diff --git a/07_final_assignment/paper/main.tex b/07_final_assignment/paper/main.tex
index 6ba1638..06d734f 100644
--- a/07_final_assignment/paper/main.tex
+++ b/07_final_assignment/paper/main.tex
@@ -65,12 +65,28 @@
 \section{Results}
 \todo{results}
 
-\begin{figure*}
-\includegraphics[width=0.9\textwidth]{plots/plot_accuracy}
+\begin{figure*}[ht]
+  \centering
+  \includegraphics[width=0.9\textwidth]{../plots/plot_accuracy}
+  \caption{
+    Top row shows model output  accuracies in dependence of modulated alpha and beta.
+    Second row visualizes corresponding nonlinear regressions (GAM).
+    Accuracy seem to approximate a maximal accuracy with growing alpha, beta parameter.
+    In the GAM you see the small influence of one of the parameter.
+    Therefore this resoults could be approximated with just one nonlinear parameter.
+  }
+  \label{fig:accuracy}
 \end{figure*}
 
-\begin{figure*}
-\includegraphics[width=0.9\textwidth]{plots/plot_numwords}
+\begin{figure*}[ht]
+  \centering
+  \includegraphics[width=0.9\textwidth]{../plots/plot_numwords}
+  \caption{
+    The left plot shows the raw num words learned of the model with modulated parameter (alpha, beta).
+    The model performs always quite well, just several parameter value result in lower perfomance. The corresponding nonlinear regression plot (middle) doesn't mirror a first hipothesis of growing words learned with growing parameter values.
+    This is not necessarily a consequense of a wrong hypothesis but of a wrong regression model because of the weak data with very high frequency of around 305 learned words but almost no other number of learned words (right plot).
+}
+  \label{fig:numwords}
 \end{figure*}