We present a new method for structured pruning of neural networks, based on the recently proposed neuron merging trick in which following a pruning operation, the weights of the next layer are suitably modified. By a rigorous mathematical analysis of the neuron merging technique we prove an upper bound on the reconstruction error. This bound defines a new objective function for pruning-and-merging.

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