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..
Towards a Persistence Diagram that is Robust to Noise and Varied Densities
Authors: Hang Zhang, Kaifeng Zhang, Kai Ming Ting, Ye Zhu
ICML 2023 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Our empirical evaluation reveals that the proposed filter function provides a better means for t-SNE visualization and SVM classification than three existing methods of TDA. |
| Researcher Affiliation | Academia | 1National Key Laboratory for Novel Software Technology, Nanjing University, Nanjing, China 2Centre for Cyber Resilience and Trust, Deakin University, Burwood, VIC, Australia. |
| Pseudocode | No | No pseudocode or algorithm blocks were found in the paper. |
| Open Source Code | Yes | 1The code of Λ-filter is available at https://github.com/IsolationKernel/Codes/tree/main/Lambda-kernel |
| Open Datasets | Yes | The dataset we used consists of 150 images (or point clouds C1, ..., C150) from 3 types of cells in tumor regions (Vipond et al., 2021) |
| Dataset Splits | Yes | In each split, we take 70% of the whole dataset for training and 30% for testing. 3-fold cross-validation on the training set is used to select the best hyperparameters for each approach |
| Hardware Specification | Yes | The experiments are performed on a machine with 1500MHz CPUs and 2TB RAM. |
| Software Dependencies | No | The paper mentions software like t-SNE, SVM, and kNN classifier but does not provide specific version numbers for these or any other software dependencies. |
| Experiment Setup | Yes | For Λ-kernel, t = 200, η = ∞, ψ is searched over {2, 4, 8, 16, 32}. For DTM and Ck NN, the k is searched in {m n|m = 0.02, 0.04, 0.06, 0.08, 0.1}, where n is the dataset size. |