Bibliography

Please cite this library and the related papers when using them in scientific publications.

@article{multipers,
  title = {Multipers: {{Multiparameter Persistence}} for {{Machine Learning}}},
  shorttitle = {Multipers},
  author = {Loiseaux, David and Schreiber, Hannah},
  year = {2024},
  month = nov,
  journal = {Journal of Open Source Software},
  volume = {9},
  number = {103},
  pages = {6773},
  issn = {2475-9066},
  doi = {10.21105/joss.06773},
  langid = {english},
}

[1]

David Loiseaux, Mathieu Carrière, and Andrew Blumberg. A Framework for Fast and Stable Representations of Multiparameter Persistent Homology Decompositions. Advances in Neural Information Processing Systems, 36:35774–35798, December 2023.

[2]

David Loiseaux, Mathieu Carrière, and Andrew J. Blumberg. Fast, Stable and Efficient Approximation of Multi-parameter Persistence Modules with MMA. June 2022. doi:10.48550/arXiv.2206.02026.

[3]

David Loiseaux and Hannah Schreiber. Multipers: Multiparameter Persistence for Machine Learning. Journal of Open Source Software, 9(103):6773, November 2024. doi:10.21105/joss.06773.

[4]

David Loiseaux, Luis Scoccola, Mathieu Carrière, Magnus Bakke Botnan, and Steve Oudot. Stable Vectorization of Multiparameter Persistent Homology using Signed Barcodes as Measures. Advances in Neural Information Processing Systems, 36:68316–68342, December 2023.

[5]

Luis Scoccola, Siddharth Setlur, David Loiseaux, Mathieu Carrière, and Steve Oudot. Differentiability and Optimization of Multiparameter Persistent Homology. In Proceedings of the 41st International Conference on Machine Learning, volume 235 of Proceedings of Machine Learning Research, 43986–44011. PMLR, July 2024.