An Integer Polynomial Programming Based Framework for Lifted MAP Inference
Authors: Somdeb Sarkhel, Deepak Venugopal, Parag Singla, Vibhav G Gogate
NeurIPS 2014 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Experiments on several benchmark MLNs show that our new algorithm is substantially superior to ground inference and existing methods in terms of computational efficiency and solution quality. |
| Researcher Affiliation | Academia | Somdeb Sarkhel, Deepak Venugopal Computer Science Department The University of Texas at Dallas {sxs104721,dxv021000}@utdallas.edu Parag Singla Department of CSE I.I.T. Delhi parags@cse.iitd.ac.in Vibhav Gogate Computer Science Department The University of Texas at Dallas vgogate@hlt.utdallas.edu |
| Pseudocode | Yes | Algorithm 1 PTP-MAP(MLN M)... Algorithm 2 SMLN-2-IPP(SMLN S) |
| Open Source Code | No | The paper mentions using open-source software (Alchemy, Tuffy) and a commercial solver (Gurobi), but does not state that the code for their own proposed methodology is open source or publicly available. |
| Open Datasets | Yes | (iii) Citation Information-Extraction (IE) MLN [11] from the Alchemy web page, consisting of five predicates and fourteen formulas. |
| Dataset Splits | No | The paper does not provide specific dataset split information (e.g., percentages, sample counts, or detailed splitting methodology) for training, validation, or test sets. |
| Hardware Specification | Yes | All our experiments were run on a third generation i7 quad-core machine having 8GB RAM. |
| Software Dependencies | No | The paper mentions software like Gurobi, Alchemy, and Tuffy but does not specify their version numbers. |
| Experiment Setup | No | The paper describes the MLNs used for evaluation and the metrics recorded (solution quality vs. time), but it does not provide specific experimental setup details such as hyperparameters or specific training configurations for the models or algorithms beyond what is implied by the core problem (MAP inference). |