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authorCédric Vincent-Cuaz <cedvincentcuaz@gmail.com>2023-03-09 14:21:33 +0100
committerGitHub <noreply@github.com>2023-03-09 14:21:33 +0100
commita5930d3b3a446bf860d6dfacc1e17151fae1dd1d (patch)
tree3897f88fae95c25314be3976f30285bc4db14494 /README.md
parent263a36ff627257422d7e191f6882fb1c8fc68326 (diff)
[MRG] Semi-relaxed (fused) gromov-wasserstein divergence and improvements of gromov-wasserstein solvers (#431)
* maj gw/ srgw/ generic cg solver * correct pep8 on current state * fix bug previous tests * fix pep8 * fix bug srGW constC in loss and gradient * fix doc html * fix doc html * start updating test_optim.py * update tests gromov and optim - plus fix gromov dependencies * add symmetry feature to entropic gw * add symmetry feature to entropic gw * add exemple for sr(F)GW matchings * small stuff * remove (reg,M) from line-search/ complete srgw tests with backend * remove backend repetitions / rename fG to costG/ fix innerlog to True * fix pep8 * take comments into account / new nx parameters still to test * factor (f)gw2 + test new backend parameters in ot.gromov + harmonize stopping criterions * split gromov.py in ot/gromov/ + update test_gromov with helper_backend functions * manual documentaion gromov * remove circular autosummary * trying stuff * debug documentation * alphabetic ordering of module * merge into branch * add note in entropic gw solvers --------- Co-authored-by: Rémi Flamary <remi.flamary@gmail.com>
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@@ -42,6 +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].
* [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:
@@ -300,4 +301,8 @@ Dictionary Learning](https://arxiv.org/pdf/2102.06555.pdf), International Confer
[45] Hundrieser, Shayan, Marcel Klatt, and Axel Munk. [The statistics of circular optimal transport.](https://arxiv.org/abs/2103.15426) Directional Statistics for Innovative Applications: A Bicentennial Tribute to Florence Nightingale. Singapore: Springer Nature Singapore, 2022. 57-82.
-[46] Bonet, C., Berg, P., Courty, N., Septier, F., Drumetz, L., & Pham, M. T. (2023). [Spherical Sliced-Wasserstein](https://openreview.net/forum?id=jXQ0ipgMdU). International Conference on Learning Representations. \ No newline at end of file
+[46] Bonet, C., Berg, P., Courty, N., Septier, F., Drumetz, L., & Pham, M. T. (2023). [Spherical Sliced-Wasserstein](https://openreview.net/forum?id=jXQ0ipgMdU). International Conference on Learning Representations.
+
+[47] Chowdhury, S., & Mémoli, F. (2019). [The gromov–wasserstein distance between networks and stable network invariants](https://academic.oup.com/imaiai/article/8/4/757/5627736). Information and Inference: A Journal of the IMA, 8(4), 757-787.
+
+[48] Cédric Vincent-Cuaz, Rémi Flamary, Marco Corneli, Titouan Vayer, Nicolas Courty (2022). [Semi-relaxed Gromov-Wasserstein divergence and applications on graphs](https://openreview.net/pdf?id=RShaMexjc-x). International Conference on Learning Representations (ICLR), 2022. \ No newline at end of file