A machine learning engineer is tuning a deep neural network using the Adam optimizer and observes that the training process has high variance in its parameter updates, leading to an unstable learning trajectory. To stabilize the training by adjusting the exponential decay rates for the moment estimates, the engineer needs to modify the hyperparameter that controls the decay rate for the first moment estimate (the mean of the gradients). Which of the following hyperparameters should the engineer adjust?
The correct answer is beta1. The Adam (Adaptive Moment Estimation) optimizer uses two key hyperparameters to control the exponential decay rates of its moment estimates. beta1 controls the decay rate for the first moment estimate, which is the moving average of the gradients and is analogous to momentum. beta2 controls the decay rate for the second moment estimate, the moving average of the squared gradients, which helps adapt the learning rate for each parameter. The learning rate (alpha) is the overall step size for the updates but does not control the decay rates of the moment estimates directly. epsilon is a small constant added for numerical stability to prevent division by zero, not a decay rate parameter.
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