The method described does not explicitly compute the full Hessian matrix. Instead, it derives the top eigenvalues and eigenvectors of the Hessian. The implementation accumulates a large batch from a dataloader by concatenating n_batches of the typical batch size. This is an approximation to estimate the genuine loss/​gradient on the complete dataset more closely. If you have a large and high-variance dataset, averaging gradients over multiple batches might be better. This is because the loss calculated from a single, accumulated batch may not be adequately representative of the entire dataset’s true loss.
The method described does not explicitly compute the full Hessian matrix. Instead, it derives the top eigenvalues and eigenvectors of the Hessian. The implementation accumulates a large batch from a dataloader by concatenating
n_batchesof the typical batch size. This is an approximation to estimate the genuine loss/​gradient on the complete dataset more closely. If you have a large and high-variance dataset, averaging gradients over multiple batches might be better. This is because the loss calculated from a single, accumulated batch may not be adequately representative of the entire dataset’s true loss.