  Pruning is one of the major methods to compress deep neural networks. In this
paper, we propose an Ising energy model within an optimization framework for
pruning convolutional kernels and hidden units. This model is designed to
reduce redundancy between weight kernels and detect inactive kernels/hidden
units. Our experiments using ResNets, AlexNet, and SqueezeNet on CIFAR-10 and
CIFAR-100 datasets show that the proposed method on average can achieve a
pruning rate of more than $50\%$ of the trainable parameters with approximately
$<10\%$ and $<5\%$ drop of Top-1 and Top-5 classification accuracy,
respectively.
