Evolutionary-Neural Hybrid Agents for Architecture Search
Neural Architecture Search has recently shown potential to automate the design of Neural Networks. The use of Neural Network agents trained with Reinforcement Learning can offer the possibility to learn complex patterns, as well as the ability to explore a vast and compositional search space. On the other hand, evolutionary algorithms offer the greediness and sample efficiency needed for such an application, as each sample requires a considerable amount of resources. We propose a class of Evolutionary-Neural hybrid agents (Evo-NAS), that retain the best qualities of the two approaches. We show that the Evo-NAS agent can outperform both Neural and Evolutionary agents, both on a synthetic task, and on architecture search for a suite of text classification datasets.
 Review: The paper introduces a novel way to do architecture search that uses an RNN to guide the mutation operation. The method and the motivation of the idea as long with the related work are all clearly described. However, the experiments section does not show a big uplift of the method versus the baselines and the number of types of tasks is relatively small (artificial and text). Cons: - No image task - No large scale task to show the scalability - No baselines that are not coming from AUTO-ML to show the relative performance of a classical method
You are a member of the scientific community tasked with peer review. Review the following paper title and abstract.
### Title + Abstract
Evolutionary-Neural Hybrid Agents for Architecture Search
Neural Architecture Search has recently shown potential to automate the design of Neural Networks. The use of Neural Network agents trained with Reinforcement Learning can offer the possibility to learn complex patterns, as well as the ability to explore a vast and compositional search space. On the other hand, evolutionary algorithms offer the greediness and sample efficiency needed for such an application, as each sample requires a considerable amount of resources. We propose a class of Evolutionary-Neural hybrid agents (Evo-NAS), that retain the best qualities of the two approaches. We show that the Evo-NAS agent can outperform both Neural and Evolutionary agents, both on a synthetic task, and on architecture search for a suite of text classification datasets.
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>



 Review: The paper introduces a novel way to do architecture search that uses an RNN to guide the mutation operation. The method and the motivation of the idea as long with the related work are all clearly described. However, the experiments section does not show a big uplift of the method versus the baselines and the number of types of tasks is relatively small (artificial and text). Cons: - No image task - No large scale task to show the scalability - No baselines that are not coming from AUTO-ML to show the relative performance of a classical method
assistant
 Review: The paper introduces a novel way to do architecture search that uses an RNN to guide the mutation operation. The method and the motivation of the idea as long with the related work are all clearly described. However, the experiments section does not show a big uplift of the method versus the baselines and the number of types of tasks is relatively small (artificial and text). Cons: - No image task - No large scale task to show the scalability - No baselines that are not coming from AUTO-ML to show the relative performance of a classical method