  Given the ever-increasing size of modern neural networks, the significance of
sparse architectures has surged due to their accelerated inference speeds and
minimal memory demands. When it comes to global pruning techniques, Iterative
Magnitude Pruning (IMP) still stands as a state-of-the-art algorithm despite
its simple nature, particularly in extremely sparse regimes. In light of the
recent finding that the two successive matching IMP solutions are linearly
connected without a loss barrier, we propose Sparse Weight Averaging with
Multiple Particles (SWAMP), a straightforward modification of IMP that achieves
performance comparable to an ensemble of two IMP solutions. For every
iteration, we concurrently train multiple sparse models, referred to as
particles, using different batch orders yet the same matching ticket, and then
weight average such models to produce a single mask. We demonstrate that our
method consistently outperforms existing baselines across different sparsities
through extensive experiments on various data and neural network structures.
