DeepMath - Deep Sequence Models for Premise Selection

http://papers.nips.cc/paper/6279-deepmath-deep-sequence-models-for-premise-selection.pdf

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.