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..
The decomposition of the higher-order homology embedding constructed from the $k$-Laplacian
Authors: Yu-Chia Chen, Marina Meila
NeurIPS 2021 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Lastly, we support our theoretical claims with numerous empirical results from point clouds and images. |
| Researcher Affiliation | Academia | Yu-Chia Chen Electrical & Computer Engineering University of Washington Seattle, WA 98195 EMAIL Marina Meila Department of Statistics University of Washington Seattle, WA 98195 EMAIL |
| Pseudocode | Yes | Algorithm 1: Subspace identification |
| Open Source Code | Yes | New codes are attached in the supplemental material codes.zip; they can also be found at https://github.com/yuchaz/homology_emb. |
| Open Datasets | Yes | RNA single-cell sequencing data [7]. |
| Dataset Splits | No | The paper does not explicitly provide training/test/validation dataset splits, such as specific percentages or sample counts for each split. |
| Hardware Specification | Yes | We perform our analysis on a desktop running Linux with 32GB RAM and an 8-Core 4.20GHz Intel Core i7-7700K CPU |
| Software Dependencies | No | The paper mentions tools and algorithms like "Infomax ICA [6]" and "Dijkstra", but does not specify version numbers for these or other software dependencies. |
| Experiment Setup | Yes | For all the point clouds, we build the VR complex SC from the Ck NN kernel [8] so that the resulting L1 is sparse and the topological information is preserved. [...] The cubical complex is constructed by intensity thresholding (also called the sub-level set method in TDA [58]) and then applying morphological closing on the binary image to remove small cavities. The weight for every rectangle w2(σ) is set to 1; [...] We chose to keep n1/β1 by treating each homology class equally, i.e., each class has roughly n1/β1 edges. |