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..
On the Effectiveness of Persistent Homology
Authors: Renata Turkes, Guido F. Montufar, Nina Otter
NeurIPS 2022 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Experiments demonstrate that PH is successful in these tasks, outperforming several baselines, including Point Net, an architecture inspired precisely by the properties of point clouds. |
| Researcher Affiliation | Academia | Renata Turkeš University of Antwerp EMAIL Guido Montúfar University of California, Los Angeles EMAIL Nina Otter Queen Mary University of London EMAIL |
| Pseudocode | No | The paper describes the 'PH pipeline' and its steps but does not include any formal pseudocode or algorithm blocks. |
| Open Source Code | Yes | All the code for data generation, analysis, and visualization is publicly available at https://github.com/renataturkes/on_the_effectiveness_of_persistent_homology. |
| Open Datasets | Yes | We provide data sets that can be directly used as a benchmark for our tasks or other related pointcloud-analysis or classification problems. |
| Dataset Splits | No | The paper specifies training and testing splits (e.g., 'train the classifier on 80%... and test on the remaining 20%'), but it does not explicitly provide details for a separate validation split. |
| Hardware Specification | Yes | The computational experiments were performed using a computer with 3.5 GHz 6-Core Intel Xeon E5 processor and 64 GB 1866 MHz DDR3 RAM. |
| Software Dependencies | Yes | The code is written in Python 3.8.10... The packages used are: gudhi (v.3.5.0), numpy (v.1.20.1), matplotlib (v.3.3.4), ripser (v.0.6.2), scikit-learn (v.0.24.1), scipy (v.1.6.2), sklearn (v.0.0). |
| Experiment Setup | Yes | SVMs with radial basis function (RBF) kernels were chosen for regression and classification tasks. The hyperparameters of the SVMs were optimized by grid search over values of C {0.1, 1, 10, 100} and γ {0.001, 0.01, 0.1, 1, 10, 100}. |