Learning To Learn Using Gradient Descent (2001)
by
Sepp Hochreiter
,
A. Steven Younger
,
Peter R. Conwell
| Venue: | IN LECTURE NOTES ON COMP. SCI. 2130, PROC. INTL. CONF. ON ARTI NEURAL NETWORKS (ICANN-2001 |
| Citations: | 17 - 1 self |
BibTeX
@INPROCEEDINGS{Hochreiter01learningto,
author = {Sepp Hochreiter and A. Steven Younger and Peter R. Conwell},
title = {Learning To Learn Using Gradient Descent},
booktitle = {IN LECTURE NOTES ON COMP. SCI. 2130, PROC. INTL. CONF. ON ARTI NEURAL NETWORKS (ICANN-2001},
year = {2001},
pages = {87--94},
publisher = {Springer}
}
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Abstract
This paper introduces the application of gradient descent methods to meta-learning. The concept of "meta-learning", i.e. of a system that improves or discovers a learning algorithm, has been of interest in machine learning for decades because of its appealing applications. Previous
