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Selection Criteria for Neuromanifolds of Stochastic Dynamics

  • Nihat Ay
  • Guido Montúfar
  • Johannes Rauh
Conference paper

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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Copyright information

© Springer Science+Business Media Dordrecht 2013

Authors and Affiliations

  1. 1.Max Planck Institute for Mathematics in the SciencesLeipzigGermany
  2. 2.Santa Fe InstituteSanta FeUSA

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