• Corpus ID: 196621796

Dynamical Systems as Temporal Feature Spaces

  title={Dynamical Systems as Temporal Feature Spaces},
  author={Peter Tiňo},
  journal={J. Mach. Learn. Res.},
  • P. Tiňo
  • Published 15 July 2019
  • Computer Science
  • J. Mach. Learn. Res.
Parameterized state space models in the form of recurrent networks are often used in machine learning to learn from data streams exhibiting temporal dependencies. To break the black box nature of such models it is important to understand the dynamical features of the input driving time series that are formed in the state space. We propose a framework for rigorous analysis of such state representations in vanishing memory state space models such as echo state networks (ESN). In particular, we… 

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