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..
ActUp: Analyzing and Consolidating tSNE and UMAP
Authors: Andrew Draganov, Jakob Jørgensen, Katrine Scheel, Davide Mottin, Ira Assent, Tyrus Berry, Cigdem Aslay
IJCAI 2023 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We experimentally validate that GDR can simulate both methods through a thorough quantitative and qualitative evaluation across many datasets and settings. |
| Researcher Affiliation | Academia | 1Aarhus University 2George Mason University EMAIL, EMAIL |
| Pseudocode | No | The paper does not contain any sections or figures explicitly labeled as "Pseudocode" or "Algorithm". |
| Open Source Code | Yes | We release improved versions of t SNE, UMAP, and GDR that are fully plug-andplay with the traditional libraries. |
| Open Datasets | Yes | The paper uses well-known public datasets such as MNIST [Deng, 2012], Fashion MNIST [Xiao et al., 2017], Coil-100 [NENE, 1996], Single Cell [Tasic et al., 2018], and Cifar-10 [Krizhevsky, 2009]. |
| Dataset Splits | No | The paper mentions "training" and metrics like "k NN accuracy" and "V-measure" but does not specify train/validation/test splits, percentages, or absolute sample counts for data partitioning. |
| Hardware Specification | No | The paper mentions running computations "on a GPU" but does not specify any particular GPU model (e.g., NVIDIA A100), CPU model, or other detailed hardware specifications. |
| Software Dependencies | No | The paper mentions |
| Experiment Setup | Yes | The paper discusses various aspects of the experimental setup, including hyperparameter effects (Table 1, Table 4), learning rate adjustments ("t SNE s learning rate stays constant over training while UMAP s linearly decreases"), and default settings for GDR ("GDR therefore defaults to t SNE s asymmetric attraction and a and b scalars along with UMAP s distance-metric, initialization, nearest neighbors, and pij symmetrization"). |