Scaling-up Importance Sampling for Markov Logic Networks
Authors: Deepak Venugopal, Vibhav G Gogate
NeurIPS 2014 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Our experiments on several MLNs clearly demonstrate the promise of our approach. |
| Researcher Affiliation | Academia | Deepak Venugopal Department of Computer Science University of Texas at Dallas dxv021000@utdallas.edu Vibhav Gogate Department of Computer Science University of Texas at Dallas vgogate@hlt.utdallas.edu |
| Pseudocode | Yes | Algorithm 1: Compute-Marginals |
| Open Source Code | No | The paper does not provide concrete access to source code for the methodology described. It references the Alchemy system, which is a third-party tool. |
| Open Datasets | Yes | Our test MLNs include Smokers and HMM (with few states) from the Alchemy website [10] and two additional MLNs, Relation (R(x, y) S(y, z)), Log Req (randomly generated formulas with singletons). [10] S. Kok, M. Sumner, M. Richardson, P. Singla, H. Poon, D. Lowd, J. Wang, and P. Domingos. The Alchemy System for Statistical Relational AI. Technical report, Department of Computer Science and Engineering, University of Washington, Seattle, WA, 2008. http://alchemy.cs.washington.edu. |
| Dataset Splits | No | The paper mentions random setting of groundings as true or false (25% each) for MLNs, but it does not specify explicit training, validation, or test dataset splits in terms of percentages or counts required for reproduction. |
| Hardware Specification | Yes | We ran all experiments on a quad-core, 6GB RAM, Ubuntu laptop. |
| Software Dependencies | No | The paper does not provide specific software dependencies with version numbers. |
| Experiment Setup | Yes | For clustering, we used the scheme in [19] with KMeans++ as the clustering method. For Gibbs sampling, we set the thinning parameter to 5 and use a burn-in of 50 samples. |