SMASH: One-Shot Model Architecture Search through HyperNetworks
Designing architectures for deep neural networks requires expert knowledge and substantial computation time. We propose a technique to accelerate architecture selection by learning an auxiliary HyperNet that generates the weights of a main model conditioned on that model*s architecture. By comparing the relative validation performance of networks with HyperNet-generated weights, we can effectively search over a wide range of architectures at the cost of a single training run. To facilitate this search, we develop a flexible mechanism based on memory read-writes that allows us to define a wide range of network connectivity patterns, with ResNet, DenseNet, and FractalNet blocks as special cases. We validate our method (SMASH) on CIFAR-10 and CIFAR-100, STL-10, ModelNet10, and Imagenet32x32, achieving competitive performance with similarly-sized hand-designed networks.
 This paper is about a new experimental technique for exploring different neural architectures. It is well-written in general, numerical experiments demonstrate the framework and its capabilities as well as its limitations. A disadvantage of the approach may be that the search for architectures is random. It would be interesting to develop a framework where the search for the architecture is done with a framework where the updates to the architecture is done using a data-driven approach. Nevertheless, there are so many different neural architectures in the literature and this paper is a step towards comparing various architectures efficiently. Minor comments: 1) Page 7, ".. moreso than domain specificity." It may be better to spell the word "moreso" as "more so", please see: https://en.wiktionary.org/wiki/moreso
You are a member of the scientific community tasked with peer review. Review the following paper title and abstract.
### Title + Abstract
SMASH: One-Shot Model Architecture Search through HyperNetworks
Designing architectures for deep neural networks requires expert knowledge and substantial computation time. We propose a technique to accelerate architecture selection by learning an auxiliary HyperNet that generates the weights of a main model conditioned on that model*s architecture. By comparing the relative validation performance of networks with HyperNet-generated weights, we can effectively search over a wide range of architectures at the cost of a single training run. To facilitate this search, we develop a flexible mechanism based on memory read-writes that allows us to define a wide range of network connectivity patterns, with ResNet, DenseNet, and FractalNet blocks as special cases. We validate our method (SMASH) on CIFAR-10 and CIFAR-100, STL-10, ModelNet10, and Imagenet32x32, achieving competitive performance with similarly-sized hand-designed networks.
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 is about a new experimental technique for exploring different neural architectures. It is well-written in general, numerical experiments demonstrate the framework and its capabilities as well as its limitations. A disadvantage of the approach may be that the search for architectures is random. It would be interesting to develop a framework where the search for the architecture is done with a framework where the updates to the architecture is done using a data-driven approach. Nevertheless, there are so many different neural architectures in the literature and this paper is a step towards comparing various architectures efficiently. Minor comments: 1) Page 7, ".. moreso than domain specificity." It may be better to spell the word "moreso" as "more so", please see: https://en.wiktionary.org/wiki/moreso
assistant
 This paper is about a new experimental technique for exploring different neural architectures. It is well-written in general, numerical experiments demonstrate the framework and its capabilities as well as its limitations. A disadvantage of the approach may be that the search for architectures is random. It would be interesting to develop a framework where the search for the architecture is done with a framework where the updates to the architecture is done using a data-driven approach. Nevertheless, there are so many different neural architectures in the literature and this paper is a step towards comparing various architectures efficiently. Minor comments: 1) Page 7, ".. moreso than domain specificity." It may be better to spell the word "moreso" as "more so", please see: https://en.wiktionary.org/wiki/moreso