Inferning with High Girth Graphical Models
Authors: Uri Heinemann, Amir Globerson
ICML 2014 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Results on synthetic data show that the models we learn indeed outperform those obtained by other algorithms, which do not return high girth graphs. |
| Researcher Affiliation | Academia | Uri Heinemann URIHEI@CS.HUJI.AC.IL The Hebrew University of Jerusalem, Jerusalem, Israel Amir Globerson GAMIR@CS.HUJI.AC.IL The Hebrew University of Jerusalem, Jerusalem, Israel |
| Pseudocode | Yes | Algorithm 1 Extended Chow Liu |
| Open Source Code | No | The paper does not provide an explicit statement about releasing its source code or a link to a code repository for the methodology described. |
| Open Datasets | No | The paper uses 'synthetic data' that was generated by the authors, with details on how it was generated ('starting with a random tree structure and then adding random edges', 'parameters hi were drawn from a uniform distribution', 'parameters Jij were drawn from a uniform distribution'), but does not provide access information (link, citation, etc.) to this data. |
| Dataset Splits | No | The paper refers to a 'training sample' and evaluates models based on '100 random queries' from synthetic data, but it does not specify explicit train/validation/test dataset splits with percentages, sample counts, or specific predefined split methodologies. |
| Hardware Specification | No | The paper does not provide any specific details regarding the hardware used for running the experiments, such as GPU or CPU models, or cloud computing specifications. |
| Software Dependencies | No | The paper describes algorithms like 'loopy belief propagation' and 'Chow Liu algorithm' but does not specify any software dependencies with version numbers used for the implementation or experiments. |
| Experiment Setup | Yes | All the models considered have p = 20 variables, so as to allow exact inference for comparisons. The underlying graphs were constrained to have a girth of g = 8. [...] The field parameters hi were drawn from a uniform distribution on [ 0.1, 0.1]. The scale of the interaction parameters Jij varied, as described next. [...] The parameters Jij were drawn from a uniform distribution on [ 1.1, 1.1]. [...] the number of samples is always n = 3200. |