Identifying Metric Structures of Deep Latent Variable Models
Authors: Stas Syrota, Yevgen Zainchkovskyy, Johnny Xi, Benjamin Bloem-Reddy, Søren Hauberg
ICML 2025 | Venue PDF | Archive PDF | Plain Text | LLM Run Details
| 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... |