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The model then used this observation to update its estimate of the variance using a fixed learning rate; the model is essentially a linear filter (see Appendix S1).
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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.
We tested whether the rats considered uncertainty during reward estimation and whether the rats utilized an uncertainty-dependent time-varying learning rate or a fixed learning rate.
In order to analyze the impact of active expectancies on sequential effects, we used a fixed sequence learning task.
Each connection uses a fixed modulation.
First it uses a fixed tuned wavelength converter.
We used fixed learning rate which was 0.01.
The experiments were conducted using a fix-bed reactor.
We used a fixed-effects model.
So we used a fixed-effects model.
The batch update strategy allows the use of a fixed, but small, learning rate η t, which eliminates the problem of the weights getting stuck because the learning rate η t was decreased too quickly.
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