Research map / Learning theory and algorithms / Prediction and decision losses
A Unified Approach for Learning the Parameters of Sum-Product Networks
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Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| Add CCCP fine-tuning of parameters after LearnSPN structure learning | LearnSPN · 20 binary density estimation benchmarks (Table 2) | average test log-likelihood | Helped |
| Use CCCP (concave-convex procedure, which equals EM here) instead of PGD, EG, or SMA for parameter learning | SPN parameter learning · 20 training datasets (convergence curves) | training negative log-likelihood vs iterations | Helped |
| Use EG (multiplicative update) instead of PGD | SPN parameter learning · 20 training datasets | training log-likelihood stability | Hurt |
| Use SMA instead of PGD and EG | SPN parameter learning · 20 training datasets | training log-likelihood stability and quality | Helped |
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