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Commit 6ead032b authored by Simon Klüttermann's avatar Simon Klüttermann
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...@@ -58,28 +58,40 @@ ...@@ -58,28 +58,40 @@
\BOOKMARK [3][-]{subsubsection.4.3.3}{Using this normation}{subsection.4.3}% 58 \BOOKMARK [3][-]{subsubsection.4.3.3}{Using this normation}{subsection.4.3}% 58
\BOOKMARK [3][-]{subsubsection.4.3.4}{Improving the Normation even further}{subsection.4.3}% 59 \BOOKMARK [3][-]{subsubsection.4.3.4}{Improving the Normation even further}{subsection.4.3}% 59
\BOOKMARK [2][-]{subsection.4.4}{Auc Scores for Toptagging}{section.4}% 60 \BOOKMARK [2][-]{subsection.4.4}{Auc Scores for Toptagging}{section.4}% 60
\BOOKMARK [1][-]{section.5}{Data}{}% 61 \BOOKMARK [1][-]{section.5}{Other Approaches}{}% 61
\BOOKMARK [2][-]{subsection.5.1}{Ligth Dark Matter}{section.5}% 62 \BOOKMARK [2][-]{subsection.5.1}{Oneoff Networks}{section.5}% 62
\BOOKMARK [2][-]{subsection.5.2}{Quark v Gluon}{section.5}% 63 \BOOKMARK [3][-]{subsubsection.5.1.1}{oneoff math}{subsection.5.1}% 63
\BOOKMARK [1][-]{section.6}{Other Approaches}{}% 64 \BOOKMARK [3][-]{subsubsection.5.1.2}{oneoff quality}{subsection.5.1}% 64
\BOOKMARK [2][-]{subsection.6.1}{Oneoff Networks}{section.6}% 65 \BOOKMARK [3][-]{subsubsection.5.1.3}{oneoff outside of physics}{subsection.5.1}% 65
\BOOKMARK [3][-]{subsubsection.6.1.1}{oneoff math}{subsection.6.1}% 66 \BOOKMARK [2][-]{subsection.5.2}{Other Algorithms}{section.5}% 66
\BOOKMARK [3][-]{subsubsection.6.1.2}{oneoff quality}{subsection.6.1}% 67 \BOOKMARK [3][-]{subsubsection.5.2.1}{Support Vector Machines}{subsection.5.2}% 67
\BOOKMARK [3][-]{subsubsection.6.1.3}{oneoff outside of physics}{subsection.6.1}% 68 \BOOKMARK [3][-]{subsubsection.5.2.2}{k neirest neighbours}{subsection.5.2}% 68
\BOOKMARK [2][-]{subsection.6.2}{Other Algorithms}{section.6}% 69 \BOOKMARK [3][-]{subsubsection.5.2.3}{Isolation Forests}{subsection.5.2}% 69
\BOOKMARK [3][-]{subsubsection.6.2.1}{Support Vector Machines}{subsection.6.2}% 70 \BOOKMARK [1][-]{section.6}{Mixed Approaches}{}% 70
\BOOKMARK [3][-]{subsubsection.6.2.2}{k neirest neighbours}{subsection.6.2}% 71 \BOOKMARK [2][-]{subsection.6.1}{Compressed One Class Learning}{section.6}% 71
\BOOKMARK [3][-]{subsubsection.6.2.3}{Isolation Forests}{subsection.6.2}% 72 \BOOKMARK [2][-]{subsection.6.2}{}{section.6}% 72
\BOOKMARK [1][-]{section.7}{Mixed Approaches}{}% 73 \BOOKMARK [3][-]{subsubsection.6.2.1}{SVM}{subsection.6.2}% 73
\BOOKMARK [2][-]{subsection.7.1}{Compressed One Class Learning}{section.7}% 74 \BOOKMARK [3][-]{subsubsection.6.2.2}{Isolation Forest}{subsection.6.2}% 74
\BOOKMARK [2][-]{subsection.7.2}{}{section.7}% 75 \BOOKMARK [3][-]{subsubsection.6.2.3}{k neirest neighbour}{subsection.6.2}% 75
\BOOKMARK [3][-]{subsubsection.7.2.1}{SVM}{subsection.7.2}% 76 \BOOKMARK [3][-]{subsubsection.6.2.4}{oneoff}{subsection.6.2}% 76
\BOOKMARK [3][-]{subsubsection.7.2.2}{Isolation Forest}{subsection.7.2}% 77 \BOOKMARK [2][-]{subsection.6.3}{???}{section.6}% 77
\BOOKMARK [3][-]{subsubsection.7.2.3}{k neirest neighbour}{subsection.7.2}% 78 \BOOKMARK [2][-]{subsection.6.4}{Why Autoencoder might not be so great}{section.6}% 78
\BOOKMARK [3][-]{subsubsection.7.2.4}{oneoff}{subsection.7.2}% 79 \BOOKMARK [3][-]{subsubsection.6.4.1}{Reproduding vs Classifing Quality}{subsection.6.4}% 79
\BOOKMARK [2][-]{subsection.7.3}{???}{section.7}% 80 \BOOKMARK [3][-]{subsubsection.6.4.2}{General Problems}{subsection.6.4}% 80
\BOOKMARK [2][-]{subsection.7.4}{Why Autoencoder might not be so great}{section.7}% 81 \BOOKMARK [3][-]{subsubsection.6.4.3}{Autoencoder and losses}{subsection.6.4}% 81
\BOOKMARK [3][-]{subsubsection.7.4.1}{Reproduding vs Classifing Quality}{subsection.7.4}% 82 \BOOKMARK [1][-]{section.7}{Data}{}% 82
\BOOKMARK [3][-]{subsubsection.7.4.2}{General Problems}{subsection.7.4}% 83 \BOOKMARK [2][-]{subsection.7.1}{Ligth Dark Matter}{section.7}% 83
\BOOKMARK [3][-]{subsubsection.7.4.3}{Autoencoder and losses}{subsection.7.4}% 84 \BOOKMARK [2][-]{subsection.7.2}{Quark v Gluon}{section.7}% 84
\BOOKMARK [1][-]{section.8}{Appendix}{}% 85 \BOOKMARK [1][-]{section.8}{Other Usecases}{}% 85
\BOOKMARK [2][-]{subsection.8.1}{Fraud Detection for Social Networks}{section.8}% 86
\BOOKMARK [3][-]{subsubsection.8.1.1}{Datageneration}{subsection.8.1}% 87
\BOOKMARK [3][-]{subsubsection.8.1.2}{Training}{subsection.8.1}% 88
\BOOKMARK [3][-]{subsubsection.8.1.3}{Whats Next}{subsection.8.1}% 89
\BOOKMARK [2][-]{subsection.8.2}{Accelarating molecular science through pooling}{section.8}% 90
\BOOKMARK [3][-]{subsubsection.8.2.1}{Datageneration}{subsection.8.2}% 91
\BOOKMARK [3][-]{subsubsection.8.2.2}{Training}{subsection.8.2}% 92
\BOOKMARK [3][-]{subsubsection.8.2.3}{Whats next?}{subsection.8.2}% 93
\BOOKMARK [2][-]{subsection.8.3}{High level Machine Learning and Feynman Diagramms, or how I learned to stop thinking and love the graphs}{section.8}% 94
\BOOKMARK [3][-]{subsubsection.8.3.1}{Data generation}{subsection.8.3}% 95
\BOOKMARK [3][-]{subsubsection.8.3.2}{Training}{subsection.8.3}% 96
\BOOKMARK [1][-]{section.9}{Appendix}{}% 97
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