The summary illustrates another point - the overwhelming majority of DL applications are on visual and sound datasets. Outside of the Facebook/Google bubble, images and sounds are not that important. On Kaggle, for example, gradient boosted trees tend to dominate the structured data challenges, but the gradient boosted tree "revolution" is happening unnoticed.
I think the combination of a) more processed datasets than google/Facebook can sometimes have, b) smaller cpu/gpu resources that google/Facebook have, and c) the incentive to squeeze every last bit of log loss out, all encourages you to ensable all your independently trained models under a gradient tree.
