  We examine how recently documented, fundamental phenomena in deep learning
models subject to pruning are affected by changes in the pruning procedure.
Specifically, we analyze differences in the connectivity structure and learning
dynamics of pruned models found through a set of common iterative pruning
techniques, to address questions of uniqueness of trainable, high-sparsity
sub-networks, and their dependence on the chosen pruning method. In
convolutional layers, we document the emergence of structure induced by
magnitude-based unstructured pruning in conjunction with weight rewinding that
resembles the effects of structured pruning. We also show empirical evidence
that weight stability can be automatically achieved through apposite pruning
techniques.
