@inproceedings{eebc12127b6c4da6af022eae500193db,
title = "Ensembles of Networks Produced from Neural Architecture Search",
abstract = "Neural architecture search (NAS) is a popular topic at the intersection of deep learning and high performance computing. NAS focuses on optimizing the architecture of neural networks along with their hyperparameters in order to produce networks with superior performance. Much of the focus has been on how to produce a single best network to solve a machine learning problem, but as NAS methods produce many networks that work very well, this affords the opportunity to ensemble these networks to produce an improved result. Additionally, the diversity of network structures produced by NAS drives a natural bias towards diversity of predictions produced by the individual networks. This results in an improved ensemble over simply creating an ensemble that contains duplicates of the best network architecture retrained to have unique weights.",
keywords = "Ensembles, High performance computing, Neural architecture search",
author = "Herron, {Emily J.} and Young, {Steven R.} and Potok, {Thomas E.}",
note = "Publisher Copyright: {\textcopyright} 2020, Springer Nature Switzerland AG.; 35th International Conference on High Performance Computing , ISC High Performance 2020 ; Conference date: 21-06-2020 Through 25-06-2020",
year = "2020",
doi = "10.1007/978-3-030-59851-8_14",
language = "English",
isbn = "9783030598501",
series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",
publisher = "Springer Science and Business Media Deutschland GmbH",
pages = "223--234",
editor = "Heike Jagode and Hartwig Anzt and Guido Juckeland and Hatem Ltaief",
booktitle = "High Performance Computing - ISC High Performance 2020 International Workshops, Revised Selected Papers",
}