Bibliography

Please cite the library and the related papers.

@misc{multipers,
    author={Loiseaux, David and Schreiber, Hannah},
    title={`multipers` : Multiparameter Persistence for Machine Learning},
    year={2022},
    publisher={GitHub},
    journal={GitHub repository},
    howpublished={\url{https://github.com/DavidLapous/multipers}}
}

[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, 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.

[4]

Luis Scoccola, Siddharth Setlur, David Loiseaux, Mathieu Carrière, and Steve Oudot. Differentiability and Optimization of Multiparameter Persistent Homology. In Forty-First International Conference on Machine Learning. June 2024.