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..
Topological Autoencoders
Authors: Michael Moor, Max Horn, Bastian Rieck, Karsten Borgwardt
ICML 2020 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We show that our approach is theoretically well-founded and that it exhibits favourable latent representations on a synthetic manifold as well as on real-world image data sets, while preserving low reconstruction errors. 5. Experiments |
| Researcher Affiliation | Academia | 1Department of Biosystems Science and Engineering, ETH Zurich, 4058 Basel, Switzerland 2SIB Swiss Institute of Bioinformatics, Switzerland. Correspondence to: Karsten Borgwardt <EMAIL>. |
| Pseudocode | No | The paper describes its methods but does not include any clearly labeled pseudocode or algorithm blocks. |
| Open Source Code | Yes | We make our code publicly available.4 https://github.com/BorgwardtLab/topological-autoencoders |
| Open Datasets | Yes | We generate a SPHERES data set that consists of ten highdimensional 100-spheres living in a 101-dimensional space... We also use three image data sets (MNIST, FASHION-MNIST, and CIFAR-10) |
| Dataset Splits | Yes | We split each data set into training and testing (using the predefined split if available; 90% versus 10% otherwise). Additionally, we remove 15% of the training split as a validation data set for tuning the hyperparameters. |
| Hardware Specification | No | The paper does not explicitly describe the specific hardware (e.g., GPU/CPU models, memory) used to run its experiments. |
| Software Dependencies | No | The paper mentions software like ADAM for optimization, but does not specify version numbers for any key software components or libraries. |
| Experiment Setup | Yes | We split each data set into training and testing (using the predefined split if available; 90% versus 10% otherwise). Additionally, we remove 15% of the training split as a validation data set for tuning the hyperparameters. We normalised our topological loss term by the batch size m in order to disentangle λ from it. All autoencoders employ batch-norm and are optimized using ADAM (Kingma & Ba, 2014). Please refer to Section A.6 for more details on architectures and hyperparameters. |