initial push
This commit is contained in:
Simon Klüttermann
commit
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<frame >
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<titlepage>
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</frame>
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<frame title="Thesis@ls9">
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<list>
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<e>First: Find a topic and a supervisor</e>
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<e>Work one month on this, to make sure</e>
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<l2st>
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<e>you still like your topic</e>
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<e>and you are sure you can handle the topic</e>
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</l2st>
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<e>then short presentation in front of our chair (15min, relaxed)</e>
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<l2st>
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<e>get some feedback/suggestions</e>
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</l2st>
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<e>afterwards register the thesis</e>
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<l2st>
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<e>(different for CS/DS students)</e>
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</l2st>
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<e>Problem: We are not able to supervise more than 2 students at the same time (CS faculty rules)</e>
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</list>
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</frame>
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<frame title="Today">
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<list>
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<e>First: A short summary of each Topic</e>
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<e>Then time for questions/Talk with your supervisor about each topic that sounds interesting</e>
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<e>Your own topics are always welcome;)</e>
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</list>
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</frame>
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<frame title="Anomaly Detection">
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<split>
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<que>
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<list>
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<e>Im working on Anomaly Detection</e>
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<e>That means characterising an often very complex distributions, to find events that dont match the expected distribution</e>
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</list>
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</que>
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<que>
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<i f="../prep/03Anomaly_Detection/circle.pdf" wmode="True"></i>
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</que>
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</split>
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</frame>
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<frame title="knn">
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<list>
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<e>kNN algorithm can also be used for AD</e>
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<e>if the k closest point is further away, a sample is considered more anomalous</e>
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<e>$r=\frac{k}{2N\cdot pdf}$</e>
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<e>Powerful method, as it can model the pdf directly</e>
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</list>
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</frame>
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<frame title="Better knn">
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<list>
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<e>The model (mostly) ignores every known sample except one</e>
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<e>So there are extensions</e>
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<e>$avg=\frac{1}{N} \sum_i knn_i(x)$</e>
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<e>$wavg=\frac{1}{N} \sum_i \frac{knn_i(x)}{i}$</e>
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</list>
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</frame>
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<frame title="Comparison">
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<list>
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<e>nothing</e>
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</list>
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</frame>
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<frame title="What to do?">
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<list>
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<e>Evaluation as anomaly detector is complicated</e>
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<l2st>
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<e>Requires known anomalies</e>
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</l2st>
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<e>->So evaluate as density estimator</e>
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<l2st>
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<e>Does not require anomalies</e>
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<e>Allows generating infinite amounts of training data</e>
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</l2st>
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</list>
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</frame>
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<frame title="What to do?">
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<list>
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<e>Collect Extensions of the oc-knn algorithm</e>
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<e>Define some distance measure to a known pdf</e>
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<e>Generate random datapoints following the pdf</e>
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<e>Evaluate which algorithm finds the pdf the best</e>
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</list>
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</frame>
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<frame title="Requirements">
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<list>
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<e>Knowledge of python ( sum([i for i in range(5) if i\%2]) )</e>
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<l2st>
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<e>Ideally incl numpy</e>
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</l2st>
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<e>Basic university level Math (you could argue that $r_k \propto \frac{k}{pdf}$)</e>
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<e>Ideally some experience working on a ssh server</e>
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<e>->Good as a Bachelor Thesis</e>
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<e>For a Master Thesis, I would extend this a bit (Could you also find $k$?)</e>
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</list>
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</frame>
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<frame title="Normalising Flows">
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<list>
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<e>Deep Learning Method, in which the output is normalised</e>
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<e>$\int f(x) dx=1 \; \forall f(x)$</e>
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<e>Can be used to estimate probability density functions</e>
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<e>->Thus useful for AD</e>
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<e>$\int f(h(x)) \|\frac{\delta h}{\delta x}\| dx=1 \; \forall h(x)$</e>
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</list>
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</frame>
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<frame title="Graph Normalising Flows">
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<list>
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<e>How to apply this to graphs?</e>
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<e>One Paper (Liu 2019) uses two NN:</e>
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<e>Autoencoder graph->vector</e>
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<e>NF on vector data</e>
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<e>which is fine, but also not really graph specific</e>
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<e>No interaction between encoding and transformation</e>
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</list>
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</frame>
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<frame title="Graph Normalising Flows">
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<list>
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||||||
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<e>So why not do this directly?</e>
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<e>->Requires differentiating a graph</e>
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<e>Why not use only one Network?</e>
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<e>Graph->Vector->pdf</e>
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||||||
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<e>->Finds trivial solution, as $<pdf> \propto \frac{1}{\sigma_{Vector}}$</e>
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<e>So regularise the standart deviation of the vector space!</e>
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<l2st>
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<e>Interplay between encoding and NF</e>
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||||||
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<e>Could also be useful for highdim data</e>
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||||||
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</l2st>
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||||||
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</list>
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||||||
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</frame>
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||||||
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||||||
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<frame title="Requirements">
|
||||||
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<list>
|
||||||
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<e>Proficient in python ( [i for i in range(1,N) if not [j for j in range(2,i) if not i\%j]] )</e>
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||||||
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<l2st>
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||||||
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<e>Ideally incl numpy, tensorflow, keras</e>
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||||||
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</l2st>
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||||||
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<e>Some deep learning experience</e>
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||||||
