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.