Note that, at query time, ∇ (L; θ) should be nearly zero since converged at the previous round of training.
we can approximate ∇ (L + x,ŷ ; θ) ≈ ∇ ( x,ŷ ; θ) for computational efficiency, because the training instances are assumed to be independent.