Data from: MANTRA: The Manifold Triangulations Assemblage
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application/gzipUpdated Apr 22, 2025
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EmailClick to copy linkLink copiedCiteBastian Rieck; Bastian Rieck; Ernst Röell; Daniel Bīn Schmid; Rubén Ballester Bautista; Ernst Röell; Daniel Bīn Schmid; Rubén Ballester Bautista (2025). MANTRA: The Manifold Triangulations Assemblage [Dataset]. http://doi.org/10.5281/zenodo.15259901application/gzipAvailable download formatsUnique identifierhttps://doi.org/10.5281/zenodo.15259901Dataset updatedApr 22, 2025AuthorsBastian Rieck; Bastian Rieck; Ernst Röell; Daniel Bīn Schmid; Rubén Ballester Bautista; Ernst Röell; Daniel Bīn Schmid; Rubén Ballester BautistaLicensehttps://opensource.org/licenses/BSD-3-Clausehttps://opensource.org/licenses/BSD-3-Clause
DescriptionMANTRA is a dataset consisting of combinatorial triangulations of manifolds. It can be used to create novel algorithms in topological deep learning or debug existing ones. See our ICLR 2025 paper for more details on potential experiments.
Please use the following citation for our work:
@inproceedings{Ballester25a, title = {{MANTRA}: {T}he {M}anifold {T}riangulations {A}ssemblage}, author = {Rubén Ballester and Ernst Röell and Daniel Bīn Schmid and Mathieu Alain and Sergio Escalera and Carles Casacuberta and Bastian Rieck}, year = 2025, eprint = {2410.02392}, archiveprefix = {arXiv}, primaryclass = {cs.LG}, booktitle = {International Conference on Learning Representations}, url = {https://openreview.net/forum?id=X6y5CC44HM}, }
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FacebookTwitterEmailhttps://opensource.org/licenses/BSD-3-Clausehttps://opensource.org/licenses/BSD-3-Clause
MANTRA is a dataset consisting of combinatorial triangulations of manifolds. It can be used to create novel algorithms in topological deep learning or debug existing ones. See our ICLR 2025 paper for more details on potential experiments.
Please use the following citation for our work:
@inproceedings{Ballester25a, title = {{MANTRA}: {T}he {M}anifold {T}riangulations {A}ssemblage}, author = {Rubén Ballester and Ernst Röell and Daniel Bīn Schmid and Mathieu Alain and Sergio Escalera and Carles Casacuberta and Bastian Rieck}, year = 2025, eprint = {2410.02392}, archiveprefix = {arXiv}, primaryclass = {cs.LG}, booktitle = {International Conference on Learning Representations}, url = {https://openreview.net/forum?id=X6y5CC44HM}, }