2016
DeepMath - Deep Sequence Models for Premise Selection
NeurIPS 2016poster
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 t…