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This commit is contained in:
Simon Klüttermann
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<frame >
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<titlepage>
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<frame title="Task">
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<i f="../prep/01Task/table.jpg"></i>
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<frame title="Intro">
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<list>
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<e>Motivation</e>
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<l2st>
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<e>AD is super important...</e>
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<e>good AD = complicated models -> Many Parameters</e>
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<e>Evaluation dependent on very few datapoints->Optimization impossible</e>
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</l2st>
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<e>Open Challenges</e>
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<l2st>
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<e>Evaluate without testing data</e>
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<e>Formalisation of existing ideas</e>
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<e>Numerical assessment of them</e>
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</l2st>
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<e>Contribution</e>
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<l2st>
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<e>Suggest new methods for AE</e>
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<e>Compare methods on many datasets</e>
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<e>Seperate into parameter and hyperparameter optimisation</e>
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</l2st>
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</list>
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</frame>
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<frame title="RW">
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<i f="../prep/04RW/graph.png"></i>
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<frame title="Problem Statement">
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<list>
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<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>
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<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>
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<plt>
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<name Current experiment status>
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<title Unsupervised Optimisation - Paper Structure>
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<stitle u-Opt>
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<institute ls9 tu Dortmund>
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<theme CambridgeUS>
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pdflatex main.tex
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pdflatex main.tex
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\title[u-Opt]{Unsupervised Optimisation - Paper Structure}
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\author{Simon Kluettermann}
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\institute{ls9 tu Dortmund}
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{\huge\bfseries \par}
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{\LARGE\itshape Simon Kluettermann\par}
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{\scshape\Large Master Thesis in Physics\par}
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{\Large \par}
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{\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}
|
||||||
|
|
@ -0,0 +1 @@
|
||||||
|
<titlepage>
|
||||||
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|
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|
|
@ -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>
|
||||||
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|
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|
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|
|
@ -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 $N$.
|
||||||
|
|
||||||
|
|
||||||
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Loading…
Reference in New Issue