From b1bcd9cc999fb06b0cfafeb365e9749c841bfa97 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Simon=20Kl=C3=BCttermann?= Date: Mon, 27 Jun 2022 15:13:50 +0200 Subject: [PATCH] initial push --- data/000.txt | 5 + data/001Task.txt | 3 + data/002Intro.txt | 22 + data/003RW.txt | 3 + data/004RW.txt | 3 + data/005But....txt | 3 + data/006Problem Statement.txt | 6 + general.txt | 12 + imgs/eifor.png | Bin 0 -> 115990 bytes imgs/histone_page-blocks.png | Bin 0 -> 11984 bytes imgs/histone_pima.png | Bin 0 -> 13956 bytes imgs/ifor.png | Bin 0 -> 60540 bytes imgs/qual.png | Bin 0 -> 101588 bytes out/compile.bat | 3 + out/compile.sh | 3 + out/label.json | 38 + out/main.aux | 70 ++ out/main.log | 1127 +++++++++++++++++++ out/main.nav | 19 + out/main.out | 0 out/main.pdf | Bin 0 -> 589704 bytes out/main.snm | 16 + out/main.tex | 245 ++++ out/main.toc | 0 prep/000/nonl | 0 prep/000/q | 1 + prep/01Task/hmode | 0 prep/01Task/table.jpg | Bin 0 -> 235984 bytes prep/02Intro/q | 18 + prep/04RW/graph.png | Bin 0 -> 96741 bytes prep/04RW/hmode | 0 prep/05RW/graph.png | Bin 0 -> 28759 bytes prep/05RW/hmode | 0 prep/06But.../graph.png | Bin 0 -> 80911 bytes prep/06But.../hmode | 0 prep/07Problem_Statement/q | 5 + touse/Ethereum-Price-Prediction.webp | Bin 0 -> 20410 bytes touse/TIM220328_Buterin.Cover_.FINAL2_.jpg | Bin 0 -> 1012611 bytes touse/TIM220328_Buterin.Cover_.FINAL2_.webp | Bin 0 -> 271456 bytes 39 files changed, 1602 insertions(+) create mode 100644 data/000.txt create mode 100644 data/001Task.txt create mode 100644 data/002Intro.txt create mode 100644 data/003RW.txt create mode 100644 data/004RW.txt create mode 100644 data/005But....txt create mode 100644 data/006Problem Statement.txt create mode 100644 general.txt create mode 100644 imgs/eifor.png create mode 100644 imgs/histone_page-blocks.png create mode 100644 imgs/histone_pima.png create mode 100644 imgs/ifor.png create mode 100644 imgs/qual.png create mode 100644 out/compile.bat create mode 100755 out/compile.sh create mode 100644 out/label.json create mode 100644 out/main.aux create mode 100644 out/main.log create mode 100644 out/main.nav create mode 100644 out/main.out create mode 100644 out/main.pdf create mode 100644 out/main.snm create mode 100644 out/main.tex create mode 100644 out/main.toc create mode 100644 prep/000/nonl create mode 100644 prep/000/q create mode 100644 prep/01Task/hmode create mode 100644 prep/01Task/table.jpg create mode 100644 prep/02Intro/q create mode 100644 prep/04RW/graph.png create mode 100644 prep/04RW/hmode create mode 100644 prep/05RW/graph.png create mode 100644 prep/05RW/hmode create mode 100644 prep/06But.../graph.png create mode 100644 prep/06But.../hmode create mode 100644 prep/07Problem_Statement/q create mode 100644 touse/Ethereum-Price-Prediction.webp create mode 100644 touse/TIM220328_Buterin.Cover_.FINAL2_.jpg create mode 100644 touse/TIM220328_Buterin.Cover_.FINAL2_.webp diff --git a/data/000.txt b/data/000.txt new file mode 100644 index 0000000..57c13ce --- /dev/null +++ b/data/000.txt @@ -0,0 +1,5 @@ + + + + + \ No newline at end of file diff --git a/data/001Task.txt b/data/001Task.txt new file mode 100644 index 0000000..75cc2d6 --- /dev/null +++ b/data/001Task.txt @@ -0,0 +1,3 @@ + + + \ No newline at end of file diff --git a/data/002Intro.txt b/data/002Intro.txt