We draw random binary vectors with P (1) = 1 − δ as in adversarial learning, but deletions are only effective if ξ j = 0 and the weight w j is more than a standard deviation (σ ||w ||) from the mean of the current absolute weight distribution (µ ||w ||).
we only delete the predictive features, with predictivity being relative to the current mean weight.