DeepArchitect: Automatically Designing and Training Deep Architectures
In deep learning, performance is strongly affected by the choice of architecture and hyperparameters. While there has been extensive work on automatic hyperpa- rameter optimization for simple spaces, complex spaces such as the space of deep architectures remain largely unexplored. As a result, the choice of architecture is done manually by the human expert through a slow trial and error process guided mainly by intuition. In this paper we describe a framework for automatically designing and training deep models. We propose an extensible and modular lan- guage that allows the human expert to compactly represent complex search spaces over architectures and their hyperparameters. The resulting search spaces are tree- structured and therefore easy to traverse. Models can be automatically compiled to computational graphs once values for all hyperparameters have been chosen. We can leverage the structure of the search space to introduce different model search algorithms, such as random search, Monte Carlo tree search (MCTS), and sequen- tial model-based optimization (SMBO). We present experiments comparing the different algorithms on CIFAR-10 and show that MCTS and SMBO outperform random search. We also present experiments on MNIST, showing that the same search space achieves near state-of-the-art performance with a few samples. These experiments show that our framework can be used effectively for model discov- ery, as it is possible to describe expressive search spaces and discover competitive models without much effort from the human expert. Code for our framework and experiments has been made publicly available
 This paper introduces a DeepArchitect framework to build and train deep models automatically. Specifically, the authors proposes three components, i.e., model search specification language, model search algorithm, model evaluation algorithm. The paper is written well, and the proposed framework provides us with a systematical way to design deep models. However, my concern is mainly about its importance in practice. The experiments and computational modules are basic and small-scale, i.e., it may be restricted for large-scale computer vision problems.
You are a member of the scientific community tasked with peer review. Review the following paper title and abstract.
### Title + Abstract
DeepArchitect: Automatically Designing and Training Deep Architectures
In deep learning, performance is strongly affected by the choice of architecture and hyperparameters. While there has been extensive work on automatic hyperpa- rameter optimization for simple spaces, complex spaces such as the space of deep architectures remain largely unexplored. As a result, the choice of architecture is done manually by the human expert through a slow trial and error process guided mainly by intuition. In this paper we describe a framework for automatically designing and training deep models. We propose an extensible and modular lan- guage that allows the human expert to compactly represent complex search spaces over architectures and their hyperparameters. The resulting search spaces are tree- structured and therefore easy to traverse. Models can be automatically compiled to computational graphs once values for all hyperparameters have been chosen. We can leverage the structure of the search space to introduce different model search algorithms, such as random search, Monte Carlo tree search (MCTS), and sequen- tial model-based optimization (SMBO). We present experiments comparing the different algorithms on CIFAR-10 and show that MCTS and SMBO outperform random search. We also present experiments on MNIST, showing that the same search space achieves near state-of-the-art performance with a few samples. These experiments show that our framework can be used effectively for model discov- ery, as it is possible to describe expressive search spaces and discover competitive models without much effort from the human expert. Code for our framework and experiments has been made publicly available
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 introduces a DeepArchitect framework to build and train deep models automatically. Specifically, the authors proposes three components, i.e., model search specification language, model search algorithm, model evaluation algorithm. The paper is written well, and the proposed framework provides us with a systematical way to design deep models. However, my concern is mainly about its importance in practice. The experiments and computational modules are basic and small-scale, i.e., it may be restricted for large-scale computer vision problems.
assistant
 This paper introduces a DeepArchitect framework to build and train deep models automatically. Specifically, the authors proposes three components, i.e., model search specification language, model search algorithm, model evaluation algorithm. The paper is written well, and the proposed framework provides us with a systematical way to design deep models. However, my concern is mainly about its importance in practice. The experiments and computational modules are basic and small-scale, i.e., it may be restricted for large-scale computer vision problems.