  Pruning neural networks at initialization would enable us to find sparse
models that retain the accuracy of the original network while consuming fewer
computational resources for training and inference. However, current methods
are insufficient to enable this optimization and lead to a large degradation in
model performance. In this paper, we identify a fundamental limitation in the
formulation of current methods, namely that their saliency criteria look at a
single step at the start of training without taking into account the
trainability of the network. While pruning iteratively and gradually has been
shown to improve pruning performance, explicit consideration of the training
stage that will immediately follow pruning has so far been absent from the
computation of the saliency criterion. To overcome the short-sightedness of
existing methods, we propose Prospect Pruning (ProsPr), which uses
meta-gradients through the first few steps of optimization to determine which
weights to prune. ProsPr combines an estimate of the higher-order effects of
pruning on the loss and the optimization trajectory to identify the trainable
sub-network. Our method achieves state-of-the-art pruning performance on a
variety of vision classification tasks, with less data and in a single shot
compared to existing pruning-at-initialization methods.
