Neural Operator Search
Existing neural architecture search (NAS) methods explore a limited feature-transformation-only search space while ignoring other advanced feature operations such as feature self-calibration by attention and dynamic convolutions. This disables the NAS algorithms to discover more advanced network architectures. We address this limitation by additionally exploiting feature self-calibration operations, resulting in a heterogeneous search space. To solve the challenges of operation heterogeneity and significantly larger search space, we formulate a neural operator search (NOS) method. NOS presents a novel heterogeneous residual block for integrating the heterogeneous operations in a unified structure, and an attention guided search strategy for facilitating the search process over a vast space. Extensive experiments show that NOS can search novel cell architectures with highly competitive performance on the CIFAR and ImageNet benchmarks.
This paper proposes a new method for neural architecture search that searches architectures with not only feature transformational components but also self-calibration and dynamic convolution ones. Although the idea seems to be straightforward and innovating, it is difficult to assess the effectiveness of the proposed approach comparing to other methods just by looking at the experimental results. Looking at the CIFAR10 results, the authors claimed that their method achieved second best among all the method compared but with significantly less number of parameters. Although indeed the number of parameters differ by a lot, the error rates are also differed significantly. It might be worth comparing these two baselines using the same number of parameters just to conduct a fair comparison. In the Imagenet results, the proposed method is behind many of the state-of-the-art methods, casting concerns on the effectiveness of the proposed approach considering its added search space.
You are a member of the scientific community tasked with peer review. Review the following paper title and abstract.
### Title + Abstract
Neural Operator Search
Existing neural architecture search (NAS) methods explore a limited feature-transformation-only search space while ignoring other advanced feature operations such as feature self-calibration by attention and dynamic convolutions. This disables the NAS algorithms to discover more advanced network architectures. We address this limitation by additionally exploiting feature self-calibration operations, resulting in a heterogeneous search space. To solve the challenges of operation heterogeneity and significantly larger search space, we formulate a neural operator search (NOS) method. NOS presents a novel heterogeneous residual block for integrating the heterogeneous operations in a unified structure, and an attention guided search strategy for facilitating the search process over a vast space. Extensive experiments show that NOS can search novel cell architectures with highly competitive performance on the CIFAR and ImageNet benchmarks.
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 proposes a new method for neural architecture search that searches architectures with not only feature transformational components but also self-calibration and dynamic convolution ones. Although the idea seems to be straightforward and innovating, it is difficult to assess the effectiveness of the proposed approach comparing to other methods just by looking at the experimental results. Looking at the CIFAR10 results, the authors claimed that their method achieved second best among all the method compared but with significantly less number of parameters. Although indeed the number of parameters differ by a lot, the error rates are also differed significantly. It might be worth comparing these two baselines using the same number of parameters just to conduct a fair comparison. In the Imagenet results, the proposed method is behind many of the state-of-the-art methods, casting concerns on the effectiveness of the proposed approach considering its added search space.
assistant
This paper proposes a new method for neural architecture search that searches architectures with not only feature transformational components but also self-calibration and dynamic convolution ones. Although the idea seems to be straightforward and innovating, it is difficult to assess the effectiveness of the proposed approach comparing to other methods just by looking at the experimental results. Looking at the CIFAR10 results, the authors claimed that their method achieved second best among all the method compared but with significantly less number of parameters. Although indeed the number of parameters differ by a lot, the error rates are also differed significantly. It might be worth comparing these two baselines using the same number of parameters just to conduct a fair comparison. In the Imagenet results, the proposed method is behind many of the state-of-the-art methods, casting concerns on the effectiveness of the proposed approach considering its added search space.