Which of the following is a weight initialization method?

The Xavier weight initialization method, also known as Glorot initialization, is a technique designed to set the initial weights of a neural network in a way that helps facilitate effective training. It aims to keep the scale of the gradients flowing through the network similar across all layers, which can help prevent issues related to vanishing or exploding gradients during the training process. Specifically, Xavier initialization generates weights by drawing them from a distribution with a mean of zero and a variance that depends on the number of input and output units in the layer. This helps maintain a stable gradient magnitude, allowing the network to learn more efficiently.

In contrast, the other options listed are not weight initialization methods. Batch Normalization is a technique used to normalize inputs to a layer in neural networks to improve training stability and speed. Gradient Clipping is a strategy to prevent the exploding gradient problem by limiting the size of the gradients during backpropagation. Dropout is a regularization technique that randomly sets a fraction of the neurons to zero during training to prevent overfitting. Each of these techniques serves different purposes in the training process but does not pertain to how weights are initialized within a model.