Update README.md

README.md

@@ -5,13 +5,11 @@ For experiments on cifar-100 and SVHN, please use the same architecture as CIFAR
 
 Also for Cifar-100 and SVHN, we used **softmax losses** instead of hinge losses for the output supervision and deep supervision. There are known convergence issues of hinge loss on > 10 classes in Caffe.
 
-Please cite the following pape:
+Please cite DSN  in your publications if it helps your research:
 
-###Deeply-Supervised Nets ###
-Chen-Yu Lee*, Saining Xie*, Patrick Gallagher, Zhengyou Zhang, Zhuowen Tu 
-(* indicates equal contributions) In Proceedings of AISTATS 2015 
+   [1] Deeply-Supervised Nets Chen-Yu Lee*, Saining Xie*, Patrick Gallagher, Zhengyou Zhang, Zhuowen Tu (* indicates equal contributions) In Proceedings of AISTATS 2015
 
-If you have problems reproducing the experiments please contact the authors.
+If you have problems reproducing the experiments feel free to contact the authors.
 
 **Deeply Supervised Nets**
 This DSN code is based on an older version of [CAFFE](http://caffe.berkeleyvision.org) framework. This is for reproducing the results reported in our [paper](http://vcl.ucsd.edu/~sxie/2014/09/12/dsn-project/). Though with minimum engineering efforts you can apply this idea to your own code, as well as new network architectures.