overview.tex (5674B)
1 \begin{tikzpicture} 2 [ 3 font=\sffamily, 4 box/.style = {rounded corners, minimum width=22mm, minimum height=5mm, align=center}, 5 ] 6 \newcommand{\myrel}[2]{\mathrel{\ooalign{\raisebox{2.2\height}{#1}\cr\hidewidth#2\hidewidth\cr}}} 7 % \definecolor{color1}{rgb}{0.86, 0.82, 1.0} 8 % \definecolor{color2}{rgb}{0.97, 0.96, 1.0} 9 \definecolor{color1}{rgb}{0.74, 0.83, 0.9} 10 \definecolor{color2}{rgb}{0.94, 0.97, 1.0} 11 \definecolor{kaogreen}{HTML}{91C889} 12 \node (nn) [box,fill=color1,minimum height=1.0cm,minimum width=3cm] {Neural Network}; 13 \node (max) [below=of nn,yshift=-0.3cm] {\(\argmax_{\bm \theta}\)}; 14 \node (posterior) [box,fill=color2,minimum width=1.8cm,minimum height=2.1cm,below=of max,xshift=-1.1cm,yshift=0.5cm] {\(\log p(\bm \theta \given \bm y)\)}; 15 \node (marginal) [box,fill=color2,minimum width=1.8cm,minimum height=2.1cm,below=of max,xshift=1.1cm,yshift=0.5cm] {\(\overbrace{\myrel{\text{\normalsize \(\log p(\bm \theta \given \bm y)\)}}{\text{\normalsize \colorbox{kaogreen}{$\log \det \bm \Lambda$}}}}^{\text{\normalsize \(\log p(\bm y)\)}}\)}; 16 % \node (marginal) [box,fill=color2,minimum width=3.8cm,minimum height=2cm,below=of max,xshift=2cm,yshift=0.5cm] {\(\overbrace{\log p(\bm \theta \given \bm y) \quad \text{\colorbox{kaogreen}{$\log \det \bm \Lambda$}}}^{\text{\normalsize \(\log p(\bm y)\)}}\)}; 17 \draw[dashed] (nn) to (max); 18 \draw[dashed,->] (max) to [bend left] (posterior.60); 19 \draw[dashed,->] (max) to [bend right] (marginal.120); 20 \begin{scope}[on background layer] 21 \node (training) [box,fill=color1,minimum width=3.8cm,inner sep=0.25cm,minimum height=3.2cm,fit={(max)(posterior)(marginal)}] {}; 22 \end{scope} 23 \node [above=0cm and 0cm of training.north west,anchor=south west] {Training}; 24 \node [above=0cm and 0cm of posterior.north west,anchor=south west] {{Post-Hoc}}; 25 \node [above=0cm and 0cm of marginal.north east,anchor=south east] {Online}; 26 % \node [above=0cm and 0cm of training.north east,anchor=south east] {\cref{ch:training}}; 27 % 28 \node (map) [below=of training,yshift=0.7cm] {\(\bm \theta_{\textsc{map}}\)}; 29 \draw[dashed] (training) to (map); 30 \node (sample) [below=of map,yshift=0.5cm] {Sample}; 31 \node (samplingeq) [below=0cm and 0cm of sample,box,fill=color2,minimum width=4.17cm,minimum height=2.1cm] {\(\begin{aligned}\bm \varepsilon \sim{}& \normal(\bm \theta_{\textsc{map}}, \bm \Lambda^{-1}) \\ ={}& \bm \theta_{\textsc{map}} + \text{\colorbox{kaogreen}{$\bm \Lambda^{-1/2} \bm \varepsilon_0$}}\end{aligned}\)}; 32 \begin{scope}[on background layer] 33 \node (inference) [box,fill=color1,inner sep=0.25cm,fit={(sample)(samplingeq)}] {}; % minimum width=8.05cm,minimum height=3.2cm, 34 \end{scope} 35 \node [above=0cm and 0cm of inference.north west,anchor=south