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Selection Criteria for Neuromanifolds of Stochastic Dynamics

Abstract

We present ways of defining neuromanifolds – models of stochastic matrices – that are compatible with the maximization of an objective function such as the expected reward in reinforcement learning theory. Our approach is based on information geometry and aims to reduce the number of model parameters with the hope to improve gradient learning processes.

Keywords

  • Extreme Point
  • Reinforcement Learning
  • Exponential Family
  • Hamilton Path
  • Deterministic Function

These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Correspondence to Nihat Ay .

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© 2013 Springer Science+Business Media Dordrecht

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Ay, N., Montúfar, G., Rauh, J. (2013). Selection Criteria for Neuromanifolds of Stochastic Dynamics. In: Yamaguchi, Y. (eds) Advances in Cognitive Neurodynamics (III). Springer, Dordrecht. https://doi.org/10.1007/978-94-007-4792-0_20

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