  What enables Stochastic Gradient Descent (SGD) to achieve better
generalization than Gradient Descent (GD) in Neural Network training? This
question has attracted much attention. In this paper, we study the distribution
of the Stochastic Gradient Noise (SGN) vectors during the training. We observe
that for batch sizes 256 and above, the distribution is best described as
Gaussian at-least in the early phases of training. This holds across data-sets,
architectures, and other choices.
