A Random Matrix Approach to Echo-State Neural Networks


Romain Couillet, Gilles Wainrib, Hafiz Tiomoko Ali, Harry Sevi ;
Proceedings of The 33rd International Conference on Machine Learning, PMLR 48:517-525, 2016.


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.

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