DeepMath - Deep Sequence Models for Premise Selection
Geoffrey Irving · Christian Szegedy · Alexander Alemi · Niklas Een · Francois Chollet · Josef Urban
2016 Poster
Abstract
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.
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