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| author | Cédric Vincent-Cuaz <cedvincentcuaz@gmail.com> | 2022-02-11 10:53:38 +0100 |
|---|---|---|
| committer | GitHub <noreply@github.com> | 2022-02-11 10:53:38 +0100 |
| commit | 50c0f17d00e3492c4d56a356af30cf00d6d07913 (patch) | |
| tree | 57abfe9510fdba64f6e9c1c4b4716e7b0ba28ed0 /README.md | |
| parent | a5e0f0d40d5046a6639924347ef97e2ac80ad0c9 (diff) | |
[MRG] GW dictionary learning (#319)
* add fgw dictionary learning feature
* add fgw dictionary learning feature
* plot gromov wasserstein dictionary learning
* Update __init__.py
* fix pep8 errors exact E501 line too long
* fix last pep8 issues
* add unitary tests for (F)GW dictionary learning without using autodifferentiable functions
* correct tests for (F)GW dictionary learning without using autodiff
* correct tests for (F)GW dictionary learning without using autodiff
* fix docs and notations
* answer to review: improve tests, docs, examples + make node weights optional
* fix pep8 and examples
* improve docs + tests + thumbnail
* make example faster
* improve ex
* update README.md
* make GDL tests faster
Co-authored-by: Rémi Flamary <remi.flamary@gmail.com>
Diffstat (limited to 'README.md')
| -rw-r--r-- | README.md | 2 |
1 files changed, 2 insertions, 0 deletions
@@ -36,6 +36,7 @@ POT provides the following generic OT solvers (links to examples): * [Partial Wasserstein and Gromov-Wasserstein](https://pythonot.github.io/auto_examples/unbalanced-partial/plot_partial_wass_and_gromov.html) (exact [29] and entropic [3] formulations). * [Sliced Wasserstein](https://pythonot.github.io/auto_examples/sliced-wasserstein/plot_variance.html) [31, 32] and Max-sliced Wasserstein [35] that can be used for gradient flows [36]. +* [Graph Dictionary Learning solvers](https://pythonot.github.io/auto_examples/gromov/plot_gromov_wasserstein_dictionary_learning.html) [38]. * [Several backends](https://pythonot.github.io/quickstart.html#solving-ot-with-multiple-backends) for easy use of POT with [Pytorch](https://pytorch.org/)/[jax](https://github.com/google/jax)/[Numpy](https://numpy.org/)/[Cupy](https://cupy.dev/)/[Tensorflow](https://www.tensorflow.org/) arrays. POT provides the following Machine Learning related solvers: @@ -198,6 +199,7 @@ The contributors to this library are * [Tanguy Kerdoncuff](https://hv0nnus.github.io/) (Sampled Gromov Wasserstein) * [Minhui Huang](https://mhhuang95.github.io) (Projection Robust Wasserstein Distance) * [Nathan Cassereau](https://github.com/ncassereau-idris) (Backends) +* [Cédric Vincent-Cuaz](https://github.com/cedricvincentcuaz) (Graph Dictionary Learning) This toolbox benefit a lot from open source research and we would like to thank the following persons for providing some code (in various languages): |