initial push

data/000.txt

@@ -0,0 +1,5 @@
+<frame >
+        
+        <titlepage>
+        
+        </frame>

data/001Task.txt

@@ -0,0 +1,3 @@
+<frame title="Task">
+<i f="../prep/01Task/table.jpg"></i>
+</frame>

data/002Intro.txt

@@ -0,0 +1,22 @@
+<frame title="Intro">
+        <list>
+        <e>Motivation</e>
+<l2st>
+<e>AD is super important...</e>
+<e>good AD = complicated models -> Many Parameters</e>
+<e>Evaluation dependent on very few datapoints->Optimization impossible</e>
+</l2st>
+<e>Open Challenges</e>
+<l2st>
+<e>Evaluate without testing data</e>
+<e>Formalisation of existing ideas</e>
+<e>Numerical assessment of them</e>
+</l2st>
+<e>Contribution</e>
+<l2st>
+<e>Suggest new methods for AE</e>
+<e>Compare methods on many datasets</e>
+<e>Seperate into parameter and hyperparameter optimisation</e>
+</l2st>
+        </list>
+        </frame>

data/003RW.txt

@@ -0,0 +1,3 @@
+<frame title="RW">
+<i f="../prep/04RW/graph.png"></i>
+</frame>

data/004RW.txt

@@ -0,0 +1,3 @@
+<frame title="RW">
+<i f="../prep/05RW/graph.png"></i>
+</frame>

data/005But....txt

@@ -0,0 +1,3 @@
+<frame title="But...">
+<i f="../prep/06But.../graph.png"></i>
+</frame>

data/006Problem Statement.txt

@@ -0,0 +1,6 @@
+<frame title="Problem Statement">
+        <list>
+        <e>Given $N$ Anomaly detection methods $M_i = TrainModel(X_{train})$, find $f(M_i)$ so that Score $S_i = f(M_i)$ can be used to find an above average AD method $M_{argmax(S)}$.</e>
+<e>Let $TrainMany(X_{train},C)=TrainModel(X_{train})_{argmax(f(M_0...M_C))}$. We assume the distribution of $TrainMany$ to be gaussian and describe it through $\mu_C$ and $\sigma_C$. We consider a function $f(M)$ to be helpful, if $\Delta = \frac{sqrt(N) \cdot (\mu_C-\mu_1)}{sqrt(\sigma_C^2+\sigma_1^2)} > 3$ for some number of models tested $N$.</e>
+        </list>
+        </frame>

general.txt

@@ -0,0 +1,12 @@
+<plt>
+
+<name Current experiment status>
+<title Unsupervised Optimisation - Paper Structure>
+<stitle u-Opt>
+
+<institute ls9 tu Dortmund>
+
+<theme CambridgeUS>
+<colo dolphin>
+
+</plt>

out/compile.bat

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+pdflatex main.tex
+pdflatex main.tex
+

out/compile.sh

@@ -0,0 +1,3 @@
+pdflatex main.tex
+pdflatex main.tex
+

out/label.json

@@ -0,0 +1,38 @@
+[
+  {
+    "typ": "img",
+    "files": [
+      "../prep/01Task/table.jpg"
+    ],
+    "label": "prep01Tasktablejpg",
+    "caption": "",
+    "where": "../uopt/data/001Task.txt"
+  },
+  {
+    "typ": "img",
+    "files": [
+      "../prep/04RW/graph.png"
+    ],
+    "label": "prep04RWgraphpng",
+    "caption": "",
+    "where": "../uopt/data/003RW.txt"
+  },
+  {
+    "typ": "img",
+    "files": [
+      "../prep/05RW/graph.png"
+    ],
+    "label": "prep05RWgraphpng",
+    "caption": "",
+    "where": "../uopt/data/004RW.txt"
+  },
+  {
+    "typ": "img",
+    "files": [
+      "../prep/06But.../graph.png"
+    ],
+    "label": "prep06Butgraphpng",
+    "caption": "",
+    "where": "../uopt/data/005But....txt"
+  }
+]

