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..
Topologically Regularized Data Embeddings
Authors: Robin Vandaele, Bo Kang, Jefrey Lijffijt, Tijl De Bie, Yvan Saeys
ICLR 2022 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We include thorough experiments on synthetic and real data that highlight the usefulness and versatility of this approach, with applications ranging from modeling high-dimensional single-cell data, to graph embedding. |
| Researcher Affiliation | Academia | Robin Vandaele, Bo Kang, Jefrey Lijffijt, Tijl De Bie, Yvan Saeys Ghent University, Gent, Belgium |
| Pseudocode | No | The paper does not contain structured pseudocode or algorithm blocks. |
| Open Source Code | Yes | Code is available on github.com/robinvndaele/topembedding. |
| Open Datasets | Yes | We considered a single-cell trajectory data set of 264 cells in a 6812-dimensional gene expression space (Cannoodt et al., 2018; Saelens et al., 2019). |
| Dataset Splits | Yes | All points in the 2D embeddings were then split into 90% points for training and 10% for testing. Consecutively, we used 5-fold CV on the training data to tune the regularization hyperparameter C {1e 2, 1e 1, 1, 1e1, 1e2}. |
| Hardware Specification | Yes | Topological optimization was performed in Pytorch on a machine equipped with an Intel Core TM i7 processor at 2.6GHz and 8GB of RAM, using code adapted from Br uel-Gabrielsson et al. (2020). |
| Software Dependencies | No | The paper mentions software like Pytorch, SCIKIT-LEARN, and Dionysus but does not provide specific version numbers for these dependencies. |
| Experiment Setup | Yes | Tables 1 & 2 summarize data sizes, hyperparameters, losses, and optimization times. |