Update Paper references in doxydoc mainpage.

gen_doc/mainpage.md

@@ -25,9 +25,11 @@ A general introduction to the FAµST framework is available in the following pap
 
 [1] [Le Magoarou L. and Gribonval R., “Flexible multi-layer sparse approximations of matrices and applications”](https://hal.archives-ouvertes.fr/hal-01167948), Journal of Selected Topics in Signal Processing, 2016.
 
-The following paper can come as complement:
+The following papers can come as complement:
 
-[2] [Le Magoarou L. and Gribonval R., Gramfort A., “FAµST: speeding up linear transforms for tractable inverse problems“](https://hal.archives-ouvertes.fr/hal-01156478v1)
+[2] [Le Magoarou L. and Gribonval R., Gramfort A., “FAµST: speeding up linear transforms for tractable inverse problems“](https://hal.archives-ouvertes.fr/hal-01156478v1), European Signal Processing Conference (EUSIPCO), Aug 2015, Nice, France.
+
+[3] [Le Magoarou L., Gribonval R., Trembaly N., “Approximate fast graph Fourier transforms via multi-layer sparse approximation“](https://hal.inria.fr/hal-01416110),Transactions on Signal and Information Processing over Networks
 
 The FAµST toolbox was initially released as a Matlab implementation ([versions 1.x](http://faust.inria.fr/download/faust-1-x/)).
 <br/>Since version 2.0, it has been implemented in C++. Besides, the development of wrappers has made this C++ core accessible from Matlab and Python programming languages.