Selection Criteria for Neuromanifolds of Stochastic Dynamics
نویسندگان
چکیده
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 at the reduction of model parameters with the hope to improve gradient learning processes.
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