Research map / Learning theory and algorithms / Combinatorial and search optimization
Linear Time Computation of Moments in Sum-Product Networks
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Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| Use cached forward values and backward derivatives to compute all edge moments in two passes | SPN moment computation on general DAGs · General DAG-structured SPNs | moment computation time complexity: O(|\mathcal{S}|^2) -> O(|\mathcal{S}|) | Helped |
| Use the linear-time moment computation algorithm as a subroutine | BMM · General SPNs | time complexity: quadratic to linear | Helped |
| Use CCCP instead of ADF or BMM for parameter learning | SPN online learning · SPNs | log-likelihood | Mixed |
Closest papers by meaning
- Bayesian Learning of Sum-Product Networks (NeurIPS 2019 · similarity 0.90)
- Maximum A Posteriori Inference in Sum-Product Networks (AAAI 2018 · similarity 0.89)
- Sum-Product-Set Networks: Deep Tractable Models for Tree-Structured Graphs (ICLR 2024 · similarity 0.86)
- A Few Moments Please: Scalable Graphon Learning via Moment Matching (NeurIPS 2025 · similarity 0.84)
- Tractable Probabilistic Graph Representation Learning with Graph-Induced Sum-Product Networks (ICLR 2024 · similarity 0.84)
- Inferring Dynamic Networks from Marginals with Iterative Proportional Fitting (ICML 2024 · similarity 0.84)
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