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Found 3 papers, 1 papers with code
Mathematical Reasoning in Latent Space
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.
Learning to Reason in Large Theories without Imitation
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
HOList: An Environment for Machine Learning of Higher-Order Theorem Proving
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
