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 [1].
On UMAP's True Loss Function
Authors: Sebastian Damrich, Fred A. Hamprecht
NeurIPS 2021 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We corroborate our theoretical ๏ฌndings on toy and single cell RNA sequencing data. |
| Researcher Affiliation | Academia | Sebastian Damrich Fred A. Hamprecht HCI/IWR at Heidelberg University, 69120 Heidelberg, Germany EMAIL |
| Pseudocode | Yes | Algorithm 1: UMAP s optimization |
| Open Source Code | Yes | Our code is publicly available at https://github.com/hci-unihd/UMAPs-true-loss. |
| Open Datasets | Yes | We illustrate our analysis on gene expression measurements of 86024 cells of C. elegans [16, 14]. We start out with a 100 dimensional PCA of the data obtained from http://cb.csail.mit.edu/cb/densvis/datasets/. We informed the authors of our use of the dataset, which they license under CC BY-NC 2.0. |
| Dataset Splits | No | The paper does not explicitly provide training/validation/test dataset splits with percentages, sample counts, or citations to predefined splits. |
| Hardware Specification | No | The paper does not provide specific hardware details such as GPU/CPU models, memory, or cloud instance types used for running the experiments. |
| Software Dependencies | No | The paper mentions a GitHub repository for their code but does not list specific software dependencies with version numbers (e.g., Python 3.x, PyTorch 1.x). |
| Experiment Setup | Yes | We start out with a 100 dimensional PCA of the data and use the cosine metric in high-dimensional space, consider k = 30 neighbors and optimize for 750 epochs, similar to [14]. |