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no code implementations • ICLR 2021 • Markus N. Rabe, Dennis Lee, Kshitij Bansal, Christian Szegedy

We examine whether self-supervised language modeling applied to mathematical formulas enables logical reasoning.

no code implementations • ICLR 2020 • Dennis Lee, Christian Szegedy, Markus N. Rabe, Sarah M. Loos, Kshitij Bansal

We design and conduct a simple experiment to study whether neural networks can perform several steps of approximate reasoning in a fixed dimensional latent space.

no code implementations • 25 May 2019 • Kshitij Bansal, Christian Szegedy, Markus N. Rabe, Sarah M. Loos, Viktor Toman

Our experiments show that the theorem prover trained with this exploration mechanism outperforms provers that are trained only on human proofs.

Ranked #3 on Automated Theorem Proving on HOList benchmark

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

3 code implementations • 5 Apr 2019 • Kshitij Bansal, Sarah M. Loos, Markus N. Rabe, Christian Szegedy, Stewart Wilcox

We present an environment, benchmark, and deep learning driven automated theorem prover for higher-order logic.

Ranked #2 on Automated Theorem Proving on HOList benchmark

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