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Daniel Whalen

Abstract
I propose a system for Automated Theorem Proving in higher order logic using deep learning and eschewing hand-constructed features. Holophrasm exploits the formalism of the Metamath language and explores partial proof trees using a neural-network-augmented bandit algorithm and a sequence-to-sequence model for action enumeration. The system proves 14% of its test theorems from Metamath's set.mm module.
Code Repositories
justin941208/SPIA-Project
Mentioned in GitHub
dwhalen/holophrasm
Official
Mentioned in GitHub
jinpz/refactor
pytorch
Mentioned in GitHub
giomasce/mmpp
Mentioned in GitHub
Benchmarks
| Benchmark | Methodology | Metrics |
|---|---|---|
| automated-theorem-proving-on-metamath-setmm | Holophrasm | Percentage correct: 14.3 |
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