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<e>University level math (google Cholesky Decomposition. Why is this useful for NF?)</e>
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||||||
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<e>Ideally some experience working on a ssh server</e>
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<e>A bit more challenging->Better as a Master thesis</e>
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||||||
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<e>(Still we would start very slowly of course)</e>
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||||||
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</list>
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||||||
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</frame>
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<frame title="Old Thesis Sina">
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<split>
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<que>
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<list>
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||||||
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<e>Isolation Forest: Different Anomaly Detection Algorithm</e>
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<e>Problem: Isolation Forests dont work on categorical data</e>
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<e>->Extend them to categorical data</e>
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</list>
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||||||
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</que>
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||||||
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<que>
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||||||
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<i f="../prep/20Old_Thesis_Sina/Bildschirmfoto vom 2022-09-26 16-22-30.png" wmode="True"></i>
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</que>
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</split>
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</frame>
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<frame title="Old Thesis Britta">
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<split>
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<que>
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<list>
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<e>Reidentification: Find known objects in new images</e>
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<e>Task: Find if two images of pallet blocks are of the same pallet block</e>
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<e>Use AD to represent the pallet blocks</e>
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</list>
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||||||
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</que>
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<que>
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||||||
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<i f="../prep/21Old_Thesis_Britta/Bildschirmfoto vom 2022-09-26 16-23-26.png" wmode="True"></i>
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</que>
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</split>
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</frame>
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<frame title="Old Thesis Hsin Ping">
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||||||
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<split>
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||||||
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<que>
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||||||
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<list>
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<e>Ensemble: Combination of multiple models</e>
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<e>Task: Explain the prediction of a model using the ensemble structure</e>
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||||||
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</list>
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||||||
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</que>
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||||||
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<que>
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||||||
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<i f="../prep/22Old_Thesis_Hsin_Ping/Bildschirmfoto vom 2022-09-26 16-24-14.png" wmode="True"></i>
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||||||
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</que>
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||||||
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</split>
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</frame>
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<frame title="Old Thesis Nikitha">
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<split>
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<que>
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||||||
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<list>
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||||||
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<e>Task: Explore a new kind of ensemble</e>
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||||||
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<e>Instead of many uncorrelated models, let the models interact during training</e>
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||||||
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</list>
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||||||
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</que>
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||||||
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<que>
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||||||
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<i f="../prep/23Old_Thesis_Nikitha/Bildschirmfoto vom 2022-09-26 16-25-06.png" wmode="True"></i>
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||||||
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</que>
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||||||
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</split>
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||||||
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</frame>
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<plt>
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<name Current experiment status>
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<title Open Thesis Topics>
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<stitle Thesis Simon>
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<institute ls9 tu Dortmund>
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<theme CambridgeUS>
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<colo dolphin>
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</plt>
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Two distributions
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<l2st>
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One known (=normal)
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One unknown (=anomalies)
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</l2st>
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Seperate them
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Two distributions
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<l2st>
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One known (=normal)
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||||||
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One unknown (=anomalies)
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||||||
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</l2st>
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Seperate them
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Problem: few anomalies
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Anomalies are rare, so often only a few datapoints known (e.g. Machine Failure in an Aircraft)
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||||||
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In practice, anomalies might appear that are not known during testing
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->So train the model only on normal samples
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||||||
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Unsupervised Machine Learning
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||||||
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<l2st>
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||||||
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What can we say without knowing anomalies?
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||||||
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''Understand you dataset''
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||||||
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</l2st>
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Anomalies are rare, so often only a few datapoints known (e.g. Machine Failure in an Aircraft)
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||||||
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In practice, anomalies might appear that are not known during testing
|
||||||
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->So train the model only on normal samples
|
||||||
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Unsupervised Machine Learning
|
||||||
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<l2st>
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||||||
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What can we say without knowing anomalies?
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||||||
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''Understand you dataset''
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</l2st>
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Seems easy? Now do this
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<l2st>
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in thousands of dimensions
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with complicated distributions
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and overlap between anomalies and normal points
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</l2st>
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Most machine learning requires Hyperparameter Optimisation
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(Find model parameters that result in the best results)
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->AutoML: Do this automatically as fast as possible
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||||||
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||||||
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So lets combine both (Auto Anomaly Detection)
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||||||
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->Problem
|
||||||
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<l2st>
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||||||
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AutoMl requires Evaluation (loss, accuracy, AUC) to optimize
|
||||||
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AD can only be evaluated with regards to the anomalies
|
||||||
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->no longer unsupervised
|
||||||
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</l2st>
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||||||
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So most Anomaly Detection is ''unoptimized''
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So how to solve this?