west] {Inference}; 36 \draw[dashed,->] (map) to (inference); 37 % \node [above=0cm and 0cm of inference.north east,anchor=south east] {\cref{ch:sampling}}; 38 \end{tikzpicture} 39 40 41 % \begin{tikzpicture} 42 % [ 43 % font=\sffamily, 44 % box/.style = {rounded corners, minimum width=22mm, minimum height=5mm, align=center}, 45 % ] 46 % % \definecolor{color1}{rgb}{0.86, 0.82, 1.0} 47 % % \definecolor{color2}{rgb}{0.97, 0.96, 1.0} 48 % \definecolor{color1}{rgb}{0.74, 0.83, 0.9} 49 % \definecolor{color2}{rgb}{0.94, 0.97, 1.0} 50 % \definecolor{kaogreen}{HTML}{91C889} 51 % \node (nn) [box,fill=color1,minimum height=1.0cm,minimum width=3cm] {Neural Network}; 52 % \node (max) [below=of nn,yshift=0.5cm] {\(\argmax_{\bm \theta}\)}; 53 % \node (posterior) [box,fill=color2,minimum width=3.8cm,minimum height=2cm,below=of max,xshift=-2cm,yshift=0.5cm] {\(\log p(\bm \theta \given \bm y)\)}; 54 % \node (marginal) [box,fill=color2,minimum width=3.8cm,minimum height=2cm,below=of max,xshift=2cm,yshift=0.5cm] {\(\overbrace{\log p(\bm \theta \given \bm y) \quad \text{\colorbox{kaogreen}{$\log \det \bm \Lambda$}}}^{\text{\normalsize \(\log p(\bm y)\)}}\)}; 55 % \draw[dashed] (nn) to (max); 56 % \draw[dashed,->] (max) to [bend right] (posterior); 57 % \draw[dashed,->] (max) to [bend left] (marginal); 58 % \begin{scope}[on background layer] 59 % \node (training) [box,fill=color1,minimum width=8.05cm,inner sep=0.25cm,minimum height=3.2cm,fit={(max)(posterior)(marginal)}] {}; 60 % \end{scope} 61 % \node [above=0cm and 0cm of training.north west,anchor=south west] {Training}; 62 % \node [above=0cm and 0cm of posterior.north west,anchor=south west] {{Post-Hoc}}; 63 % \node [above=0cm and 0cm of marginal.north east,anchor=south east] {Online}; 64 % % \node [above=0cm and 0cm of training.north east,anchor=south east] {\cref{ch:training}}; 65 % % 66 % \node (map) [below=of training,yshift=0.7cm] {\(\bm \theta_{\textsc{map}}\)}; 67 % \draw[dashed] (training) to (map); 68 % \node (sample) [below=of map,yshift=0.5cm] {Sample}; 69 % \node (samplingeq) [below=0cm and 0cm of sample,box,fill=color2,minimum width=3.8cm,minimum height=2cm] {\(\begin{aligned}\bm \varepsilon \sim{}& \normal(\bm \theta_{\textsc{map}}, \bm \Lambda^{-1}) \\ ={}& \bm \theta_{\textsc{map}} + \text{\colorbox{kaogreen}{$\bm \Lambda^{-1/2} \bm \varepsilon_0$}}\end{aligned}\)}; 70 % \begin{scope}[on background layer] 71 % \node (inference) [box,fill=color1,inner sep=0.25cm,fit={(sample)(samplingeq)}] {}; % minimum width=8.05cm,minimum height=3.2cm, 72 % \end{scope} 73 % \node [above=0cm and 0cm of inference.north west,anchor=south west] {Inference}; 74 % \draw[dashed,->] (map) to (inference); 75 % % \node [above=0cm and 0cm of inference.north east,anchor=south east] {\cref{ch:sampling}}; 76 % \end{tikzpicture}