Which method is utilized to normalize gradients when they exceed a certain threshold?

The method that normalizes gradients when they exceed a certain threshold is known as gradient clipping. This technique is widely used in training deep learning models to prevent issues such as exploding gradients, which can occur during the optimization process. When gradients are too large, they can lead to instability in the training, resulting in erratic updates or even divergence from the optimal solution.

Gradient clipping addresses this by setting a predefined threshold. If the magnitude of a gradient exceeds this threshold, the gradient is scaled down to ensure it remains within the acceptable range. This allows training to continue smoothly without drastic jumps that could hinder convergence.

Other options like weight decay, batch normalization, and learning rate scheduling serve different purposes in the training process. Weight decay is used for regularization, batch normalization helps in stabilizing and accelerating training by normalizing the inputs to layers, and learning rate scheduling adjusts the learning rate during training to improve convergence. However, none of these methods specifically target the problem of excessive gradient values, making gradient clipping the correct choice for normalizing gradients beyond a certain threshold.