Research map / Probabilistic and scientific modeling / Variational and generative sampling
Variational Inference with Locally Enhanced Bounds for Hierarchical Models
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Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| Apply importance weighting separately to each group's local variables instead of to the full model | Variational inference for hierarchical models · Synthetic hierarchical datasets | final lower bound | Helped |
| Apply importance weighting separately to each group's local variables instead of to the full model | Variational inference for hierarchical models · MovieLens100K | final lower bound | Helped |
| Apply uncorrected Hamiltonian annealing separately to each group's local variables instead of to the full model | Variational inference for hierarchical models · Synthetic hierarchical datasets | test log-likelihood | No clear effect |
| Apply uncorrected Hamiltonian annealing separately to each group's local variables instead of to the full model | Variational inference for hierarchical models · MovieLens100K | final lower bound | No clear effect |
| Use a global importance-weighted objective rather than applying importance weighting per group | Variational inference for hierarchical models · Synthetic hierarchical datasets | test log-likelihood | Hurt |
| Use a global importance-weighted objective rather than applying importance weighting per group | Variational inference for hierarchical models · MovieLens100K | final lower bound | Hurt |
| Increase the number of importance-weighted samples K | Locally-enhanced bounds · Synthetic hierarchical datasets | final lower bound | Helped |
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