It is true that MLPs are classic, but the regularizaton methods that apparently make a big empirical difference at this paper are new concepts (data augmentation, skip connections/residual blocks, dropout, batch norm, lookahead, stochastic weight averaging, etc.). They compare againts a good old MLP without the bells and whistles at Table 2 and the classic MLP is quite a poor performer (XGBoost beats a classical MLP very significantly). Which leads to the conclusion that we need all these recent deep learning advances on innovative regularization techniques to make the difference.