Low-rank passthrough neural networks
Deep learning consists in training neural networks to perform computations that sequentially unfold in many steps over a time dimension or an intrinsic depth dimension. For large depths, this is usually accomplished by specialized network architectures that are designed to mitigate the vanishing gradient problem, e.g. LSTMs, GRUs, Highway Networks and Deep Residual Networks, which are based on a single structural principle: the state passthrough. We observe that these *Passthrough Networks* architectures enable the decoupling of the network state size from the number of parameters of the network, a possibility that is exploited in some recent works but not thoroughly explored. In this work we propose simple, yet effective, low-rank and low-rank plus diagonal matrix parametrizations for Passthrough Networks which exploit this decoupling property, reducing the data complexity and memory requirements of the network while preserving its memory capacity. We present competitive experimental results on several tasks, including a near state of the art result on sequential randomly-permuted MNIST classification, a hard task on natural data.
The authors study the use of low-rank approximation to the matrix-multiply in RNNs. This reduces the number of parameters by a large factor, and with a diagonal addition (called low-rank plus diagonal) it is shown to work as well as a fully-parametrized network on a number of tasks. The paper is solid, the only weakness being some claims about conceptual unification (e.g., the first line of the conclusion -- *We presented a framework that unifies the description various types of recurrent and feed-forward neural networks as passthrough neural networks.* -- claiming this framework as a contribution of this paper is untrue, the general framework is well known in the community and RNNs have been presented in this way before.) Aside from the above small point, the true contribution is in making low-rank RNNs work, the results are generally as good as fully-parametrized networks. They are hardly better though, which makes it unclear why low-rank networks should be used. The contribution is thus not very strong in terms of results, but even achieving the same results with fewer parameters is not easy and the studies were well-executed and explained.
You are a member of the scientific community tasked with peer review. Review the following paper title and abstract.
### Title + Abstract
Low-rank passthrough neural networks
Deep learning consists in training neural networks to perform computations that sequentially unfold in many steps over a time dimension or an intrinsic depth dimension. For large depths, this is usually accomplished by specialized network architectures that are designed to mitigate the vanishing gradient problem, e.g. LSTMs, GRUs, Highway Networks and Deep Residual Networks, which are based on a single structural principle: the state passthrough. We observe that these *Passthrough Networks* architectures enable the decoupling of the network state size from the number of parameters of the network, a possibility that is exploited in some recent works but not thoroughly explored. In this work we propose simple, yet effective, low-rank and low-rank plus diagonal matrix parametrizations for Passthrough Networks which exploit this decoupling property, reducing the data complexity and memory requirements of the network while preserving its memory capacity. We present competitive experimental results on several tasks, including a near state of the art result on sequential randomly-permuted MNIST classification, a hard task on natural data.
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 authors study the use of low-rank approximation to the matrix-multiply in RNNs. This reduces the number of parameters by a large factor, and with a diagonal addition (called low-rank plus diagonal) it is shown to work as well as a fully-parametrized network on a number of tasks. The paper is solid, the only weakness being some claims about conceptual unification (e.g., the first line of the conclusion -- *We presented a framework that unifies the description various types of recurrent and feed-forward neural networks as passthrough neural networks.* -- claiming this framework as a contribution of this paper is untrue, the general framework is well known in the community and RNNs have been presented in this way before.) Aside from the above small point, the true contribution is in making low-rank RNNs work, the results are generally as good as fully-parametrized networks. They are hardly better though, which makes it unclear why low-rank networks should be used. The contribution is thus not very strong in terms of results, but even achieving the same results with fewer parameters is not easy and the studies were well-executed and explained.
assistant
The authors study the use of low-rank approximation to the matrix-multiply in RNNs. This reduces the number of parameters by a large factor, and with a diagonal addition (called low-rank plus diagonal) it is shown to work as well as a fully-parametrized network on a number of tasks. The paper is solid, the only weakness being some claims about conceptual unification (e.g., the first line of the conclusion -- *We presented a framework that unifies the description various types of recurrent and feed-forward neural networks as passthrough neural networks. * -- claiming this framework as a contribution of this paper is untrue, the general framework is well known in the community and RNNs have been presented in this way before.) Aside from the above small point, the true contribution is in making low-rank RNNs work, the results are generally as good as fully-parametrized networks. They are hardly better though, which makes it unclear why low-rank networks should be used. The contribution is thus not very strong in terms of results, but even achieving the same results with fewer parameters is not easy and the studies were well-executed and explained.