| _version_ | 1866902186634510336 |
|---|---|
| author | Walch, Maria |
| author_facet | Walch, Maria |
| contents | <p>In this experiment, high-dimensional (i.e. 10d and 13d) data sets that have been created and checked for torsion by the author are used as an input to a topological autoencoder architecture. The latents and outputs are checked for torsion and the results are logged. </p> |
| format | Recurso digital |
| id | zenodo_https___doi_org_10_5281_zenodo_17206957 |
| institution | Zenodo |
| language | |
| publishDate | 2025 |
| publisher | Zenodo |
| record_format | zenodo |
| spellingShingle | Topological Autoencoder on High-Dimensional Point Clouds Walch, Maria <p>In this experiment, high-dimensional (i.e. 10d and 13d) data sets that have been created and checked for torsion by the author are used as an input to a topological autoencoder architecture. The latents and outputs are checked for torsion and the results are logged. </p> |
| title | Topological Autoencoder on High-Dimensional Point Clouds |
| url | https://doi.org/10.5281/zenodo.17206957 |