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Separate semi bimodal plots; add likelihood terms

master
Markus Kaiser 2 years ago
parent
commit
39bc2c7df8
  1. 1
      ecml-poster/figures/noise_separation_attribution.tex
  2. 1
      ecml-poster/figures/noise_separation_data.tex
  3. 4
      ecml-poster/figures/preamble/tikz_style.tex
  4. 48
      ecml-poster/figures/semi_bimodal_attribution.tex
  5. 23
      ecml-poster/figures/semi_bimodal_data.tex
  6. 59
      ecml-poster/figures/semi_bimodal_joint.tex
  7. 33
      ecml-poster/figures/semi_bimodal_predictive_attribution.tex
  8. 118
      ecml-poster/poster.tex

1
ecml-poster/figures/noise_separation_attribution.tex

@ -10,6 +10,7 @@
\def\datapath{\figurepath/data/choicenet_noisy_0.6}
\begin{axis}[
choicenet plot,
xlabel=,
]
\addplot[

1
ecml-poster/figures/noise_separation_data.tex

@ -10,6 +10,7 @@
\def\datapath{\figurepath/data/choicenet_noisy_0.6}
\begin{axis}[
choicenet plot,
xlabel=,
]
\addplot[

4
ecml-poster/figures/preamble/tikz_style.tex

@ -40,7 +40,7 @@
\pgfplotsset{choicenet plot/.style = {
model plot,
width=.8\textwidth,
height=175pt,
height=180pt,
clip mode=individual,
xlabel=$\rv{X}$, ylabel=$\rv{y}$,
ymin=-1.5, ymax=3.5,
@ -51,7 +51,7 @@
\pgfplotsset{multimodal plot/.style = {
model plot,
width=.8\textwidth,
height=175pt,
height=180pt,
clip mode=individual,
xlabel=$\rv{X}$, ylabel=$\rv{y}$,
ymin=-5, ymax=3,

48
ecml-poster/figures/semi_bimodal_attribution.tex

@ -0,0 +1,48 @@
\documentclass[beamer,tikz,crop]{standalone}
\input{preamble/tikz_standalone.tex}
\input{preamble/tikz_common.tex}
\input{preamble/tikz_style.tex}
\input{preamble/tikz_colors.tex}
\input{../abbreviations.tex}
\begin{document}
\begin{tikzpicture}
\def\datapath{\figurepath/data/semi_bimodal_fancy}
\begin{axis}[
multimodal plot,
xlabel=,
]
\addplot[
data,
fourth,
mark=square*,
] table[
ignore chars={\#}, col sep=space, x=X, y=Y
] {\datapath/Xs_Ys_attrib_separated_1.dat};
\addplot[
data,
third,
mark=triangle*,
] table[
ignore chars={\#}, col sep=space, x=X, y=Y
] {\datapath/Xs_Ys_attrib_separated_0.dat};
\addplot[
data,
second,
mark=diamond*,
] table[
ignore chars={\#}, col sep=space, x=X, y=Y
] {\datapath/Xs_Ys_attrib_separated_2.dat};
\addplot[
data,
first,
mark=pentagon*,
] table[
ignore chars={\#}, col sep=space, x=X, y=Y
] {\datapath/Xs_Ys_attrib_separated_3.dat};
\end{axis}
\end{tikzpicture}
\end{document}

23
ecml-poster/figures/semi_bimodal_data.tex

@ -0,0 +1,23 @@
\documentclass[beamer,tikz,crop]{standalone}
\input{preamble/tikz_standalone.tex}
\input{preamble/tikz_common.tex}
\input{preamble/tikz_style.tex}
\input{preamble/tikz_colors.tex}
\input{../abbreviations.tex}
\begin{document}
\begin{tikzpicture}
\def\datapath{\figurepath/data/semi_bimodal_fancy}
\begin{axis}[
multimodal plot,
xlabel=,
]
\addplot[
data,
] table[
ignore chars={\#}, col sep=space, x=X, y=Y
] {\datapath/Xs_Ys.dat};
\end{axis}
\end{tikzpicture}
\end{document}

