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Commit 05dabf6

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[MRG] Add release version information to each example in the examples gallery (#743)
* release version for examples * release indication on examples * change added in release.md + minor modifs on examples added in 0.9.6
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‎RELEASES.md‎

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- Backend implementation of `ot.dist` for (PR #701)
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- Updated documentation Quickstart guide and User guide with new API (PR #726)
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- Fix jax version for auto-grad (PR #732)
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- Added to each example in the examples gallery the information about the release version in which it was introduced (PR #743)
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#### Closed issues
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- Fixed `ot.mapping` solvers which depended on deprecated `cvxpy` `ECOS` solver (PR #692, Issue #668)

‎examples/backends/plot_Sinkhorn_gradients.py‎

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This example illustrates the differences in terms of computation time between the gradient options for the Sinkhorn solver.
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.. note::
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Example added in release: 0.9.6
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"""
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# Author: Sonia Mazelet <sonia.mazelet@polytechnique.edu>

‎examples/backends/plot_dual_ot_pytorch.py‎

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Dual OT solvers for entropic and quadratic regularized OT with Pytorch
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======================================================================
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.. note::
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Example added in release: 0.8.2.
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"""
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‎examples/backends/plot_optim_gromov_pytorch.py‎

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Optimizing the Gromov-Wasserstein distance with PyTorch
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=======================================================
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.. note::
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Example added in release: 0.8.0.
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In this example, we use the pytorch backend to optimize the Gromov-Wasserstein
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(GW) loss between two graphs expressed as empirical distribution.
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‎examples/backends/plot_sliced_wass_grad_flow_pytorch.py‎

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Sliced Wasserstein barycenter and gradient flow with PyTorch
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============================================================
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.. note::
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Example added in release: 0.8.0.
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In this example we use the pytorch backend to optimize the sliced Wasserstein
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loss between two empirical distributions [31].
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‎examples/backends/plot_stoch_continuous_ot_pytorch.py‎

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Continuous OT plan estimation with Pytorch
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======================================================================
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.. note::
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Example added in release: 0.8.2.
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"""
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‎examples/backends/plot_unmix_optim_torch.py‎

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Wasserstein unmixing with PyTorch
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=================================
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In this example we estimate mixing parameters from distributions that minimize
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the Wasserstein distance. In other words we suppose that a target
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distribution :math:`\mu^t` can be expressed as a weighted sum of source

‎examples/backends/plot_wass2_gan_torch.py‎

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Wasserstein 2 Minibatch GAN with PyTorch
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========================================
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In this example we train a Wasserstein GAN using Wasserstein 2 on minibatches
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as a distribution fitting term.
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‎examples/barycenters/plot_debiased_barycenter.py‎

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Debiased Sinkhorn barycenter demo
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=================================
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This example illustrates the computation of the debiased Sinkhorn barycenter
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as proposed in [37]_.
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‎examples/barycenters/plot_free_support_sinkhorn_barycenter.py‎

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2D free support Sinkhorn barycenters of distributions
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========================================================
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Example added in release: 0.9.1.
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Illustration of Sinkhorn barycenter calculation between empirical distributions understood as point clouds
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"""

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