PhD

The LaTeX sources of my Ph.D. thesis
git clone https://esimon.eu/repos/PhD.git
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problems.tex (3118B)


      1 \begin{frame}{Source of Low Scores}%
      2 	\begin{block}{Degenerate distributions}
      3 		\begin{columns}%
      4 			\begin{column}{6.5cm}%
      5 				\centering%
      6 				\begin{tikzpicture}
      7 					\drawDistribution{activation}{\(P(\rndm{r}\mid s_1) = \)}{4.5}{0/0.32, 1/0.35, 2/0.31, 3/0.37, 4/0.38, 5/0.36, 6/0.34, 7/0.36, 8/0.36, 9/0.31}
      8 					\drawDistribution{activation}{\(P(\rndm{r}\mid s_2) = \)}{4}{0/0.31, 1/0.37, 2/0.38, 3/0.35, 4/0.32, 5/0.33, 6/0.37, 7/0.36, 8/0.32, 9/0.35}
      9 					\drawDistribution{activation}{\(P(\rndm{r}\mid s_3) = \)}{3.5}{0/0.32, 1/0.35, 2/0.31, 3/0.38, 4/0.32, 5/0.33, 6/0.37, 7/0.33, 8/0.32, 9/0.33}
     10 					\drawDistribution{activation}{\(P(\rndm{r}\mid s_4) = \)}{3}{0/0.33, 1/0.31, 2/0.34, 3/0.36, 4/0.34, 5/0.33, 6/0.37, 7/0.35, 8/0.36, 9/0.32}
     11 					\node at (0.75, 2.665) {\(\vdots\)};
     12 				\end{tikzpicture}%
     13 			\end{column}
     14 			\begin{column}{6.5cm}%
     15 				\centering%
     16 				\begin{tikzpicture}
     17 					\drawDistribution{activation}{\(P(\rndm{r}\mid s_1) = \)}{5.5}{0/0.02, 1/0.05, 2/0.01, 3/0.87, 4/0.08, 5/0.06, 6/0.04, 7/0.06, 8/0.06, 9/0.01}
     18 					\drawDistribution{activation}{\(P(\rndm{r}\mid s_2) = \)}{5}{0/0.01, 1/0.07, 2/0.08, 3/0.85, 4/0.02, 5/0.03, 6/0.07, 7/0.06, 8/0.02, 9/0.05}
     19 					\drawDistribution{activation}{\(P(\rndm{r}\mid s_3) = \)}{4.5}{0/0.02, 1/0.05, 2/0.01, 3/0.88, 4/0.02, 5/0.03, 6/0.07, 7/0.03, 8/0.02, 9/0.03}
     20 					\drawDistribution{activation}{\(P(\rndm{r}\mid s_4) = \)}{4}{0/0.03, 1/0.01, 2/0.04, 3/0.86, 4/0.04, 5/0.03, 6/0.07, 7/0.05, 8/0.06, 9/0.02}
     21 					\node at (0.75, 3.665) {\(\vdots\)};
     22 				\end{tikzpicture}%
     23 			\end{column}
     24 		\end{columns}
     25 	\end{block}
     26 	\begin{columns}
     27 		\begin{column}{6cm}%
     28 			\begin{block}{Desired distribution}%
     29 				\centering%
     30 				\begin{tikzpicture}
     31 					\drawDistribution{activation}{\(P(\rndm{r}\mid s_1) = \)}{1.5}{0/0.02, 1/0.05, 2/0.01, 3/0.07, 4/0.88, 5/0.06, 6/0.04, 7/0.06, 8/0.06, 9/0.01}
     32 					\drawDistribution{activation}{\(P(\rndm{r}\mid s_2) = \)}{1}{0/0.01, 1/0.07, 2/0.88, 3/0.05, 4/0.02, 5/0.03, 6/0.07, 7/0.06, 8/0.02, 9/0.05}
     33 					\drawDistribution{activation}{\(P(\rndm{r}\mid s_3) = \)}{0.5}{0/0.02, 1/0.05, 2/0.01, 3/0.88, 4/0.02, 5/0.03, 6/0.07, 7/0.03, 8/0.02, 9/0.03}
     34 					\drawDistribution{activation}{\(P(\rndm{r}\mid s_4) = \)}{0}{0/0.03, 1/0.01, 2/0.04, 3/0.06, 4/0.04, 5/0.03, 6/0.87, 7/0.05, 8/0.06, 9/0.02}
     35 					\node at (0.75, -0.335) {\(\vdots\)};
     36 				\end{tikzpicture}%
     37 			\end{block}
     38 		\end{column}%
     39 		\begin{column}{7cm}%
     40 			\begin{block}{VAE Model Reminder (Marcheggiani)}
     41 				\vspace*{3mm}%
     42 				\(\displaystyle\overbrace{P(e_{-i} \mid s, e_i)}^{\text{fill-in-the-blank}} = \sum_{r\in\relationSet} \overbrace{P(r\mid s)}^{\text{classifier}} \overbrace{P(e_{-i} \mid r, e_i)}^{\text{entity predictor}}\)
     43 
     44 				\bigskip
     45 
     46 				\(\loss{vae reg}(\vctr{\phi}) = \kl(Q(\rndm{r}\mid \rndmvctr{e}; \vctr{\phi}) \mathrel{\|} \uniformDistribution(\relationSet))\)
     47 			\end{block}
     48 		\end{column}%
     49 	\end{columns}%
     50 	\pause
     51 	\begin{tikzpicture}[overlay, remember picture]
     52 		\node[inner sep=0, draw=black] at (current page.center) {\problemBoxContent{Marcheggiani's model cannot handle deep encoder.}};
     53 	\end{tikzpicture}
     54 \end{frame}