On the Effectiveness of Persistent Homology
Authors: Renata Turkes, Guido F. Montufar, Nina Otter
NeurIPS 2022 | Conference PDF | Archive PDF | Plain Text | 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 renata.turkes@uantwerpen.be Guido Montúfar University of California, Los Angeles montufar@math.ucla.edu Nina Otter Queen Mary University of London n.otter@qmul.ac.uk |
| 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}. |