Modeling Neural Architecture Search Methods for Deep Networks

• URL: http://arxiv.org/abs/1912.13183v1
• Date: Tue, 31 Dec 2019 05:51:03 GMT
• Title: Modeling Neural Architecture Search Methods for Deep Networks
• Authors: Emad Malekhosseini, Mohsen Hajabdollahi, Nader Karimi, Shadrokh Samavi
• Abstract summary: We propose a general abstraction model for neural architecture search (NAS) methods. It is possible to compare different design approaches for categorizing and identifying critical areas of interest.
• Score: 9.561123408923489
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: There are many research works on the designing of architectures for the deep neural networks (DNN), which are named neural architecture search (NAS) methods. Although there are many automatic and manual techniques for NAS problems, there is no unifying model in which these NAS methods can be explored and compared. In this paper, we propose a general abstraction model for NAS methods. By using the proposed framework, it is possible to compare different design approaches for categorizing and identifying critical areas of interest in designing DNN architectures. Also, under this framework, different methods in the NAS area are summarized; hence a better view of their advantages and disadvantages is possible.

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