DeepMath - Deep Sequence Models for Premise Selection

neural information processing systems, Volume abs/1606.04442, 2016, Pages 2235-2243.

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Other Links: dblp.uni-trier.de|academic.microsoft.com|arxiv.org

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 ...More

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