# HG changeset patch
# User Dumitru Erhan <dumitru.erhan@gmail.com>
# Date 1275446062 25200
# Node ID 84f42fe05594a0016d14484d8b09ce972980bd42
# Parent  f0ee2212ea7cbf49c94da985792c1da14481165f# Parent  caf7769ca19c0e71bb5b27e1f112e692213e3fb0
merge

diff -r caf7769ca19c -r 84f42fe05594 writeup/nips2010_submission.tex
--- a/writeup/nips2010_submission.tex	Tue Jun 01 22:25:35 2010 -0400
+++ b/writeup/nips2010_submission.tex	Tue Jun 01 19:34:22 2010 -0700
@@ -334,7 +334,7 @@
 
 \iffalse
 \begin{figure}[ht]
-\centerline{\resizebox{.99\textwidth}{!}{\includegraphics{images/example_t.png}}}\\
+\centerline{\resizebox{.9\textwidth}{!}{\includegraphics{images/example_t.png}}}\\
 \caption{Illustration of the pipeline of stochastic 
 transformations applied to the image of a lower-case \emph{t}
 (the upper left image). Each image in the pipeline (going from
@@ -700,7 +700,7 @@
 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}.