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This repository contains PyTorch extensions to compute persistent homology and to differentiate through the persistent homology computation. The packaging structure is similar to PyTorch's structure to facilitate usage for people familiar with PyTorch.


Read the docs!

The folder tutorials (within docs) contains some (more or less) minimalistic examples in form of Jupyter notebooks to demonstrate how to use the PyTorch extensions.

Associated publications

If you use any of these extensions, please cite the following works (depending on which functionality you use, obviously :)

  author    = {C.~Hofer, R.~Kwitt, M.~Niethammer and A.~Uhl},
  title     = {Deep Learning with Topological Signatures},
  booktitle = {NIPS},
  year      = {2017}}

  author    = {C.~Hofer, R.~Kwitt, M.~Dixit and M.~Niethammer},
  title     = {Connectivity-Optimized Representation Learning via Persistent Homology},
  booktitle = {ICML},
  year      = {2019}}

  author    = {C.~Hofer, R.~Kwitt, and M.~Niethammer},
  title     = {Learning Representations of Persistence Barcodes},
  booktitle = {JMLR},
  year      = {2019}}

  author    = {C.~Hofer, F.~Graf, R.~Kwitt, B.~Rieck and M.~Niethammer},
  title     = {Graph Filtration Learning},
  booktitle = {arXiv},
  year      = {2020}}

  author    = {C.~Hofer, F.~Graf, M.~Niethammer and R.~Kwitt},     
  title     = {Topologically Densified Distributions},     
  booktitle = {arXiv},    
  year      = {2020}} 

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