AISTATS 2022poster1 citations
Embedded Ensembles: infinite width limit and operating regimes
Maksim Velikanov, Roman V. Kail, Ivan Anokhin, Roman Vashurin, Maxim Panov, Alexey Zaytsev, Dmitry Yarotsky
Abstract
A memory efficient approach to ensembling neural networks is to share most weights among the ensembled models by means of a single reference network. We refer to this strategy as
BibTeX
@InProceedings{pmlr-v151-velikanov22a,
title = { Embedded Ensembles: infinite width limit and operating regimes },
author = {Velikanov, Maksim and Kail, Roman V. and Anokhin, Ivan and Vashurin, Roman and Panov, Maxim and Zaytsev, Alexey and Yarotsky, Dmitry},
booktitle = {Proceedings of The 25th International Conference on Artificial Intelligence and Statistics},
pages = {3138--3163},
year = {2022},
editor = {Camps-Valls, Gustau and Ruiz, Francisco J. R. and Valera, Isabel},
volume = {151},
series = {Proceedings of Machine Learning Research},
month = {28--30 Mar},
publisher = {PMLR},
pdf = {https://proceedings.mlr.press/v151/velikanov22a/velikanov22a.pdf},
url = {https://proceedings.mlr.press/v151/velikanov22a.html},
abstract = { A memory efficient approach to ensembling neural networks is to share most weights among the ensembled models by means of a single reference network. We refer to this strategy as