Research map / Learning theory and algorithms / Combinatorial and search optimization
Scalable Training of Markov Logic Networks Using Approximate Counting
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Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| approximate counting with IJGP CD instead of Tuffy-MCSAT | CD · WebKB | CLL: −0.89 -> −0.66 | Helped |
| CD vs VP and PLL with approximate counting | CD · Smoker | CLL: −1.61 -> −0.695 | Helped |
| approximate counting makes learning feasible where Alchemy runs out of memory | CD with IJGP · WebKB, Protein, ER | CLL | Helped |
| higher IJGP i-bound (exact counts) vs i-bound 1 on Protein (ablation) | CD · Protein | CLL: -0.9948 -> -0.7797 | Helped |
| increase i-bound beyond 1 on WebKB and ER (ablation) | CD · WebKB and ER | convergence | Hurt |
| approximate vs exact counting at i-bound 2 vs 3 (ablation) | CD · Protein | CLL: -0.7797 -> -0.7795 | No clear effect |
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