ATPboost: Learning Premise Selection in Binary Setting with ATP Feedback

9 Feb 2018 Bartosz Piotrowski Josef Urban

ATPboost is a system for solving sets of large-theory problems by interleaving ATP runs with state-of-the-art machine learning of premise selection from the proofs. Unlike many previous approaches that use multi-label setting, the learning is implemented as binary classification that estimates the pairwise-relevance of (theorem, premise) pairs... (read more)

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