Part of Advances in Neural Information Processing Systems 29 (NIPS 2016)
Geoffrey Irving, Christian Szegedy, Alexander A. Alemi, Niklas Een, Francois Chollet, Josef Urban
We study the effectiveness of neural sequence models for premise selection in automated theorem proving, a key bottleneck for progress in formalized 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 theorem proving on a large scale.