new file mode 100644 index 0000000..99285da --- /dev/null +++ b/data/002Intro.txt @@ -0,0 +1,22 @@ + + + Motivation + +AD is super important... +good AD = complicated models -> Many Parameters +Evaluation dependent on very few datapoints->Optimization impossible + +Open Challenges + +Evaluate without testing data +Formalisation of existing ideas +Numerical assessment of them + +Contribution + +Suggest new methods for AE +Compare methods on many datasets +Seperate into parameter and hyperparameter optimisation + + + \ No newline at end of file diff --git a/data/003RW.txt b/data/003RW.txt new file mode 100644 index 0000000..8cd1f12 --- /dev/null +++ b/data/003RW.txt @@ -0,0 +1,3 @@ + + + \ No newline at end of file diff --git a/data/004RW.txt b/data/004RW.txt new file mode 100644 index 0000000..442b697 --- /dev/null +++ b/data/004RW.txt @@ -0,0 +1,3 @@ + + + \ No newline at end of file diff --git a/data/005But....txt b/data/005But....txt new file mode 100644 index 0000000..4fb27ff --- /dev/null +++ b/data/005But....txt @@ -0,0 +1,3 @@ + + + \ No newline at end of file diff --git a/data/006Problem Statement.txt b/data/006Problem Statement.txt new file mode 100644 index 0000000..be4d4f5 --- /dev/null +++ b/data/006Problem Statement.txt @@ -0,0 +1,6 @@ + + + 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 $\sigma_C$. 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100644 index 0000000..e72fefd --- /dev/null +++ b/out/main.tex @@ -0,0 +1,245 @@ +\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} diff --git a/out/main.toc b/out/main.toc new file mode 100644 index 0000000..e69de29 diff --git a/prep/000/nonl b/prep/000/nonl new file mode 100644 index 0000000..e69de29 diff --git a/prep/000/q b/prep/000/q new file mode 100644 index 0000000..97c72ec --- /dev/null +++ b/prep/000/q @@ -0,0 +1 @@ +<titlepage> diff --git a/prep/01Task/hmode b/prep/01Task/hmode new file mode 100644 index 0000000..e69de29 diff --git a/prep/01Task/table.jpg b/prep/01Task/table.jpg new file mode 100644 index 0000000000000000000000000000000000000000..3abcd9b5f091488176f60f77e95b1391b26fcad1 GIT binary patch literal 235984 zcmcG$3p|wR`#=8ZxE*X;O9zzQsKl1-fK5f4MJfszIptF#Mq^42DNK7vrDRnINg<~( zY&C}%ncWnHsl<$JrZJ1ynsP2f!}wqKGc!`#em~#;@AdoD>oJcp^E~(Ay5HCPdSBQ5 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+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> diff --git a/prep/04RW/graph.png b/prep/04RW/graph.png new file mode 100644 index 0000000000000000000000000000000000000000..01e64b55ad0e091e4d4c73d095feda2dc958efdf GIT binary patch literal 96741 zcma&O2RPR4|2{4$x0SsmGdm+>?-gZbWkpkDhU~pJ+1V=GX4x4bA|sTQ6(UmE8KwHY z-fo`n^Z9(g|Ks={M;*^`JaXUf>$+a!JkQs8y~DLNRf+KF@UgJ4h%Txr>0)6Wk3s&T zPQw4u+PRbn|2cMFS5*P4>@C9=EG#Chi%Rl(Zsy;bPB`n`*=cU_pVPd{rQImUwPHM3 zO0hF%*HNuKIThL9>Ce(yT1T-%<rzt>zHUF&zv{cu$M*2%eD8e5`5p7GX3hzlV?Qo! zyv?*FmGRl%)v;oe!~X9-B-s8aiWo2ZI4r#X`V*w$*A~A@OvZHVKmYh^<7ta+5Gnkx zkCL11+WB9f66f~|XUWky`?PApfBnqw8cB}BR;0~m=8JwfYXAPz;*Cuavr_p<?LWWx z7}**D{J_VtPm1loAGS`*uWiuVmEq!lKb4O>{D8kKEtKuQmT_zcmhqxLhV_4aJ3mTW z>}Q9_X54@8)r`zBeAS%~j{oc1cTd0%Ue?`aO!&`*_~A@)$jvUvNXGoHZ-2=IKcF7% 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We assume the distribution of $TrainMany$ to be gaussian and describe it through $\mu_C$ and $\sigma_C$. 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