More detail in readme.

Author: gregversteeg

README.md

@@ -2,7 +2,7 @@
 
 Linear CorEx finds latent factors that are as informative as possible about relationships in the data. 
 The approach is described in this paper:
-[Low Complexity Gaussian Latent Factor Models and a Blessing of Dimensionality](https://arxiv.org/abs/1706.).
+[Low Complexity Gaussian Latent Factor Models and a Blessing of Dimensionality](https://arxiv.org/abs/1706.03353).
 This is useful for covariance estimation, clustering related variables, and dimensionality reduction, especially 
 in the high-dimensional, under-sampled regime. 
 
@@ -62,7 +62,7 @@ Missing values can be specified, but are just imputed in a naive way.
 See [Sifting Common Info...](https://arxiv.org/abs/1606.02307) and 
 [Maximally informative representations...](https://arxiv.org/abs/1410.7404) for work building up to this method. 
 The main paper describing the method is 
-[Low Complexity Gaussian Latent Factor Models and a Blessing of Dimensionality](https://arxiv.org/abs/1706.).
+[Low Complexity Gaussian Latent Factor Models and a Blessing of Dimensionality](https://arxiv.org/abs/1706.03353).
 The connections with the idea of "synergy" will be described in future work.