Search Results for author:
Found 5 papers, 1 papers with code
Mathematical Reasoning via Self-supervised Skip-tree Training
We examine whether self-supervised language modeling applied to mathematical formulas enables logical reasoning.
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
Graph Representations for Higher-Order Logic and Theorem Proving
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
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
