no code implementations • 24 May 2019 • Aditya Paliwal, Sarah Loos, Markus Rabe, Kshitij Bansal, Christian Szegedy
This paper presents the first use of graph neural networks (GNNs) for higher-order proof search and demonstrates that GNNs can improve upon state-of-the-art results in this domain.
Ranked #1 on Automated Theorem Proving on HOList benchmark
no code implementations • 24 Jan 2017 • Sarah Loos, Geoffrey Irving, Christian Szegedy, Cezary Kaliszyk
Here we suggest deep learning based guidance in the proof search of the theorem prover E. We train and compare several deep neural network models on the traces of existing ATP proofs of Mizar statements and use them to select processed clauses during proof search.