out/main.aux

@@ -0,0 +1,70 @@
+\relax 
+\providecommand\hyper@newdestlabel[2]{}
+\providecommand\HyperFirstAtBeginDocument{\AtBeginDocument}
+\HyperFirstAtBeginDocument{\ifx\hyper@anchor\@undefined
+\global\let\oldcontentsline\contentsline
+\gdef\contentsline#1#2#3#4{\oldcontentsline{#1}{#2}{#3}}
+\global\let\oldnewlabel\newlabel
+\gdef\newlabel#1#2{\newlabelxx{#1}#2}
+\gdef\newlabelxx#1#2#3#4#5#6{\oldnewlabel{#1}{{#2}{#3}}}
+\AtEndDocument{\ifx\hyper@anchor\@undefined
+\let\contentsline\oldcontentsline
+\let\newlabel\oldnewlabel
+\fi}
+\fi}
+\global\let\hyper@last\relax 
+\gdef\HyperFirstAtBeginDocument#1{#1}
+\providecommand\HyField@AuxAddToFields[1]{}
+\providecommand\HyField@AuxAddToCoFields[2]{}
+\@writefile{nav}{\headcommand {\slideentry {0}{0}{1}{1/1}{}{0}}}
+\@writefile{nav}{\headcommand {\beamer@framepages {1}{1}}}
+\newlabel{Task<1>}{{2}{2}{}{Doc-Start}{}}
+\@writefile{snm}{\beamer@slide {Task<1>}{2}}
+\newlabel{Task}{{2}{2}{}{Doc-Start}{}}
+\@writefile{snm}{\beamer@slide {Task}{2}}
+\newlabel{fig:prep01Tasktablejpg}{{2}{2}{}{Doc-Start}{}}
+\@writefile{snm}{\beamer@slide {fig:prep01Tasktablejpg}{2}}
+\@writefile{nav}{\headcommand {\slideentry {0}{0}{2}{2/2}{}{0}}}
+\@writefile{nav}{\headcommand {\beamer@framepages {2}{2}}}
+\newlabel{Intro<1>}{{3}{3}{}{Doc-Start}{}}
+\@writefile{snm}{\beamer@slide {Intro<1>}{3}}
+\newlabel{Intro}{{3}{3}{}{Doc-Start}{}}
+\@writefile{snm}{\beamer@slide {Intro}{3}}
+\@writefile{nav}{\headcommand {\slideentry {0}{0}{3}{3/3}{}{0}}}
+\@writefile{nav}{\headcommand {\beamer@framepages {3}{3}}}
+\newlabel{RW<1>}{{4}{4}{}{Doc-Start}{}}
+\@writefile{snm}{\beamer@slide {RW<1>}{4}}
+\newlabel{RW}{{4}{4}{}{Doc-Start}{}}
+\@writefile{snm}{\beamer@slide {RW}{4}}
+\newlabel{fig:prep04RWgraphpng}{{4}{4}{}{Doc-Start}{}}
+\@writefile{snm}{\beamer@slide {fig:prep04RWgraphpng}{4}}
+\@writefile{nav}{\headcommand {\slideentry {0}{0}{4}{4/4}{}{0}}}
+\@writefile{nav}{\headcommand {\beamer@framepages {4}{4}}}
+\newlabel{RW<1>}{{5}{5}{}{Doc-Start}{}}
+\@writefile{snm}{\beamer@slide {RW<1>}{5}}
+\newlabel{RW}{{5}{5}{}{Doc-Start}{}}
+\@writefile{snm}{\beamer@slide {RW}{5}}
+\newlabel{fig:prep05RWgraphpng}{{5}{5}{}{Doc-Start}{}}
+\@writefile{snm}{\beamer@slide {fig:prep05RWgraphpng}{5}}
+\@writefile{nav}{\headcommand {\slideentry {0}{0}{5}{5/5}{}{0}}}
+\@writefile{nav}{\headcommand {\beamer@framepages {5}{5}}}
+\newlabel{But...<1>}{{6}{6}{}{Doc-Start}{}}
+\@writefile{snm}{\beamer@slide {But...<1>}{6}}
+\newlabel{But...}{{6}{6}{}{Doc-Start}{}}
+\@writefile{snm}{\beamer@slide {But...}{6}}
+\newlabel{fig:prep06Butgraphpng}{{6}{6}{}{Doc-Start}{}}
+\@writefile{snm}{\beamer@slide {fig:prep06Butgraphpng}{6}}
+\@writefile{nav}{\headcommand {\slideentry {0}{0}{6}{6/6}{}{0}}}
+\@writefile{nav}{\headcommand {\beamer@framepages {6}{6}}}
+\newlabel{Problem Statement<1>}{{7}{7}{}{Doc-Start}{}}
+\@writefile{snm}{\beamer@slide {Problem Statement<1>}{7}}
+\newlabel{Problem Statement}{{7}{7}{}{Doc-Start}{}}
+\@writefile{snm}{\beamer@slide {Problem Statement}{7}}
+\@writefile{nav}{\headcommand {\slideentry {0}{0}{7}{7/7}{}{0}}}
+\@writefile{nav}{\headcommand {\beamer@framepages {7}{7}}}
+\@writefile{nav}{\headcommand {\beamer@partpages {1}{7}}}
+\@writefile{nav}{\headcommand {\beamer@subsectionpages {1}{7}}}
+\@writefile{nav}{\headcommand {\beamer@sectionpages {1}{7}}}
+\@writefile{nav}{\headcommand {\beamer@documentpages {7}}}
+\@writefile{nav}{\headcommand {\gdef \inserttotalframenumber {7}}}
+\gdef \@abspage@last{7}

