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..
Persistence Bag-of-Words for Topological Data Analysis
Authors: Bartosz Zieliński, Michał Lipiński, Mateusz Juda, Matthias Zeppelzauer, Paweł Dłotko
IJCAI 2019 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Comprehensive experiments show that the new representation achieves state-of-the-art performance and beyond in much less time than alternative approaches.Sections 5 and 6 present experimental setup, results and discussion. |
| Researcher Affiliation | Academia | 1The Institute of Computer Science and Computer Mathematics, Faculty of Mathematics and Computer Science, Jagiellonian University 2Media Computing Group, Institute of Creative Media Technologies, St. P olten University of Applied Sciences, 3Department of Mathematics and Swansea Academy of Advanced Computing, Swansea University |
| Pseudocode | No | The paper does not contain any clearly labeled pseudocode or algorithm blocks. Method steps are described in prose and mathematical formulas. |
| Open Source Code | Yes | The code of our experiments is implemented in Matlab9. [Footnote 9: https://github.com/bziiuj/pcodebooks] |
| Open Datasets | Yes | we evaluate all approaches on a set of synthetically generated shape classes from [Adams et al., 2017]. ...real-world datasets for geometry-informed material recognition (Geo Mat) [De Gol et al., 2016], classification of social network graphs (reddit-5k, reddit-12k) [Hofer et al., 2017], analysis of 3D surface texture (Petro Surf3D) [Zeppelzauer et al., 2017], and 3D shape segmentation [Carri ere et al., 2017]. |
| Dataset Splits | Yes | To enhance the comparability we employ (if available) the original train/test division of the datasets. To find optimal parameters for each evaluated approach, we run a grid search including cross-validation over the hyperparameters of all approaches (see Table 1 in SMa). |
| Hardware Specification | No | The paper does not explicitly describe the specific hardware (e.g., CPU/GPU models, memory) used for running the experiments. |
| Software Dependencies | No | The code of our experiments is implemented in Matlab9. For bag-of-words we employ the VLFeat library [Vedaldi and Fulkerson, 2008]. No version numbers are provided for these software dependencies. |
| Experiment Setup | Yes | To find optimal parameters for each evaluated approach, we run a grid search including cross-validation over the hyperparameters of all approaches (see Table 1 in SMa). |