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The adaptation depends also in this case by a learning rate.
The magnitudes of these weight adjustments are controlled by a learning rate [11] which can be lowered over time so as to acquire finer movements in a later learning phase.
The size of this update is determined by a learning rate which we decreased from 1.0 to 0.001 over 500 steps.
Each process is characterized by a learning rate that controls how strongly the motor memory is updated based on errors and a retention factor determining the movement-to-movement retention of the motor memory.
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This problem has been noted by [ 18] and a solution is proposed by the imposition of a learning rate on the gradient descent form of the IRLS algorithm.
Training a simple classifier uses a learning rate of 0.001, reducing by 10% every 5000 iterations.
Here, the rate constant corresponds to a learning rate in conventional (reinforcement learning) formulations.
Using Equations 6, 7, 8, and 9, each parameter can be updated by stochastic gradient descent with a fixed learning rate and a momentum term.
Robustness to model parameter uncertainties is accomplished by optimizing the cost summed over models, by adjusting the learning rate as a function of frequency, and by relaxing the zero error requirement as a function of frequency.
We have also considered some additional weights of the network as free parameters, by applying fine tuning (a smaller learning rate value) to the weights of (some of) the convolutional layers of the ResNet network, while using a normal learning rate value for the FC part of it.
Then, for each parameter, its respective gradient is multiplied by the learning rate and added to it.
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