out/main.nav

@@ -0,0 +1,19 @@
+\headcommand {\slideentry {0}{0}{1}{1/1}{}{0}}
+\headcommand {\beamer@framepages {1}{1}}
+\headcommand {\slideentry {0}{0}{2}{2/2}{}{0}}
+\headcommand {\beamer@framepages {2}{2}}
+\headcommand {\slideentry {0}{0}{3}{3/3}{}{0}}
+\headcommand {\beamer@framepages {3}{3}}
+\headcommand {\slideentry {0}{0}{4}{4/4}{}{0}}
+\headcommand {\beamer@framepages {4}{4}}
+\headcommand {\slideentry {0}{0}{5}{5/5}{}{0}}
+\headcommand {\beamer@framepages {5}{5}}
+\headcommand {\slideentry {0}{0}{6}{6/6}{}{0}}
+\headcommand {\beamer@framepages {6}{6}}
+\headcommand {\slideentry {0}{0}{7}{7/7}{}{0}}
+\headcommand {\beamer@framepages {7}{7}}
+\headcommand {\beamer@partpages {1}{7}}
+\headcommand {\beamer@subsectionpages {1}{7}}
+\headcommand {\beamer@sectionpages {1}{7}}
+\headcommand {\beamer@documentpages {7}}
+\headcommand {\gdef \inserttotalframenumber {7}}

out/main.snm

@@ -0,0 +1,16 @@
+\beamer@slide {Task<1>}{2}
+\beamer@slide {Task}{2}
+\beamer@slide {fig:prep01Tasktablejpg}{2}
+\beamer@slide {Intro<1>}{3}
+\beamer@slide {Intro}{3}
+\beamer@slide {RW<1>}{4}
+\beamer@slide {RW}{4}
+\beamer@slide {fig:prep04RWgraphpng}{4}
+\beamer@slide {RW<1>}{5}
+\beamer@slide {RW}{5}
+\beamer@slide {fig:prep05RWgraphpng}{5}
+\beamer@slide {But...<1>}{6}
+\beamer@slide {But...}{6}
+\beamer@slide {fig:prep06Butgraphpng}{6}
+\beamer@slide {Problem Statement<1>}{7}
+\beamer@slide {Problem Statement}{7}

