What would be a essentials only reading list for the newer supervised deep learning techniques?

I've heard there is a recent surge in purely supervised techniques in deep learning, in contrast to earlier pretraining based models.

Which would be the essential techniques and architectures that characterize this ideology?

And which papers would you recommend for someone familier with older work (RBMs,Autoencoders,CNN etc.) to get up to speed quickly on this?