Research map / Probabilistic and scientific modeling / Variational and generative sampling
Collapsed Variational Inference for Sum-Product Networks
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Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| Collapsed variational Bayes (CVB-SPN) instead of maximum likelihood (MLE-SPN) for batch parameter learning | SPN parameter learning · 20 benchmark data sets (batch) | average test log-likelihood | Helped |
| Collapsed variational inference in online setting (OCVB-SPN) instead of online MLE (OMLE-SPN) | SPN online parameter learning · 20 benchmark data sets (online) | average test log-likelihood | Helped |
| Collapsed variational inference (OCVB-SPN) instead of online Bayesian moment matching (OBMM) | SPN online parameter learning · 20 benchmark data sets (online) | average test log-likelihood | Mixed |
Closest papers by meaning
- Maximum A Posteriori Inference in Sum-Product Networks (AAAI 2018 · similarity 0.92)
- Bayesian Learning of Sum-Product Networks (NeurIPS 2019 · similarity 0.91)
- Probabilistic Circuits for Variational Inference in Discrete Graphical Models (NeurIPS 2020 · similarity 0.91)
- A Unified Approach for Learning the Parameters of Sum-Product Networks (NeurIPS 2016 · similarity 0.90)
- Annealing in variational inference mitigates mode collapse: a theoretical study on Gaussian mixtures (NeurIPS 2026 · similarity 0.85)
- Training Bayesian Neural Networks with Sparse Subspace Variational Inference (ICLR 2024 · similarity 0.85)
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