DeepMath - Deep Sequence Models for Premise Selection
  We study the effectiveness of neural sequence models for premise selection in automated theorem proving, one of the main bottlenecks in the formalization of mathematics. We propose a two stage approach for this task that yields good results for the premise selection task on the Mizar corpus while avoiding the hand-engineered features of existing state-of-the-art models. To our knowledge, this is the first time deep learning has been applied to theorem proving on a large scale. 
Alex A. Alemi, Francois Chollet, Niklas Een, Geoffrey Irving,   Christian Szegedy and Josef Urban
cs.AI cs.LG cs.LO