awesome new readme

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@@ -16,39 +16,51 @@ learning.
 
 Website and documentation: [https://PythonOT.github.io/](https://PythonOT.github.io/)
 
-POT provides the following generic OT solvers:
-* OT Network Flow solver for the linear program/ Earth Movers Distance [1].
-* Conditional gradient [6] and Generalized conditional gradient for regularized OT [7].
-* Entropic regularization OT solver with Sinkhorn Knopp Algorithm [2],
-  stabilized version [9] [10], greedy Sinkhorn [22] and Screening Sinkhorn [26] with optional GPU
-  implementation (requires cupy).
-* Bregman projections for Wasserstein barycenter [3], convolutional barycenter [21] and unmixing [4].
+Source Code (MIT): [https://github.com/PythonOT/POT](https://github.com/PythonOT/POT)
+
+POT provides the following generic OT solvers (links to examples):
+
+* [OT Network Simplex solver](https://pythonot.github.io/auto_examples/plot_OT_1D.html) for the linear program/ Earth Movers Distance [1] .
+* [Conditional gradient](https://pythonot.github.io/auto_examples/plot_optim_OTreg.html) [6] and [Generalized conditional gradient](https://pythonot.github.io/auto_examples/plot_optim_OTreg.html) for regularized OT [7].
+* Entropic regularization OT solver with [Sinkhorn Knopp Algorithm](https://pythonot.github.io/auto_examples/plot_OT_1D.html) [2] , stabilized version [9] [10], greedy Sinkhorn [22] and [Screening Sinkhorn [26] ](https://pythonot.github.io/auto_examples/plot_screenkhorn_1D.html) with optional GPU implementation (requires cupy).
+* Bregman projections for [Wasserstein barycenter](https://pythonot.github.io/auto_examples/plot_barycenter_lp_vs_entropic.html) [3], [convolutional barycenter](https://pythonot.github.io/auto_examples/plot_convolutional_barycenter.html) [21]  and unmixing [4].
 * Sinkhorn divergence [23] and entropic regularization OT  from empirical data.
-* Smooth optimal transport solvers (dual and semi-dual) for KL and squared L2 regularizations [17].
-* Non regularized Wasserstein barycenters [16] with LP solver (only small scale).
-* Gromov-Wasserstein distances and barycenters ([13] and regularized [12])
-* Stochastic Optimization for Large-scale Optimal Transport (semi-dual problem [18] and dual problem [19])
-* Non regularized free support Wasserstein barycenters [20].
-* Unbalanced OT with KL relaxation distance and barycenter [10, 25].
-* Partial Wasserstein and Gromov-Wasserstein (exact [29] and entropic [3]
+* [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].
+* Non regularized [Wasserstein barycenters [16] ](https://pythonot.github.io/auto_examples/plot_barycenter_lp_vs_entropic.html)) with LP solver (only small scale).
+* [Gromov-Wasserstein distances](https://pythonot.github.io/auto_examples/plot_gromov.html) and [GW barycenters](https://pythonot.github.io/auto_examples/plot_gromov_barycenter.html)  (exact [13] and regularized [12])
+ * [Fused-Gromov-Wasserstein distances solver](https://pythonot.github.io/auto_examples/plot_fgw.html#sphx-glr-auto-examples-plot-fgw-py) and [FGW barycenters](https://pythonot.github.io/auto_examples/plot_barycenter_fgw.html) [24]
+* [Stochastic solver](https://pythonot.github.io/auto_examples/plot_stochastic.html) for Large-scale Optimal Transport (semi-dual problem [18] and dual problem [19])
+* Non regularized [free support Wasserstein barycenters](https://pythonot.github.io/auto_examples/plot_free_support_barycenter.html) [20].
+* [Unbalanced OT](https://pythonot.github.io/auto_examples/plot_UOT_1D.html) with KL relaxation and [barycenter](https://pythonot.github.io/auto_examples/plot_UOT_barycenter_1D.html) [10, 25].
+* [Partial Wasserstein and Gromov-Wasserstein](https://pythonot.github.io/auto_examples/plot_partial_wass_and_gromov.html) (exact [29] and entropic [3]
   formulations).
 
 POT provides the following Machine Learning related solvers:
-* Optimal transport for domain adaptation with group lasso regularization and   Laplacian regularization [5] [30].
-* Linear OT [14] and Joint OT matrix and mapping estimation [8].
-* Wasserstein Discriminant Analysis [11] (requires autograd + pymanopt).
-* JCPOT algorithm for multi-source domain adaptation with target shift [27].
+
+* [Optimal transport for domain
+  adaptation](https://pythonot.github.io/auto_examples/plot_otda_classes.html)
+  with [group lasso regularization](https://pythonot.github.io/auto_examples/plot_otda_classes.html),   [Laplacian regularization](https://pythonot.github.io/auto_examples/plot_otda_laplacian.html) [5] [30] and [semi
+  supervised setting](https://pythonot.github.io/auto_examples/plot_otda_semi_supervised.html).
+* [Linear OT mapping](https://pythonot.github.io/auto_examples/plot_otda_linear_mapping.html) [14] and [Joint OT mapping estimation](https://pythonot.github.io/auto_examples/plot_otda_mapping.html) [8].
+* [Wasserstein Discriminant Analysis](https://pythonot.github.io/auto_examples/plot_WDA.html) [11] (requires autograd + pymanopt).
+* [JCPOT algorithm for multi-source domain adaptation with target shift](https://pythonot.github.io/auto_examples/plot_otda_jcpot.html) [27].
 
 Some demonstrations  are available in the  [documentation](https://pythonot.github.io/auto_examples/index.html).
 
 #### Using and citing the toolbox
 
-If you use this toolbox in your research and find it useful, please cite POT using the following bibtex reference:
+If you use this toolbox in your research and find it useful, please cite POT
+using the following bibtex reference:
+```
+Rémi Flamary and Nicolas Courty, POT Python Optimal Transport library, Website: https://pythonot.github.io/, 2017
+```
+
+In Bibtex format:
 ```
 @misc{flamary2017pot,
 title={POT Python Optimal Transport library},
 author={Flamary, R{'e}mi and Courty, Nicolas},
-url={https://github.com/rflamary/POT},
+url={https://pythonot.github.io/},
 year={2017}
 }
 ```