Notice: The reproducibility variables underlying each score are classified using an automated LLM-based pipeline, validated against a manually labeled dataset. LLM-based classification introduces uncertainty and potential bias; scores should be interpreted as estimates. Full accuracy metrics and methodology are described in Coakley et alK. L. Coakley, T. Snelleman, H. Hoos, and O. E. Gundersen, "The embrace of open science: An analysis of a decade of AI research and 56 800 conference papers," Under Review, 2026..
Sliced Wasserstein Kernel for Persistence Diagrams
Authors: Mathieu Carrière, Marco Cuturi, Steve Oudot
ICML 2017 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | In this section, we compare k SW to k PSS and k PWG on several benchmark applications for which PDs have been proven useful. We compare these kernels in terms of classification accuracies and computational cost. |
| Researcher Affiliation | Academia | 1INRIA Saclay 2CREST, ENSAE, Universit e Paris Saclay. |
| Pseudocode | Yes | Algorithm 1 Computation of SWM |
| Open Source Code | No | The paper references LIBSVM: 'Software available at http://www.csie.ntu.edu. tw/ cjlin/libsvm.' but does not provide a direct link to the source code for the methodology described in this paper. |
| Open Datasets | Yes | We use some categories of the mesh segmentation benchmark of Chen et al. (Chen et al., 2009), which contains 3D shapes classified in several categories ( airplane , human , ant ...). |
| Dataset Splits | Yes | The cost factor C is cross-validated in the following grid: {0.001, 0.01, 0.1, 1, 10, 100, 1000}. |
| Hardware Specification | Yes | results are averaged over 10 runs on a 2.4GHz Intel Xeon E5530 Quad Core. |
| Software Dependencies | No | The paper mentions 'LIBSVM (Chang & Lin, 2011)' and 'The GUDHI Project (The GUDHI Project, 2015)' but does not specify version numbers for these software components. |
| Experiment Setup | Yes | The cost factor C is cross-validated in the following grid: {0.001, 0.01, 0.1, 1, 10, 100, 1000}. |