  We describe a very large improvement of existing hammer-style proof
automation over large ITP libraries by combining learning and theorem proving.
In particular, we have integrated state-of-the-art machine learners into the E
automated theorem prover, and developed methods that allow learning and
efficient internal guidance of E over the whole Mizar library. The resulting
trained system improves the real-time performance of E on the Mizar library by
70% in a single-strategy setting.