59
ecml-poster/figures/semi_bimodal_joint.tex

@ -0,0 +1,59 @@
\documentclass[beamer,tikz,crop]{standalone}
\input{preamble/tikz_standalone.tex}
\input{preamble/tikz_common.tex}
\input{preamble/tikz_style.tex}
\input{preamble/tikz_colors.tex}
\input{../abbreviations.tex}
\begin{document}
\begin{tikzpicture}
\def\datapath{\figurepath/data/semi_bimodal_fancy}
\begin{axis}[
multimodal plot,
xlabel=,
]
\addplot[
data,
] table[
ignore chars={\#}, col sep=space, x=X, y=Y
] {\datapath/Xs_Ys.dat};
\addplot[std, fourth, name path=minus]
table[ignore chars={\#}, col sep=space, x=X, y expr=\thisrow{mu} - \thisrow{2std}] {\datapath/Xt_mode_1.dat};
\addplot[std, fourth, name path=plus]
table[ignore chars={\#}, col sep=space, x=X, y expr=\thisrow{mu} + \thisrow{2std}] {\datapath/Xt_mode_1.dat};
\addplot[fourth fill]
fill between[of=plus and minus];
\addplot[mean, fourth]
table[ignore chars={\#}, col sep=space, x=X, y=mu] {\datapath/Xt_mode_1.dat};
\addplot[std, third, name path=plus]
table[ignore chars={\#}, col sep=space, x=X, y expr=\thisrow{mu} + \thisrow{2std}] {\datapath/Xt_mode_0.dat};
\addplot[std, third, name path=minus]
table[ignore chars={\#}, col sep=space, x=X, y expr=\thisrow{mu} - \thisrow{2std}] {\datapath/Xt_mode_0.dat};
\addplot[third fill]
fill between[of=plus and minus];
\addplot[mean, third]
table[ignore chars={\#}, col sep=space, x=X, y=mu] {\datapath/Xt_mode_0.dat};
\addplot[std, second, name path=plus]
table[ignore chars={\#}, col sep=space, x=X, y expr=\thisrow{mu} + \thisrow{2std}] {\datapath/Xt_mode_2.dat};
\addplot[std, second, name path=minus]
table[ignore chars={\#}, col sep=space, x=X, y expr=\thisrow{mu} - \thisrow{2std}] {\datapath/Xt_mode_2.dat};
\addplot[second fill]
fill between[of=plus and minus];
\addplot[mean, second]
table[ignore chars={\#}, col sep=space, x=X, y=mu] {\datapath/Xt_mode_2.dat};
\addplot[std, first, name path=plus]
table[ignore chars={\#}, col sep=space, x=X, y expr=\thisrow{mu} + \thisrow{2std}] {\datapath/Xt_mode_3.dat};
\addplot[std, first, name path=minus]
table[ignore chars={\#}, col sep=space, x=X, y expr=\thisrow{mu} - \thisrow{2std}] {\datapath/Xt_mode_3.dat};
\addplot[first fill]
fill between[of=plus and minus];
\addplot[mean, first]
table[ignore chars={\#}, col sep=space, x=X, y=mu] {\datapath/Xt_mode_3.dat};
\end{axis}
\end{tikzpicture}
\end{document}

33
ecml-poster/figures/semi_bimodal_predictive_attribution.tex

@ -0,0 +1,33 @@
\documentclass[beamer,tikz,crop]{standalone}
\input{preamble/tikz_standalone.tex}
\input{preamble/tikz_common.tex}
\input{preamble/tikz_style.tex}
\input{preamble/tikz_colors.tex}
\input{../abbreviations.tex}
\begin{document}
\begin{tikzpicture}
\def\datapath{\figurepath/data/semi_bimodal_fancy}
\begin{axis}[
multimodal plot,
xlabel=$\rv{X}$, ylabel=$\Fun{\softmax}{\rv{\alpha}}$,
ylabel style={rotate=90},
ymin=-0.05, ymax=0.8,
ytick={0, 0.33, 0.66},
]
\pgfplotsset{restrict/.style = {
restrict x to domain=-10:10,
}}
\addplot[very thick, third, restrict, densely dotted]
table[ignore chars={\#}, col sep=space, x=X, y=mode_0] {\datapath/Xt_mode_prob.dat};
\addplot[very thick, fourth, restrict, densely dashed]
table[ignore chars={\#}, col sep=space, x=X, y=mode_1] {\datapath/Xt_mode_prob.dat};
\addplot[very thick, second, restrict]
table[ignore chars={\#}, col sep=space, x=X, y=mode_2] {\datapath/Xt_mode_prob.dat};
\addplot[very thick, first, restrict, dashdotted]
table[ignore chars={\#}, col sep=space, x=X, y=mode_3] {\datapath/Xt_mode_prob.dat};
\end{axis}
\end{tikzpicture}
\end{document}

