readme again

1 files changed, 13 insertions, 5 deletions

diff --git a/README.md b/README.md
index 2e81a55..dd4c83e 100644
--- a/README.md
+++ b/README.md
@@ -2,7 +2,7 @@
 
 
 [![Documentation Status](https://readthedocs.org/projects/pot/badge/?version=latest)](http://pot.readthedocs.io/en/latest/?badge=latest)
-                
+
 This open source Python library provide several solvers for optimization problems related to Optimal Transport for signal, image processing and machine learning.
 
 It provides the following solvers:
@@ -13,6 +13,12 @@ It provides the following solvers:
 * Optimal transport for domain adaptation with group lasso regularization [5]
 * Conditional gradient [6] and Generalized conditional gradient for regularized OT [7].
 
+We are also currently working on the following features:
+
+[] Image color adaptation demo
+[] Scikit-learn inspired classes for domain adaptation
+[] Mapping estimation as proposed in [8]
+
 Some demonstrations (both in Python and Jupyter Notebook format) are available in the examples folder.
 
 ## Installation
@@ -53,10 +59,10 @@ The examples folder contain several examples and use case for the library. The f
 
  Here is a list of the Python notebook if you want a quick look:
 
-* [1D optimal transport](examples/Demo_1D_OT.ipynb)
-* [2D optimal transport on empirical distributions](examples/Demo_2D_OT_samples.ipynb)
-* [1D Wasserstein barycenter](examples/Demo_1D_barycenter.ipynb)
-* [OT with user provided regularization](examples/Demo_Optim_OTreg.ipynb)
+* [1D optimal transport](https://github.com/rflamary/POT/blob/master/examples/Demo_1D_OT.ipynb)
+* [2D optimal transport on empirical distributions](https://github.com/rflamary/POT/blob/master/examples/Demo_2D_OT_samples.ipynb)
+* [1D Wasserstein barycenter](https://github.com/rflamary/POT/blob/master/examples/Demo_1D_barycenter.ipynb)
+* [OT with user provided regularization](https://github.com/rflamary/POT/blob/master/examples/Demo_Optim_OTreg.ipynb)
 
 
 ## Acknowledgements
@@ -89,3 +95,5 @@ This toolbox benefit a lot from open source research and we would like to thank
 [6] Ferradans, S., Papadakis, N., Peyré, G., & Aujol, J. F. (2014). Regularized discrete optimal transport. SIAM Journal on Imaging Sciences, 7(3), 1853-1882.
 
 [7] Rakotomamonjy, A., Flamary, R., & Courty, N. (2015). Generalized conditional gradient: analysis of convergence and applications. arXiv preprint arXiv:1510.06567.
+
+[8] M. Perrot, N. Courty, R. Flamary, A. Habrard, "Mapping estimation for discrete optimal transport", Neural Information Processing Systems (NIPS), 2016.