Qualitative Reasoning about Directions in Semantic Spaces
Authors: Steven Schockaert, Jae Hee Lee
IJCAI 2015 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We have used CBC4 for solving the linear programs, with a timeout of 300 seconds; all reported values are the median of 50 executions. In Figures 3 and 4, we write rand, MDS and hyb to denote configurations where the initialisation used random values, MDS, or the hybrid method. We write uni, rand1 and rand2 to indicate whether the uniform objective or the randomised objective (18) was used, and in the latter case, whether we set N = 1 or N = 2. Finally, names ending with -R denote configurations where we reset the values of π(xi) after 250 iterations without improvement. For the results in Figure 3, we have considered a 20dimensional space of films, for which 40 directions have been obtained using the method from [Derrac and Schockaert, 2014a]. From this space, we generate EER instances with 40 rankings and a variable number of objects. In Figure 3 we observe that (i) MDS substantially outperforms random initialisations and performs better than the hybrid method for sufficiently small problem instances, (ii) N = 1 is better than N = 2, and (iii) the uniform objective outperforms the randomised objective. |
| Researcher Affiliation | Academia | Steven Schockaert Cardiff University Cardiff, UK s.schockaert@cs.cardiff.ac.uk Jae Hee Lee Australian National University Canberra, Australia jae-hee.lee@anu.edu.au |
| Pseudocode | No | The paper describes methods in textual and mathematical form but does not contain structured pseudocode or algorithm blocks. |
| Open Source Code | No | The paper does not provide any explicit statement or link indicating that the source code for the described methodology is publicly available. |
| Open Datasets | Yes | For the results in Figure 3, we have considered a 20dimensional space of films, for which 40 directions have been obtained using the method from [Derrac and Schockaert, 2014a]. |
| Dataset Splits | No | The paper mentions generating EER instances from a space of films and using the LR-LEFT-ALL benchmark, but it does not specify explicit train/validation/test dataset splits or percentages within the paper itself. |
| Hardware Specification | No | The paper does not provide specific hardware details (e.g., exact GPU/CPU models, processor types, or memory amounts) used for running its experiments. |
| Software Dependencies | Yes | We have used CBC4 for solving the linear programs... solvers such as QEPCAD2 and Redlog3 do not scale beyond trivial instances. |
| Experiment Setup | Yes | We have used CBC4 for solving the linear programs, with a timeout of 300 seconds; all reported values are the median of 50 executions. In Figures 3 and 4, we write rand, MDS and hyb to denote configurations where the initialisation used random values, MDS, or the hybrid method. We write uni, rand1 and rand2 to indicate whether the uniform objective or the randomised objective (18) was used, and in the latter case, whether we set N = 1 or N = 2. Finally, names ending with -R denote configurations where we reset the values of π(xi) after 250 iterations without improvement. |