Search Results for author: Kshitij Bansal

Found 5 papers, 1 papers with code

Mathematical Reasoning in Latent Space

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.

Mathematical Reasoning

Learning to Reason in Large Theories without Imitation

no code implementations25 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.

Automated Theorem Proving Imitation Learning +1

Graph Representations for Higher-Order Logic and Theorem Proving

no code implementations24 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.

Automated Theorem Proving

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