think the idea is worthy of wide dissemination and has great potential for furthering the applications of multi-task learning.
assistant
 In this paper, the authors present a novel formulation for learning the optimal architecture of a neural network in a multi-task learning framework. Using multi-agent reinforcement learning to find a policy — a mapping from the input layer (and task indicator) to the function block that must be used at the next layer, the paper shows improvement over hard-coded architectures with shared layers. The idea is very interesting and the paper is well-written. The extensive review of the existing literature, along with systematically presented qualitative and quantitative results make for a clear and communicative read. I do think that further scrutiny and research would benefit the work since there are instances when the presented approach enjoys some benefits when solving the problems considered. For example, the more general formulation that uses the dispatcher does not appear to work ver well (Fig. 5), and the situation is only improved by using a task specific router. Overall, I think the idea is worthy of wide dissemination and has great potential for furthering the applications of multi-task learning.