  Mirror descent is an elegant optimization technique that leverages a dual
space of parametric models to perform gradient descent. While originally
developed for convex optimization, it has increasingly been applied in the
field of machine learning. In this study, we propose a novel approach for
utilizing mirror descent to initialize the parameters of neural networks.
Specifically, we demonstrate that by using the Hopfield model as a prototype
for neural networks, mirror descent can effectively train the model with
significantly improved performance compared to traditional gradient descent
methods that rely on random parameter initialization. Our findings highlight
the potential of mirror descent as a promising initialization technique for
enhancing the optimization of machine learning models.
