  We develop a regularization method which finds flat minima during the
training of deep neural networks and other machine learning models. These
minima generalize better than sharp minima, allowing models to better
generalize to real word test data, which may be distributed differently from
the training data. Specifically, we propose a method of regularized
optimization to reduce the spectral radius of the Hessian of the loss function.
Additionally, we derive algorithms to efficiently perform this optimization on
neural networks and prove convergence results for these algorithms.
Furthermore, we demonstrate that our algorithm works effectively on multiple
real world applications in multiple domains including healthcare. In order to
show our models generalize well, we introduce different methods of testing
generalizability.