118
ecml-poster/poster.tex

@ -92,7 +92,34 @@
\begin{block}{Multimodal Data}
\begin{figure}
\centering
\includestandalonewithpath{figures/semi_bimodal}
\begin{subfigure}{\textwidth}
\centering
\includestandalonewithpath{figures/semi_bimodal_data}
\caption{
Data
}
\end{subfigure}\\[2ex]
\begin{subfigure}{\textwidth}
\centering
\includestandalonewithpath{figures/semi_bimodal_attribution}
\caption{
Attribution
}
\end{subfigure}\\[2ex]
\begin{subfigure}{\textwidth}
\centering
\includestandalonewithpath{figures/semi_bimodal_joint}
\caption{
Joint
}
\end{subfigure}\\[2ex]
\begin{subfigure}{\textwidth}
\centering
\includestandalonewithpath{figures/semi_bimodal_predictive_attribution}
\caption{
Attribution predictions
}
\end{subfigure}
\end{figure}
\begin{itemize}
\item AMO-GP correctly recovers the latent shared function, warping and alignment
@ -132,17 +159,34 @@
\separatorcolumn
%
\begin{column}{\colwidth}
\begin{block}{Graphical model of DAGP}
\begin{block}{Modelling Assumptions of DAGP}
\begin{figure}
\centering
\includestandalonewithpath{figures/graphical_model}
\end{figure}
\end{block}
\vspace{3ex}
\begin{block}{Scalable Inference}
\begin{itemize}
\item AMO-GP connects multiple deep GPs via a shared layer which is a multi-output GP
\smallskip
\item Marginal Likelihood
\begin{align}
\Prob*{\mat{Y} \given \mat{X}} &=
\int
\Prob*{\mat{Y} \given \mat{F}, \mat{A}}
\Prob*{\mat{F} \given \mat{X}}
\Prob*{\mat{A} \given \mat{X}}
\diff \mat{A} \diff \mat{F} \\
\Prob*{\mat{Y} \given \mat{F}, \mat{A}} &=
\prod_{n=1}^N\prod_{k=1}^K
\Gaussian*{\mat{y_n} \given \mat{f_n^{\pix{k}}}, \left(\sigma^{\pix{k}}\right)^2}^{\Fun{\Ind}{a_n^{\pix{k}} = 1}}\\
\Prob*{\mat{A} \given \mat{X}} &=
\int
\Multinomial*{\mat{A} \given \Fun{\softmax}{\mat{\alpha}}} \Prob*{\mat{\alpha} \given \mat{X}}
\diff \rv{\alpha}
\end{align}
\end{itemize}
\end{block}
\begin{block}{Scalable Inference}
\begin{itemize}
\item Application to non-linear time series alignment with very noisy observations
\smallskip
\item Variational distribution
@ -190,38 +234,38 @@
\centering
\includestandalonewithpath{figures/wetchicken}
\end{block}
% \begin{block}{Interpretable Transition Model}
% \begin{figure}
% \centering
% \includestandalonewithpath{figures/dynamics_posterior}
% \end{figure}
% \begin{itemize}
% \item AMO-GP correctly recovers the latent shared function, warping and alignment
% \end{itemize}
% \end{block}
% \begin{block}{Conservative Policy}
% \begin{figure}
% \centering
% \begin{subfigure}{.475\textwidth}
% \centering
% \includestandalonewithpath{figures/policy_quiver}
% \caption{
% $\Fun*{R}{x, y} = x$
% }
% \end{subfigure}
% \hfill
% \begin{subfigure}{.475\textwidth}
% \centering
% \includestandalonewithpath{figures/conservative_policy_quiver}
% \caption{
% $\Fun*{R^\prime}{x, y} = \Fun*{R}{x, y} - 5 \cdot \Prob{\text{drop} \given x, y}$
% }
% \end{subfigure}
% \end{figure}
% \begin{itemize}
% \item AMO-GP correctly recovers the latent shared function, warping and alignment
% \end{itemize}
% \end{block}
\begin{block}{Interpretable Transition Model}
\begin{figure}
\centering
\includestandalonewithpath{figures/dynamics_posterior}
\end{figure}
\begin{itemize}
\item AMO-GP correctly recovers the latent shared function, warping and alignment
\end{itemize}
\end{block}
\begin{block}{Conservative Policy}
\begin{figure}
\centering
\begin{subfigure}{.475\textwidth}
\centering
\includestandalonewithpath{figures/policy_quiver}
\caption{
$\Fun*{R}{x, y} = x$
}
\end{subfigure}
\hfill
\begin{subfigure}{.475\textwidth}
\centering
\includestandalonewithpath{figures/conservative_policy_quiver}
\caption{
$\Fun*{R^\prime}{x, y} = \Fun*{R}{x, y} - 5 \cdot \Prob{\text{drop} \given x, y}$
}
\end{subfigure}
\end{figure}
\begin{itemize}
\item AMO-GP correctly recovers the latent shared function, warping and alignment
\end{itemize}
\end{block}
\end{column}
%
\separatorcolumn

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