Dimension reduction in recurrent networks by canonicalization

• Main Authors: Grigoryeva, Lyudmila, Ortega, Juan-Pablo
• 其他作者: School of Physical and Mathematical Sciences
• 格式: Article
• 語言: English
• 出版: 2022
• 主題: Science::Mathematics; Recurrent Neural Network; Reservoir Computing
• 在線閱讀: https://hdl.handle.net/10356/161577

Many recurrent neural network machine learning paradigms can be formulated using state-space representations. The classical notion of canonical state-space realization is adapted in this paper to accommodate semi-infinite inputs so that it can be used as a dimension reduction tool in the recurrent n...