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.
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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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DOI: https://doi.org/10.1007/978-94-007-4792-0_20
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