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 Shift Toward Open and Reproducible AI Research," Under Review, 2026. doi: 10.48550/arXiv.2606.16974..
Identifying Metric Structures of Deep Latent Variable Models
Authors: Stas Syrota, Yevgen Zainchkovskyy, Johnny Xi, Benjamin Bloem-Reddy, Søren Hauberg
ICML 2025 | Venue PDF | LLM Run Details | Input Tokens: 26,291 Total number of tokens sent to the LLM as input for this paper's analysis. | Output Tokens: 4,910 Total number of tokens produced by the LLM (including reasoning/thinking tokens) for this paper's analysis.
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We empirically demonstrate that our theory results in more reliable latent distances, offering a principled path forward in extracting trustworthy conclusions from deep latent variable models. |
| Researcher Affiliation | Academia | 1Department of Applied Mathematics and Computer Science, Technical University of Denmark, Lyngby, Denmark 2Department of Statistics, University of British Columbia. Correspondence to: Stas Syrota <EMAIL>. |
| Pseudocode | No | The paper describes the methodology for computing geodesics in Appendix C but does not present it in a structured pseudocode or algorithm block. |
| Open Source Code | Yes | The code to reproduce our results is available in the project repository Git Hub1. 1https://github.com/mustass/identifiable-latent-metric-space |
| Open Datasets | Yes | We train this model on a 3-class subset of MNIST (Deng, 2012) with a 2D latent space for visualization purposes and full CIFAR10 (Krizhevsky et al.). |
| Dataset Splits | No | The paper mentions using a 'test set' for evaluating distances but does not provide specific percentages, sample counts, or explicit methodology for training, validation, and test dataset splits. |
| Hardware Specification | No | The paper does not provide specific details about the hardware (e.g., GPU/CPU models, memory) used for running the experiments. |
| Software Dependencies | No | The paper mentions optimizers like Adam and specific model architectures like M-flows and RQS splines, but it does not provide specific version numbers for any software dependencies or libraries used for implementation. |
| Experiment Setup | Yes | We train 30 models with different initial seeds and compute both Euclidean (d E) and geodesic (dg) distances in the latent space between 100 randomly chosen unique point pairs... |