Another name for the weight initialization method commonly referred to as Glorot is:

The weight initialization method commonly referred to as Glorot is also known as Xavier Initialization. It was introduced by Xavier Glorot and Yoshua Bengio in their paper on deep learning. The primary purpose of this initialization method is to maintain a balanced variance across layers in a neural network during training.

Xavier Initialization specifically sets the weights of the neural network based on the number of input and output neurons in a layer, using a uniform or normal distribution. This approach helps in preventing the activation functions from saturating and allows for smoother gradients, which is crucial for effective training of deeper networks.

The name "Glorot" comes from the lead author's surname, while "Xavier" is used interchangeably, referring to the same technique they developed. This method is especially popular when using activation functions like sigmoid or hyperbolic tangent, where maintaining the variance is critical for convergence.

The other weight initialization methods, such as He Initialization, are named for different strategies suited for specific activation functions like ReLU. However, they do not refer to the same approach as Xavier Initialization, reinforcing the distinction between these methodologies in neural network training.