Neural Architecture Search Benchmarks: Insights and Survey
Author:
Affiliation:
1. Department of Electrical and Computer Engineering, Iowa State University, Ames, IA, USA
2. Argonne National Laboratory, Lemont, IL, USA
Funder
Philip and Virginia Sproul Professorship;
National Science Foundation at Iowa State University
Argonne Leadership Computing Facility, which is a DOE Office of Science User Facility
Publisher
Institute of Electrical and Electronics Engineers (IEEE)
Subject
General Engineering,General Materials Science,General Computer Science,Electrical and Electronic Engineering
Link
https://ieeexplore.ieee.org/ielam/6287639/10005208/10063950-aam.pdf
Reference165 articles.
1. BossNAS: Exploring Hybrid CNN-transformers with Block-wisely Self-supervised Neural Architecture Search
2. Zero-cost proxies for lightweight NAS;abdelfattah;arXiv 2101 08134,2021
3. Surrogate NAS benchmarks: Going beyond the limited search spaces of tabular NAS benchmarks;zela;arXiv 2008 09777,2020
4. NAS evaluation is frustratingly hard;yang;arXiv 1912 12522,2019
5. Progressive neural architecture search;liu;Proc Eur Conf Comput Vis (ECCV),2018
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