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@@ -229,12 +229,12 @@ The re-sampled output:
 ### QDRiemaNNLinear ###
 The Quasi-Diagonal Riemannian Neural Network Linear (QDRiemaNNLinear) module is an implementation
 of the quasi-diagonal reduction of metrics, used for Riemannian gradient descent.
-The algorithm is defined in http://arxiv.org/abs/1303.0818 and an efficient implementation is described in http://arxiv.org/abs/1602.08007.
+The algorithm is defined in Riemannian metrics for neural networks I: feedforward networks by Yann Ollivier (http://arxiv.org/abs/1303.0818) and an efficient implementation is described in Practical Riemannian Neural Networks by Yann Ollivier and Gaetan Marceau-Caron (http://arxiv.org/abs/1602.08007).
 To use this module, simply replace nn.Linear(ninput,noutput) with nnx.QDRiemaNNLinear(ninput,noutput).
 As always, the step-size must be chosen accordingly.
-Two other arguments are also possible:
-gamma (default=0.01): determine the update rate of the metric for a minibatch setting, i.e., (1-gamma) * oldMetric + gamma newMetric. Should be set to 1/#minibatch
-qdFlag (default=true): Whether to use the quasi-diagonal reduction (true) or only the diagonal (false). The former should be better.
+Two additional arguments are also possible:
+* gamma (default=0.01): determine the update rate of the metric for a minibatch setting, i.e., (1-gamma) * oldMetric + gamma newMetric. Should be set to 1/#minibatch
+* qdFlag (default=true): Whether to use the quasi-diagonal reduction (true) or only the diagonal (false). The former should be better.
 
 To implement a natural gradient descent, one should also use a module for generating the pseudo-labels.