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Tuesday, June 14, 2016

DeepMath - Deep Sequence Models for Premise Selection. (arXiv:1606.04442v1 [cs.AI])

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.



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