Limited Discrepancy AND
Authors: Javier Larrosa, Emma Rollon, Rina Dechter
IJCAI 2016 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | In this section we report results comparing LDS vs LDSAO as any-time schemes in the min-sum problem of Graphical Models. and Figure 3 shows any-time plots for one instance from each benchmark (note the logarithmic scale of time). |
| Researcher Affiliation | Academia | Javier Larrosa, Emma Rollon UPC Barcelona Tech Barcelona, Spain Rina Dechter University of California, Irvine Irvine, California, USA |
| Pseudocode | Yes | Algorithm 1: LDS and Algorithm 2: LDSAO |
| Open Source Code | No | The paper does not include an unambiguous statement or a direct link to the source code for the LDSAO methodology described. |
| Open Datasets | Yes | Instances have been taken from http://genoweb.toulouse.inra.fr/ degivry /evalgm and http://bioinfo.cs.technion.ac.il/superlink. |
| Dataset Splits | No | The paper focuses on optimization algorithms run on problem instances and does not describe explicit training, validation, or test dataset splits in the typical machine learning sense. |
| Hardware Specification | No | The paper mentions 'CPU time' as a metric but does not provide any specific hardware details such as GPU/CPU models or system configurations used for the experiments. |
| Software Dependencies | No | The paper mentions the Mini-Bucket-Elimination heuristic but does not provide specific software names with version numbers (e.g., libraries, solvers, or programming language versions) used for the experiments. |
| Experiment Setup | Yes | In the experiments the two algorithms ran with the same i-bound (10 for all benchmarks except for Linkage and Type4 pedigree where it was set to 15 and 16, respectively). |