  Recent work has explored the possibility of pruning neural networks at
initialization. We assess proposals for doing so: SNIP (Lee et al., 2019),
GraSP (Wang et al., 2020), SynFlow (Tanaka et al., 2020), and magnitude
pruning. Although these methods surpass the trivial baseline of random pruning,
they remain below the accuracy of magnitude pruning after training, and we
endeavor to understand why. We show that, unlike pruning after training,
randomly shuffling the weights these methods prune within each layer or
sampling new initial values preserves or improves accuracy. As such, the
per-weight pruning decisions made by these methods can be replaced by a
per-layer choice of the fraction of weights to prune. This property suggests
broader challenges with the underlying pruning heuristics, the desire to prune
at initialization, or both.
