ACL 2021long30 citations
Reservoir Transformers
Sheng Shen, Alexei Baevski, Ari Morcos, Kurt Keutzer, Michael Auli, Douwe Kiela
Abstract
We demonstrate that transformers obtain impressive performance even when some of the layers are randomly initialized and never updated. Inspired by old and well-established ideas in machine learning, we explore a variety of non-linear “reservoir” layers interspersed with regular transformer layers, and show improvements in wall-clock compute time until convergence, as well as overall performance, on various machine translation and (masked) language modelling tasks.
BibTeX
@inproceedings{shen-etal-2021-reservoir,
title = "Reservoir Transformers",
author = "Shen, Sheng and
Baevski, Alexei and
Morcos, Ari and
Keutzer, Kurt and
Auli, Michael and
Kiela, Douwe",
editor = "Zong, Chengqing and
Xia, Fei and
Li, Wenjie and
Navigli, Roberto",
booktitle = "Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers)",
month = aug,
year = "2021",
address = "Online",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2021.acl-long.331/",
doi = "10.18653/v1/2021.acl-long.331",
pages = "4294--4309"
}