Title: On Iterative Neural Network Pruning, Reinitialization, and the Similarity of Masks. Abstract: We examine how recently documented, fundamental phenomena in deep learn-ing 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 un-structured 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.