[jimfleming]

Regarding your edit, the authors of the paper in question focus on FNNs and note the reason in the paper:

> Both RNNs and CNNs can stabilize learning via weight sharing, therefore they are less prone to these perturbations. In contrast, FNNs trained with normalization techniques suffer from these perturbations and have high variance in the training error (see Figure 1).

Essentially FNNs stand to benefit more from this work than CNNs or RNNs.