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Selection Criteria for Neuromanifolds of Stochastic Dynamics
Nihat Ay, Guido Montúfar and Johannes Rauh
We present ways of deﬁning neuromanifolds – models of stochastic matrices – that are compatible with the maximization of an objective function (reward in reinforcement learning, predictive information in robotics, information ﬂow in neural networks). Our approach is based on information geometry and aims at the reduction of model parameters with the hope to improve gradient learning processes. We discuss advantages and shortcomings of this approach.
Selection criteria for neuromanifolds of stochastic dynamics
In: Advances in cognitive neurodynamics III : proceedings of the 3rd International Conference on Cognitive Neurodynamics 2011 ; [June 9-13, 2011, Hilton Niseko Village, Hokkaido, Japan] / Yoko Yamaguchi (ed.) Dordrecht : Springer, 2013. - pp. 147-154 (Advances in cognitive neurodynamics)