Greedy Structure Search for Sum-Product Networks
Authors: Aaron Dennis, Dan Ventura
IJCAI 2015 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Instead we introduce a greedy structure search algorithm that learns DAG-structured SPNs and compare this with a structure learning algorithm that learns tree-structured SPNs. Empirical results provide evidence that DAG-SPNs have advantages over tree-SPNs.5 Experiments We compare SEARCHSPN and LEARNSPN (from GD) on twenty datasets that were recently used in Rooshenas and Lowd [2014] and Gens and Domingos [2013]. We also compare the algorithms on a set of artificially-generated datasets based on the permanent of an n n matrix. For each dataset we run a grid search over hyperparameter values γ {0.1, 0.3, 1.0, 3.0, 10.0} and τ {0.003, 0.01, 0.03, 0.1, 0.3}, the cluster penalty and pairwise dependency threshold, respectively. Chosen models are those with the highest likelihood on a validation set. Table 1 and Table 2 show the mean test set likelihood over ten runs. |
| Researcher Affiliation | Academia | Aaron Dennis and Dan Ventura Computer Science Department Brigham Young University Provo, UT 84602 adennis@byu.edu, ventura@cs.byu.edu |
| Pseudocode | Yes | Algorithm 1 MIXCLONES(p , S1, k)Algorithm 2 SEARCHSPN(N, T ) |
| Open Source Code | No | The paper does not provide an explicit statement or link to its open-source code. |
| Open Datasets | Yes | We compare SEARCHSPN and LEARNSPN (from GD) on twenty datasets that were recently used in Rooshenas and Lowd [2014] and Gens and Domingos [2013]. |
| Dataset Splits | No | Chosen models are those with the highest likelihood on a validation set. We stop when the likelihood of a validation set reaches a maximum. |
| Hardware Specification | No | The learning-time results are less definitive than the model-size results since some or all of the difference reported here could be due to differences in such factors as the hardware and programming language used in the experiments, and not due to differences in the algorithms. |
| Software Dependencies | No | The paper mentions 'programming language used in the experiments' but does not specify any software dependencies with version numbers. |
| Experiment Setup | Yes | For each dataset we run a grid search over hyperparameter values γ {0.1, 0.3, 1.0, 3.0, 10.0} and τ {0.003, 0.01, 0.03, 0.1, 0.3}, the cluster penalty and pairwise dependency threshold, respectively. |