Year: 2020
Type: article
Abstract: For the past years, many researchers and engineers have been developing and optimising deep neural networks (DNN). The process of neural architecture design and tuning its hyperparameters remains monotonous, timeconsuming, and do not always ensure optimal results. In his regard, the automatic design of machine learning (AutoML) has been widely utilised, and neural architecture search (NAS) has been actively developing in recent years. Despite meaningful advances in the field of NAS, a unified, systematic approach \nto explore and compare search methods has not been established yet. In this paper, we aim to close this knowledge gap by summarising search decisions and \nstrategies and propose a schematic framework. It applies quantitative and qualitative metrics for prototyping, comparing, and benchmarking the NAS methods. \nMoreover, our framework enables categorising critical areas to search for better \nneural architectures.
Authors: Pavlo Radiuk, Nadiia Hrypynska
Language: English
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: green
OpenAlex ID: W3029210052