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Tackling neural architecture search with quality diversity optimization
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Schneider, Lennart, Pfisterer, Florian, Kent, Paul, Branke, Juergen, Bischl, Bernd and Thomas, Janek (2022) Tackling neural architecture search with quality diversity optimization. In: AutoML-Conf 2022 : 1st International Conference on Automated Machine Learning, Baltimore, US, 25-27 Jul 2022
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Official URL: https://openreview.net/forum?id=r0feZb6S8l9
Abstract
Neural architecture search (NAS) has been studied extensively and has grown to become a research field with substantial impact. While classical single-objective NAS searches for the architecture with the best performance, multi-objective NAS considers multiple objectives that should be optimized simultaneously, e.g., minimizing resource usage along the validation error. Although considerable progress has been made in the field of multiobjective NAS, we argue that there is some discrepancy between the actual optimization problem of practical interest and the optimization problem that multi-objective NAS tries to solve. We resolve this discrepancy by formulating the multi-objective NAS problem as a quality diversity optimization (QDO) problem and introduce three quality diversity NAS optimizers (two of them belonging to the group of multifidelity optimizers), which search for high-performing yet diverse architectures that are optimal for application-specific niches, e.g., hardware constraints. By comparing these optimizers to their multi-objective counterparts, we demonstrate that quality diversity NAS in general outperforms multiobjective NAS with respect to quality of solutions and efficiency. We further show how applications and future NAS research can thrive on QDO.
| Item Type: | Conference Item (Paper) | |||||||||
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| Subjects: | Q Science > QA Mathematics > QA76 Electronic computers. Computer science. Computer software | |||||||||
| Divisions: | Faculty of Science, Engineering and Medicine > Science > Mathematics Faculty of Social Sciences > Warwick Business School > Operational Research & Management Sciences Faculty of Social Sciences > Warwick Business School |
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| Library of Congress Subject Headings (LCSH): | Neural networks (Computer science), Computer architecture, Neural computers, Machine learning | |||||||||
| Official Date: | 2022 | |||||||||
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| Status: | Peer Reviewed | |||||||||
| Publication Status: | Published | |||||||||
| Access rights to Published version: | Open Access (Creative Commons) | |||||||||
| Date of first compliant deposit: | 6 June 2022 | |||||||||
| Date of first compliant Open Access: | 6 June 2022 | |||||||||
| RIOXX Funder/Project Grant: |
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| Conference Paper Type: | Paper | |||||||||
| Title of Event: | AutoML-Conf 2022 : 1st International Conference on Automated Machine Learning | |||||||||
| Type of Event: | Conference | |||||||||
| Location of Event: | Baltimore, US | |||||||||
| Date(s) of Event: | 25-27 Jul 2022 | |||||||||
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