Year: 2016
Type: preprint
Abstract: 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 on a large scale.
Source: arXiv (Cornell University)
Authors: Alemi, Alex A., Chollet, Francois, Een, Niklas, Irving, Geoffrey, Szegedy, Christian, Urban, Josef
Language: English
Subfield: Computational Theory and Mathematics
Field: Computer Science
Domain: Physical Sciences
Open Access status: green
OpenAlex ID: W2428112462