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Hsieh, H.-L. & Shanechi, M.M. Optimizing the learning rate for adaptive estimation of neural encoding models.
The learning rate for this neuron is set higher than the unlearning rate because one does not want to the robot it forget quickly its proximity from the light source and crash into it.
The outcomes of the analysis are quantitative criteria for selection of the width of the B-splines, and of the learning rate, for which the system is guaranteed to be stable.
ξ is the learning rate for Q-learning.
The learning rate for the weights was 0.03 and for biases was 0.1.
In Equation 25, ξ is the learning rate for Q-learning.
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In this article, the practicality of this type of learning rule is expanded by deriving a scaling equation for the learning rates for various network architectures.
In BE-AAPSA, the objective is to create a robust background estimation model where the learning rates for each pixel are calculated according to the results of the two adaptive weight arrays and the module where the video is classified.
The purpose of this paper is to show that the curve and the learning rates for an unperturbed system emerge from fundamental findings in quantum theory, non-equilibrium thermodynamics and cybernetics.
Table 1 shows the learning rates for these sessions expressed as percent error for all three monkeys at each of the four stages in the present experiment.
The learning rates for each of the four layers were 0.05, 0.03, 0.005, and 0.005, as these rates were shown to produce convergence of the synaptic weights after 15 50 training epochs.
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