Splitting an LPMLN Program
Authors: Bin Wang, Zhizheng Zhang, Hongxiang Xu, Jun Shen
AAAI 2018 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | The preliminary experimental results show that these approaches are alternative ways to promote an LPMLN solver. |
| Researcher Affiliation | Academia | Bin Wang, Zhizheng Zhang, Hongxiang Xu, Jun Shen School of Computer Science and Engineering Southeast University, Nanjing 211189, China {kse.wang, seu zzz, xhx1693, junshen}@seu.edu.cn |
| Pseudocode | Yes | Algorithm 1: A Parallel LPMLN Solver: lpmln-sst; Algorithm 2: A Parallel LPMLN Solver: lpmln-idp |
| Open Source Code | No | The paper mentions existing LPMLN solvers like 'LPMLN-Models' that were used in their implementations, but it does not state that the code for the specific methodologies described in *this* paper (Algorithm 1 and 2) is open-source or provide a link. |
| Open Datasets | No | The paper refers to 'the program M in Example 5' and mentions 'b-n to denote the program with n birds'. This indicates an example LPMLN program used for evaluation, but no public dataset or concrete access information (link, DOI, formal citation) is provided for this program or any other dataset. |
| Dataset Splits | No | The paper discusses experimental results but does not provide specific details on dataset splits (e.g., train/validation/test percentages or counts) needed for reproduction. |
| Hardware Specification | Yes | These experiments were carried out on a Dell Power Edge R920 server with an Intel Xeon E7-4820@2.00GHz with 32 cores and 24 GB RAM running the Ubuntu 16.04 operating system. |
| Software Dependencies | No | The paper mentions 'Ubuntu 16.04 operating system' and that 'The LPMLN solver used in the implementations of Algorithm 1 and 2 was LPMLN-Models'. While the OS version is given, no specific version number is provided for LPMLN-Models or any other libraries or programming languages used in the implementation. |
| Experiment Setup | No | The paper mentions using 'at most 16 threads' for the implementations, which is a system setting. However, it does not provide specific details on hyperparameters, model initialization, or training schedules for the LPMLN solver itself that would typically be expected in an experimental setup description. |