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\title[u-Opt]{Unsupervised Optimisation - Paper Structure}   
\author{Simon Kluettermann} 
\date{\today} 


 \institute{ls9 tu Dortmund}


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\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}
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\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}


%from file ../uopt/data/007Impossible.txt
\begin{frame}[label=Impossible]
\frametitle{Impossible}
\begin{figure}[H] 
  \centering
\includegraphics[height=0.9\textheight]{../prep/08Impossible/impos.pdf}
\label{fig:prep08Impossibleimpospdf}
  \end{figure}


\end{frame}


%from file ../uopt/data/008Blob.txt
\begin{frame}[label=Blob]
\frametitle{Blob}
\begin{figure}[H] 
  \centering
\includegraphics[height=0.9\textheight]{../prep/09Blob/blob_cardio.png}
\label{fig:prep09Blobblob_cardiopng}
  \end{figure}


\end{frame}


%from file ../uopt/data/009Blob.txt
\begin{frame}[label=Blob]
\frametitle{Blob}
\begin{figure}[H] 
  \centering
\includegraphics[height=0.9\textheight]{../prep/10Blob/blob_page-blocks.png}
\label{fig:prep10Blobblob_page-blockspng}
  \end{figure}


\end{frame}


%from file ../uopt/data/010One Dataset.txt
\begin{frame}[label=One Dataset]
\frametitle{One Dataset}
\begin{figure}[H] 
  \centering
\includegraphics[height=0.9\textheight]{../prep/12One Dataset/histone_page-blocks.pdf}
\label{fig:prep12One Datasethistone_page-blockspdf}
  \end{figure}


\end{frame}


%from file ../uopt/data/011Many Datasets.txt
\begin{frame}[label=Many Datasets]
\frametitle{Many Datasets}
\begin{figure}[H] 
  \centering
\includegraphics[height=0.9\textheight]{../prep/13Many Datasets/z_robu.pdf}
\label{fig:prep13Many Datasetsz_robupdf}
  \end{figure}


\end{frame}


%from file ../uopt/data/012Afterwards.txt
\begin{frame}[label=Afterwards]
\frametitle{Afterwards}
\begin{itemize}

    \item Afterwards:

    \item Table: Fraction of positive impro, Average impro

    \item Correlation between optimizers

    \item Hyperparam: Same table

    \item Improvement by hyperparameter (latent dim is different from batch size)


\end{itemize}
\end{frame}



\end{document}
