  This paper addresses the topic of sparsifying deep neural networks (DNN's).
While DNN's are powerful models that achieve state-of-the-art performance on a
large number of tasks, the large number of model parameters poses serious
storage and computational challenges. To combat these difficulties, a growing
line of work focuses on pruning network weights without sacrificing
performance. We propose a general affine scaling transformation (AST) algorithm
to sparsify DNN's. Our approach follows in the footsteps of popular sparse
recovery techniques, which have yet to be explored in the context of DNN's. We
describe a principled framework for transforming densely connected DNN's into
sparsely connected ones without sacrificing network performance. Unlike
existing methods, our approach is able to learn sparse connections at each
layer simultaneously, and achieves comparable pruning results on the
architecture tested.

Machine Learning