DeepMath - Deep Sequence Models for Premise Selection

NeurIPS 2016 Alex A. AlemiFrancois CholletNiklas EenGeoffrey IrvingChristian SzegedyJosef Urban

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... (read more)

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