diff options
author | Cédric Vincent-Cuaz <cedvincentcuaz@gmail.com> | 2023-06-12 12:01:48 +0200 |
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committer | GitHub <noreply@github.com> | 2023-06-12 12:01:48 +0200 |
commit | 9076f02903ba2fb9ea9fe704764a755cad8dcd63 (patch) | |
tree | b7fda84880c5dabd1c441a1655741493e0683342 /README.md | |
parent | f0dab2f684f4fc768fd50e0b70918e075dcdd0f3 (diff) |
[FEAT] Entropic gw/fgw/srgw/srfgw solvers (#455)upstream/latest
* add entropic fgw + fgw bary + srgw + srfgw with tests
* add exemples for entropic srgw - srfgw solvers
* add PPA solvers for GW/FGW + complete previous commits
* update readme
* add tests
* add examples + tests + warning in entropic solvers + releases
* reduce testing runtimes for test_gromov
* fix conflicts
* optional marginals
* improve coverage
* gromov doc harmonization
* fix pep8
* complete optional marginal for entropic srfgw
---------
Co-authored-by: Rémi Flamary <remi.flamary@gmail.com>
Diffstat (limited to 'README.md')
-rw-r--r-- | README.md | 8 |
1 files changed, 5 insertions, 3 deletions
@@ -27,8 +27,8 @@ POT provides the following generic OT solvers (links to examples): * [Smooth optimal transport solvers](https://pythonot.github.io/auto_examples/plot_OT_1D_smooth.html) (dual and semi-dual) for KL and squared L2 regularizations [17]. * Weak OT solver between empirical distributions [39] * Non regularized [Wasserstein barycenters [16] ](https://pythonot.github.io/auto_examples/barycenters/plot_barycenter_lp_vs_entropic.html) with LP solver (only small scale). -* [Gromov-Wasserstein distances](https://pythonot.github.io/auto_examples/gromov/plot_gromov.html) and [GW barycenters](https://pythonot.github.io/auto_examples/gromov/plot_gromov_barycenter.html) (exact [13] and regularized [12]), differentiable using gradients from Graph Dictionary Learning [38] - * [Fused-Gromov-Wasserstein distances solver](https://pythonot.github.io/auto_examples/gromov/plot_fgw.html#sphx-glr-auto-examples-plot-fgw-py) and [FGW barycenters](https://pythonot.github.io/auto_examples/gromov/plot_barycenter_fgw.html) [24] +* [Gromov-Wasserstein distances](https://pythonot.github.io/auto_examples/gromov/plot_gromov.html) and [GW barycenters](https://pythonot.github.io/auto_examples/gromov/plot_gromov_barycenter.html) (exact [13] and regularized [12,51]), differentiable using gradients from Graph Dictionary Learning [38] + * [Fused-Gromov-Wasserstein distances solver](https://pythonot.github.io/auto_examples/gromov/plot_fgw.html#sphx-glr-auto-examples-plot-fgw-py) and [FGW barycenters](https://pythonot.github.io/auto_examples/gromov/plot_barycenter_fgw.html) (exact [24] and regularized [12,51]). * [Stochastic solver](https://pythonot.github.io/auto_examples/others/plot_stochastic.html) and [differentiable losses](https://pythonot.github.io/auto_examples/backends/plot_stoch_continuous_ot_pytorch.html) for @@ -42,7 +42,7 @@ POT provides the following generic OT solvers (links to examples): * [Wasserstein distance on the circle](https://pythonot.github.io/auto_examples/plot_compute_wasserstein_circle.html) [44, 45] * [Spherical Sliced Wasserstein](https://pythonot.github.io/auto_examples/sliced-wasserstein/plot_variance_ssw.html) [46] * [Graph Dictionary Learning solvers](https://pythonot.github.io/auto_examples/gromov/plot_gromov_wasserstein_dictionary_learning.html) [38]. -* [Semi-relaxed (Fused) Gromov-Wasserstein divergences](https://pythonot.github.io/auto_examples/gromov/plot_semirelaxed_fgw.html) [48]. +* [Semi-relaxed (Fused) Gromov-Wasserstein divergences](https://pythonot.github.io/auto_examples/gromov/plot_semirelaxed_fgw.html) (exact and regularized [48]). * [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: @@ -310,3 +310,5 @@ Dictionary Learning](https://arxiv.org/pdf/2102.06555.pdf), International Confer [49] Redko, I., Vayer, T., Flamary, R., and Courty, N. (2020). [CO-Optimal Transport](https://proceedings.neurips.cc/paper/2020/file/cc384c68ad503482fb24e6d1e3b512ae-Paper.pdf). Advances in Neural Information Processing Systems, 33. [50] Liu, T., Puigcerver, J., & Blondel, M. (2023). [Sparsity-constrained optimal transport](https://openreview.net/forum?id=yHY9NbQJ5BP). Proceedings of the Eleventh International Conference on Learning Representations (ICLR). + +[51] Xu, H., Luo, D., Zha, H., & Duke, L. C. (2019). [Gromov-wasserstein learning for graph matching and node embedding](http://proceedings.mlr.press/v97/xu19b.html). In International Conference on Machine Learning (ICML), 2019. |