  We introduce a new method for speeding up the inference of deep neural
networks. It is somewhat inspired by the reduced-order modeling techniques for
dynamical systems.The cornerstone of the proposed method is the maximum volume
algorithm. We demonstrate efficiency on neural networks pre-trained on
different datasets. We show that in many practical cases it is possible to
replace convolutional layers with much smaller fully-connected layers with a
relatively small drop in accuracy.
