--- a/writeup/nips2010_submission.tex	Tue Jun 01 18:19:40 2010 -0700
+++ b/writeup/nips2010_submission.tex	Tue Jun 01 18:28:09 2010 -0700
@@ -691,7 +691,7 @@
 experiments showed its positive effects in a \emph{limited labeled data}
 scenario. However, many of the results by \citet{RainaR2007} (who used a
 shallow, sparse coding approach) suggest that the relative gain of self-taught
-learning diminishes as the number of labeled examples increases, (essentially,
+learning diminishes as the number of labeled examples increases (essentially,
 a ``diminishing returns'' scenario occurs).  We note instead that, for deep
 architectures, our experiments show that such a positive effect is accomplished
 even in a scenario with a \emph{very large number of labeled examples}.