ICML 2016poster7 citations
A Random Matrix Approach to Echo-State Neural Networks
Romain Couillet, Gilles Wainrib, Hafiz Tiomoko Ali, Harry Sevi
Abstract
Recurrent neural networks, especially in their linear version, have provided many qualitative insights on their performance under different configurations. This article provides, through a novel random matrix framework, the quantitative counterpart of these performance results, specifically in the case of echo-state networks. Beyond mere insights, our approach conveys a deeper understanding on the core mechanism under play for both training and testing.
BibTeX
@InProceedings{pmlr-v48-couillet16,
title = {A Random Matrix Approach to Echo-State Neural Networks},
author = {Couillet, Romain and Wainrib, Gilles and Ali, Hafiz Tiomoko and Sevi, Harry},
booktitle = {Proceedings of The 33rd International Conference on Machine Learning},
pages = {517--525},
year = {2016},
editor = {Balcan, Maria Florina and Weinberger, Kilian Q.},
volume = {48},
series = {Proceedings of Machine Learning Research},
address = {New York, New York, USA},
month = {20--22 Jun},
publisher = {PMLR},
pdf = {http://proceedings.mlr.press/v48/couillet16.pdf},
url = {https://proceedings.mlr.press/v48/couillet16.html},
abstract = {Recurrent neural networks, especially in their linear version, have provided many qualitative insights on their performance under different configurations. This article provides, through a novel random matrix framework, the quantitative counterpart of these performance results, specifically in the case of echo-state networks. Beyond mere insights, our approach conveys a deeper understanding on the core mechanism under play for both training and testing.}
}