Highway and Residual Networks learn Unrolled Iterative Estimation
The past year saw the introduction of new architectures such as Highway networks and Residual networks which, for the first time, enabled the training of feedforward networks with dozens to hundreds of layers using simple gradient descent. While depth of representation has been posited as a primary reason for their success, there are indications that these architectures defy a popular view of deep learning as a hierarchical computation of increasingly abstract features at each layer. In this report, we argue that this view is incomplete and does not adequately explain several recent findings. We propose an alternative viewpoint based on unrolled iterative estimation---a group of successive layers iteratively refine their estimates of the same features instead of computing an entirely new representation. We demonstrate that this viewpoint directly leads to the construction of highway and residual networks. Finally we provide preliminary experiments to discuss the similarities and differences between the two architectures.
The paper describes an alternative view on hierarchical feature representations in deep neural networks. The viewpoint of refining representations is well motivated and is in agreement with the success of recent model structures like ResNets. Pros: - Good motivation for the effectiveness of ResNets and Highway networks - Convincing analysis and evaluation Cons: - The effect of this finding of the interpretation of batch-normalization is only captured briefly but seems to be significant - Explanation of findings in (Zeiler & Fergus (2014)) using UIE viewpoint missing Remarks: - Missing word in line 223: *that it *is* valid*
You are a member of the scientific community tasked with peer review. Review the following paper title and abstract.
### Title + Abstract
Highway and Residual Networks learn Unrolled Iterative Estimation
The past year saw the introduction of new architectures such as Highway networks and Residual networks which, for the first time, enabled the training of feedforward networks with dozens to hundreds of layers using simple gradient descent. While depth of representation has been posited as a primary reason for their success, there are indications that these architectures defy a popular view of deep learning as a hierarchical computation of increasingly abstract features at each layer. In this report, we argue that this view is incomplete and does not adequately explain several recent findings. We propose an alternative viewpoint based on unrolled iterative estimation---a group of successive layers iteratively refine their estimates of the same features instead of computing an entirely new representation. We demonstrate that this viewpoint directly leads to the construction of highway and residual networks. Finally we provide preliminary experiments to discuss the similarities and differences between the two architectures.
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 describes an alternative view on hierarchical feature representations in deep neural networks. The viewpoint of refining representations is well motivated and is in agreement with the success of recent model structures like ResNets. Pros: - Good motivation for the effectiveness of ResNets and Highway networks - Convincing analysis and evaluation Cons: - The effect of this finding of the interpretation of batch-normalization is only captured briefly but seems to be significant - Explanation of findings in (Zeiler & Fergus (2014)) using UIE viewpoint missing Remarks: - Missing word in line 223: *that it *is* valid*
assistant
The paper describes an alternative view on hierarchical feature representations in deep neural networks. The viewpoint of refining representations is well motivated and is in agreement with the success of recent model structures like ResNets. Pros: - Good motivation for the effectiveness of ResNets and Highway networks - Convincing analysis and evaluation Cons: - The effect of this finding of the interpretation of batch-normalization is only captured briefly but seems to be significant - Explanation of findings in (Zeiler & Fergus (2014)) using UIE viewpoint missing Remarks: - Missing word in line 223: *that it *is* valid*