out/main.tex

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+\UseRawInputEncoding
+%\documentclass[hyperref={pdfpagelabels=false}]{beamer}
+\documentclass[hyperref={pdfpagelabels=false},aspectratio=169]{beamer}
+% Die Hyperref Option hyperref={pdfpagelabels=false} verhindert die Warnung:
+% Package hyperref Warning: Option `pdfpagelabels' is turned off
+% (hyperref)                because \thepage is undefined. 
+% Hyperref stopped early 
+%
+
+\usepackage{lmodern}
+% Das Paket lmodern erspart die folgenden Warnungen:
+% LaTeX Font Warning: Font shape `OT1/cmss/m/n' in size <4> not available
+% (Font)              size <5> substituted on input line 22.
+% LaTeX Font Warning: Size substitutions with differences
+% (Font)              up to 1.0pt have occurred.
+%
+
+% Wenn \titel{\ldots} \author{\ldots} erst nach \begin{document} kommen,
+% kommt folgende Warnung:
+% Package hyperref Warning: Option `pdfauthor' has already been used,
+% (hyperref) ... 
+% Daher steht es hier vor \begin{document}
+
+\title[u-Opt]{Unsupervised Optimisation - Paper Structure}   
+\author{Simon Kluettermann} 
+\date{\today} 
+
+
+ \institute{ls9 tu Dortmund}
+
+
+% Dadurch wird verhindert, dass die Navigationsleiste angezeigt wird.
+\setbeamertemplate{navigation symbols}{}
+
+% zusaetzlich ist das usepackage{beamerthemeshadow} eingebunden 
+\usepackage{beamerthemeshadow}
+
+\hypersetup{pdfstartview={Fit}} % fits the presentation to the window when first displayed
+
+\usepackage{appendixnumberbeamer}
+\usepackage{listings}
+
+
+\usetheme{CambridgeUS}
+\usepackage{ngerman}
+\usecolortheme{dolphin}
+
+
+%  \beamersetuncovermixins{\opaqueness<1>{25}}{\opaqueness<2$\Rightarrow${15}}
+%  sorgt dafuer das die Elemente die erst noch (zukuenftig) kommen 
+%  nur schwach angedeutet erscheinen 
+%\beamersetuncovermixins{\opaqueness<1>{25}}{\opaqueness<2$\Rightarrow${15}}%here disabled
+% klappt auch bei Tabellen, wenn teTeX verwendet wird\ldots
+\renewcommand{\figurename}{}
+
+\setbeamertemplate{footline}
+{
+  \leavevmode%
+  \hbox{%
+  \begin{beamercolorbox}[wd=.4\paperwidth,ht=2.25ex,dp=1ex,center]{author in head/foot}%
+    \usebeamerfont{author in head/foot}\insertshorttitle
+  \end{beamercolorbox}%
+  \begin{beamercolorbox}[wd=.25\paperwidth,ht=2.25ex,dp=1ex,center]{title in head/foot}%
+    \usebeamerfont{title in head/foot}\insertsection
+  \end{beamercolorbox}%
+  \begin{beamercolorbox}[wd=.3499\paperwidth,ht=2.25ex,dp=1ex,right]{date in head/foot}%
+    \usebeamerfont{date in head/foot}\insertshortdate{}\hspace*{2em}
+    \hyperlink{toc}{\insertframenumber{} / \inserttotalframenumber\hspace*{2ex}} 
+  \end{beamercolorbox}}%
+  \vskip0pt%
+}
+
+\usepackage[absolute,overlay]{textpos}
+\usepackage{graphicx}
+
+\newcommand{\source}[1]{\begin{textblock*}{9cm}(0.1cm,8.9cm)
+    \begin{beamercolorbox}[ht=0.5cm,left]{framesource}
+        \usebeamerfont{framesource}\usebeamercolor[fg!66]{framesource} Source: {#1}
+    \end{beamercolorbox}
+\end{textblock*}}
+
+
+\begin{document}
+
+
+
+%from file ../uopt/data/000.txt
+\begin{frame}[label=]
+\frametitle{}
+\begin{titlepage}
+
+	\centering
+	{\huge\bfseries \par}
+	\vspace{2cm}
+	{\LARGE\itshape Simon Kluettermann\par}
+	\vspace{1.5cm}
+	{\scshape\Large Master Thesis in Physics\par}
+	\vspace{0.2cm}
+	{\Large submitted to the \par}
+	\vspace{0.2cm}
+	{\scshape\Large Faculty of Mathematics Computer Science and Natural Sciences \par}
+	\vspace{0.2cm}
+	{\Large \par}
+	\vspace{0.2cm}
+	{\scshape\Large RWTH Aachen University}
+	\vspace{1cm}
+	
+	\vfill
+	{\scshape\Large Department of Physics\par}
+	\vspace{0.2cm}
+	{\scshape\Large Insitute for theoretical Particle Physics and Cosmology\par}
+	\vspace{0.2cm}
+	{ \Large\par}
+	\vspace{0.2cm}
+	{\Large First Referee: Prof. Dr. Michael Kraemer \par}
+	{\Large Second Referee: Prof. Dr. Felix Kahlhoefer}
+
+	\vfill
+
+% Bottom of the page
+	{\large November 2020 \par}
+\end{titlepage}
+\pagenumbering{roman}
+\thispagestyle{empty}
+\null
+\newpage
+\setcounter{page}{1}
+\pagenumbering{arabic}
+\end{frame}
+
+
+%from file ../uopt/data/001Task.txt
+\begin{frame}[label=Task]
+\frametitle{Task}
+\begin{figure}[H] 
+  \centering
+\includegraphics[height=0.9\textheight]{../prep/01Task/table.jpg}
+\label{fig:prep01Tasktablejpg}
+  \end{figure}
+
+
+\end{frame}
+
+
+%from file ../uopt/data/002Intro.txt
+\begin{frame}[label=Intro]
+\frametitle{Intro}
+\begin{itemize}
+
+    \item Motivation
+
+\begin{itemize}
+
+    \item AD is super important...
+
+    \item good AD = complicated models $\Rightarrow$ Many Parameters
+
+    \item Evaluation dependent on very few datapoints$\Rightarrow$Optimization impossible
+
+
+\end{itemize}
+    \item Open Challenges
+
+\begin{itemize}
+
+    \item Evaluate without testing data
+
+    \item Formalisation of existing ideas
+
+    \item Numerical assessment of them
+
+
+\end{itemize}
+    \item Contribution
+
+\begin{itemize}
+
+    \item Suggest new methods for AE
+
+    \item Compare methods on many datasets
+
+    \item Seperate into parameter and hyperparameter optimisation
+
+
+\end{itemize}
+
+\end{itemize}
+\end{frame}
+
+
+%from file ../uopt/data/003RW.txt
+\begin{frame}[label=RW]
+\frametitle{RW}
+\begin{figure}[H] 
+  \centering
+\includegraphics[height=0.9\textheight]{../prep/04RW/graph.png}
+\label{fig:prep04RWgraphpng}
+  \end{figure}
+
+
+\end{frame}
+
+
+%from file ../uopt/data/004RW.txt
+\begin{frame}[label=RW]
+\frametitle{RW}
+\begin{figure}[H] 
+  \centering
+\includegraphics[height=0.9\textheight]{../prep/05RW/graph.png}
+\label{fig:prep05RWgraphpng}
+  \end{figure}
+
+
+\end{frame}
+
+
+%from file ../uopt/data/005But....txt
+\begin{frame}[label=But...]
+\frametitle{But...}
+\begin{figure}[H] 
+  \centering
+\includegraphics[height=0.9\textheight]{../prep/06But.../graph.png}
+\label{fig:prep06Butgraphpng}
+  \end{figure}
+
+
+\end{frame}
+
+
+%from file ../uopt/data/006Problem Statement.txt
+\begin{frame}[label=Problem Statement]
+\frametitle{Problem Statement}
+\begin{itemize}
+
+    \item Given $N$ Anomaly detection methods $M_i = TrainModel(X_{train})$, find $f(M_i)$ so that Score $S_i = f(M_i)$ can be used to find an above average AD method $M_{argmax(S)}$.
+
+    \item Let $TrainMany(X_{train},C)=TrainModel(X_{train})_{argmax(f(M_0...M_C))}$. We assume the distribution of $TrainMany$ to be gaussian and describe it through $\mu_C$ and $\sigma_C$. We consider a function $f(M)$ to be helpful, if $\Delta = \frac{sqrt(N) \cdot (\mu_C-\mu_1)}{sqrt(\sigma_C^2+\sigma_1^2)} > 3$ for some number of models tested $N$.
+
+
+\end{itemize}
+\end{frame}
+
+
+
+\end{document}

