Dimension reduction in recurrent networks by canonicalization
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...
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| Main Authors: | , |
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| 其他作者: | |
| 格式: | Article |
| 語言: | English |
| 出版: |
2022
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| 主題: | |
| 在線閱讀: | https://hdl.handle.net/10356/161577 |
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| 機構: | Nanyang Technological University |
| 語言: | English |