Automated Theorem Proving On Holstep
Metrics
Classification Accuracy
Results
Performance results of various models on this benchmark
Model Name | Classification Accuracy | Paper Title | Repository |
|---|---|---|---|
| Siamese 1D CNN | 0.82 | HolStep: A Machine Learning Dataset for Higher-order Logic Theorem Proving | - |
| FormulaNet-basic | 0.891 | Premise Selection for Theorem Proving by Deep Graph Embedding | - |
| MPNN-DagLSTM | 0.916 | Improving Graph Neural Network Representations of Logical Formulae with Subgraph Pooling | - |
| FormulaNet | 0.903 | Premise Selection for Theorem Proving by Deep Graph Embedding | - |
| Siamese 1D CNN-LSTM | 0.83 | HolStep: A Machine Learning Dataset for Higher-order Logic Theorem Proving | - |
0 of 5 row(s) selected.