From 86208a1bd7e6a7735340b318be4b40ced8448f1a Mon Sep 17 00:00:00 2001
From: jeanpouget-abadie <jean.pougetabadie@gmail.com>
Date: Thu, 5 Feb 2015 12:42:12 -0500
Subject: adding four figures

---
 paper/sections/experiments.tex | 21 ++++++++++++---------
 1 file changed, 12 insertions(+), 9 deletions(-)

(limited to 'paper/sections/experiments.tex')

diff --git a/paper/sections/experiments.tex b/paper/sections/experiments.tex
index 600cac3..ccd82ce 100644
--- a/paper/sections/experiments.tex
+++ b/paper/sections/experiments.tex
@@ -3,15 +3,18 @@
 \caption{Precision-Recall curve Holme-Kim Model. 200 nodes, 16200 edges.}
 \end{figure}
 
-\begin{figure}
-\includegraphics[scale=.4]{figures/watts_strogatz.pdf}
-\caption{Watts-Strogatz Model. 200 nodes, 20000 edges.}
-\end{figure}
-
-\begin{figure}
-\includegraphics[scale=.4]{figures/barabasi_albert.pdf}
-\caption{Barabasi Model.}
-\end{figure}
+\begin{table*}[t]
+\centering
+\begin{tabular}{c c c c}
+
+\includegraphics[scale=.21]{figures/barabasi_albert.pdf} 
+& \includegraphics[scale=.21]{figures/watts_strogatz.pdf}
+& \includegraphics[scale=.23]{figures/kronecker_l2_norm.pdf}
+& \includegraphics[scale=.23]{figures/kronecker_l2_norm_nonsparse.pdf}\\
+(a) & (b) & (c) & (d)
+\end{tabular}
+\captionof{figure}{blabla}
+\end{table*}
 
 In this section, we validate empirically the results and assumptions of Section~\ref{sec:results} for different initializations of parameters ($n$, $m$, $\lambda$) and for varying levels of sparsity. We compare our algorithm to two different state-of-the-art algorithms: \textsc{greedy} and \textsc{mle} from \cite{Netrapalli:2012}. As an extra benchmark, we also introduce a new algorithm \textsc{lasso}, which approximates our \textsc{sparse mle} algorithm. We find empirically that \textsc{lasso} is highly robust, and can be computed more efficiently than both \textsc{mle} and \textsc{sparse mle} without sacrificing for performance.
 
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cgit v1.2.3-70-g09d2

