# HG changeset patch
# User Dumitru Erhan <dumitru.erhan@gmail.com>
# Date 1275442123 25200
# Node ID 47894d0ecbde3a487d5d6cc03b5cb919611e7390
# Parent  5157a583012508797ded72fa22bfc1b6a626ff11# Parent  22d5cd82d5f08cc0123105f955c7c16014e6be0e
merge

diff -r 22d5cd82d5f0 -r 47894d0ecbde writeup/nips2010_submission.tex
--- a/writeup/nips2010_submission.tex	Tue Jun 01 21:24:39 2010 -0400
+++ b/writeup/nips2010_submission.tex	Tue Jun 01 18:28:43 2010 -0700
@@ -692,7 +692,7 @@
 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}.