Several machine learning inspired methods for perturbation-based fiber
nonlinearity (PBNLC) compensation have been presented in recent literature. We
critically revisit acclaimed benefits of those over non-learned methods.
Numerical results suggest that learned linear processing of perturbation
triplets of PB-NLC is preferable over feedforward neural-network solutions