  Neural networks are easier to optimise when they have many more weights than
are required for modelling the mapping from inputs to outputs. This suggests a
two-stage learning procedure that first learns a large net and then prunes away
connections or hidden units. But standard training does not necessarily
encourage nets to be amenable to pruning. We introduce targeted dropout, a
method for training a neural network so that it is robust to subsequent
pruning. Before computing the gradients for each weight update, targeted
dropout stochastically selects a set of units or weights to be dropped using a
simple self-reinforcing sparsity criterion and then computes the gradients for
the remaining weights. The resulting network is robust to post hoc pruning of
weights or units that frequently occur in the dropped sets. The method improves
upon more complicated sparsifying regularisers while being simple to implement
and easy to tune.
