Efficient Localized Inference for Large Graphical Models
Authors: Jinglin Chen, Jian Peng, Qiang Liu
IJCAI 2018 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We verify our theoretical bounds on various datasets and demonstrate that our localized inference algorithm can provide fast and accurate approximation for large graphical models. |
| Researcher Affiliation | Academia | 1 University of Illinois at Urbana-Champaign 2 University of Texas at Austin |
| Pseudocode | Yes | Algorithm 1 Greedy expansion algorithm for localized inference |
| Open Source Code | No | The paper mentions using the UGM Matlab package, a third-party tool, but does not provide access to its own source code. |
| Open Datasets | Yes | We perform experimental evaluations on the Cora data set2. Cora consists of a large collection of machine learning papers with citation relations between the papers, in which each paper is labeled as one of seven classes. 2https://people.cs.umass.edu/~mccallum/data.html |
| Dataset Splits | No | The paper describes the Cora dataset and how nodes are queried, but it does not provide specific details on training, validation, and test dataset splits with percentages or sample counts. |
| Hardware Specification | No | The paper does not provide any specific details regarding the hardware used for running the experiments. |
| Software Dependencies | No | The paper mentions using the 'UGM Matlab package' but does not provide a specific version number for this software dependency. |
| Experiment Setup | Yes | The parameters in the Ising model is generated by drawing hi uniformly from [ I1, I1] for all nodes i and Jij uniformly from [ I2, I2] for all edges i j . Here I1 and I2 control the locality and hardness of this Ising model. For this experiment, we fix I1 = 1 and I2 = 0.25 and average on 100 random trials. |