prep/000/q

@@ -0,0 +1 @@
+<titlepage>

prep/02Intro/q

@@ -0,0 +1,18 @@
+Motivation
+<l2st>
+AD is super important...
+good AD = complicated models -> Many Parameters
+Evaluation dependent on very few datapoints->Optimization impossible
+</l2st>
+Open Challenges
+<l2st>
+Evaluate without testing data
+Formalisation of existing ideas
+Numerical assessment of them
+</l2st>
+Contribution
+<l2st>
+Suggest new methods for AE
+Compare methods on many datasets
+Seperate into parameter and hyperparameter optimisation
+</l2st>

prep/07Problem_Statement/q

@@ -0,0 +1,5 @@
+Given $N$ Anomaly detection methods $M_i = TrainModel(X_{train})$, find $f(M_i)$ so that Score $S_i = f(M_i)$ can be used to find an above average AD method $M_{argmax(S)}$.
+
+Let $TrainMany(X_{train},C)=TrainModel(X_{train})_{argmax(f(M_0...M_C))}$. We assume the distribution of $TrainMany$ to be gaussian and describe it through $\mu_C$ and $\sigma_C$. We consider a function $f(M)$ to be helpful, if $\Delta = \frac{sqrt(N) \cdot (\mu_C-\mu_1)}{sqrt(\sigma_C^2+\sigma_1^2)} > 3$ for some number of models tested $N$.
+
+