Research map / Probabilistic and scientific modeling / Variational and generative sampling
Fast Relational Probabilistic Inference and Learning: Approximate Counting via Hypergraphs
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Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| approximate counts with MACH hypergraph summaries instead of exact counts | MLN-Boost · Citeseer | learning time (s): 42499 -> 10837 | Helped |
| approximate counts with MACH instead of exact counts | MLN-Boost · UWCSE | AUC-ROC: 0.998 -> 0.981 | No clear effect |
| approximate counts with MACH instead of exact counts | MLN-Boost · UWCSE | learning time (s): 27.5 -> 13.2 | Helped |
| represent n-ary relations faithfully (MACH) instead of decomposing into binary relations (FACT) (ablation) | FACT · UWCSE | AUC-ROC: 0.500 -> 0.981 | Helped |
| represent n-ary relations faithfully (MACH) instead of decomposing into binary relations (FACT) (ablation) | FACT · Citeseer | AUC-ROC: 0.97 -> 0.998 | Helped |
| represent n-ary relations faithfully (MACH) instead of decomposing into binary relations (FACT) (ablation) | FACT · Carcinogenesis | AUC-ROC: 0.500 -> 0.525 | Helped |
| use MACH instead of Tuffy for inference | Tuffy · UW-CSE | inference time (s): 37.11 -> 20.06 | Helped |
| use MACH instead of Tuffy for inference | Tuffy · Carcinogenesis | inference time (s): 944.98 -> 199.8 | Helped |
| use MACH instead of Tuffy for inference | Tuffy · Carcinogenesis | AUC-ROC: 0.560 -> 0.524 | No clear effect |
| approximate counts with MACH instead of exact counts | MLN-Boost · NELL-Sports | CLL: -0.55 -> -1.43 | Hurt |
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