What neural networks learn depends fundamentally on the geometry of the underlying loss
function. We study how different regularizers affect the geometry of this function. One of the …

Recently, optimal time variable learning in deep neural networks was introduced in Antil et
al. In this manuscript we extend the concept by introducing a regularization term that directly …