  $L_p$-norm regularization schemes such as $L_0$, $L_1$, and $L_2$-norm
regularization and $L_p$-norm-based regularization techniques such as weight
decay, LASSO, and elastic net compute a quantity which depends on model weights
considered in isolation from one another. This paper introduces a regularizer
based on minimizing a novel measure of entropy applied to the model during
optimization. In contrast with $L_p$-norm-based regularization, this
regularizer is concerned with the spatial arrangement of weights within a
weight matrix. This novel regularizer is an additive term for the loss function
and is differentiable, simple and fast to compute, scale-invariant, requires a
trivial amount of additional memory, and can easily be parallelized.
Empirically this method yields approximately a one order-of-magnitude
improvement in the number of nonzero model parameters required to achieve a
given level of test accuracy when training LeNet300 on MNIST.