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||||||
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One option: Think of some function to evaluate only the normal points
|
||||||
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->A bit hard to do in a case study
|
||||||
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||||||
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So how to solve this?
|
||||||
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One option: ''Just find the best solution directly''
|
||||||
|
->Zero Shot AutoML
|
||||||
|
Find best practices for hyperparameters
|
||||||
|
Requires optimisation for each model seperately -> matches the case study structure quite well!
|
||||||
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|
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|
||||||
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Basics of Scientific Computing
|
||||||
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Basics of AD
|
||||||
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Basics of AutoML
|
||||||
|
Build groups for each algorithm
|
||||||
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<l2st>
|
||||||
|
Choose a set of Hyperparameters
|
||||||
|
Find ''best practice`s'' for them
|
||||||
|
Maybe consider more complicated Transformations (Preprocessing, Ensemble)
|
||||||
|
</l2st>
|
||||||
|
Compare between groups (best algorithm for current situation)
|
||||||
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Evaluate on new datasets
|
||||||
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Write a report/Present your work
|
||||||
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@ -0,0 +1,7 @@
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||||||
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Requirements:
|
||||||
|
<l2st>
|
||||||
|
MD Req 1->MD Req 8
|
||||||
|
Basic Python/Math Knowledge
|
||||||
|
Motivation to learn something new;)
|
||||||
|
</l2st>
|
||||||
|
Registration till Saturday, by Email to Simon.Kluettermann@cs.tu-dortmund.de
|
||||||
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pdflatex main.tex
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pdflatex main.tex
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||||||
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pdflatex main.tex
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pdflatex main.tex
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||||||
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[
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||||||
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{
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"typ": "img",
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"files": [
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||||||
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"../prep/03Anomaly_Detection/circle.pdf"
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],
|
||||||
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"label": "prep03Anomaly_Detectioncirclepdf",
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||||||
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"caption": "",
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||||||
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"where": "../knn1//data/003Anomaly Detection.txt"
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},
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{
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"typ": "img",
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"files": [
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"../prep/20Old_Thesis_Sina/Bildschirmfoto vom 2022-09-26 16-22-30.png"
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],
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"label": "prep20Old_Thesis_SinaBildschirmfoto vom 2022-09-26 16-22-30png",
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"caption": "",
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||||||
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"where": "../knn1//data/014Old Thesis Sina.txt"
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},
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||||||
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{
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||||||
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"typ": "img",
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||||||
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"files": [
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"../prep/21Old_Thesis_Britta/Bildschirmfoto vom 2022-09-26 16-23-26.png"
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||||||
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],
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||||||
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"label": "prep21Old_Thesis_BrittaBildschirmfoto vom 2022-09-26 16-23-26png",
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||||||
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"caption": "",
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||||||
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"where": "../knn1//data/015Old Thesis Britta.txt"
|
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},
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||||||
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{
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"typ": "img",
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||||||
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"files": [
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"../prep/22Old_Thesis_Hsin_Ping/Bildschirmfoto vom 2022-09-26 16-24-14.png"
|
||||||
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],
|
||||||
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"label": "prep22Old_Thesis_Hsin_PingBildschirmfoto vom 2022-09-26 16-24-14png",
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||||||
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"caption": "",
|
||||||
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"where": "../knn1//data/016Old Thesis Hsin Ping.txt"
|
||||||
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},
|
||||||
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{
|
||||||
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"typ": "img",
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|
||||||
|
\title[Thesis Simon]{Open Thesis Topics}
|
||||||
|
\author{Simon.Kluettermann@cs.tu-dortmund.de}
|
||||||
|
\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 ../knn1//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 ../knn1//data/001Thesis@ls9.txt
|
||||||
|
\begin{frame}[label=Thesis@ls9]
|
||||||
|
\frametitle{Thesis@ls9}
|
||||||
|
\begin{itemize}
|
||||||
|
|
||||||
|
\item First: Find a topic and a supervisor
|
||||||
|
|
||||||
|
\item Work one month on this, to make sure
|
||||||
|
|
||||||
|
\begin{itemize}
|
||||||
|
|
||||||
|
\item you still like your topic
|
||||||
|
|
||||||
|
\item and you are sure you can handle the topic
|
||||||
|
|
||||||
|
|
||||||
|
\end{itemize}
|
||||||
|
\item then short presentation in front of our chair (15min, relaxed)
|
||||||
|
|
||||||
|
\begin{itemize}
|
||||||
|
|
||||||
|
\item get some feedback/suggestions
|
||||||
|
|
||||||
|
|
||||||
|
\end{itemize}
|
||||||
|
\item afterwards register the thesis
|
||||||
|
|
||||||
|
\begin{itemize}
|
||||||
|
|
||||||
|
\item (different for CS/DS students)
|
||||||
|
|
||||||
|
|
||||||
|
\end{itemize}
|
||||||
|
\item Problem: We are not able to supervise more than 2 students at the same time (CS faculty rules)
|
||||||
|
|
||||||
|
|
||||||
|
\end{itemize}
|
||||||
|
\end{frame}
|
||||||
|
|
||||||
|
|
||||||
|
%from file ../knn1//data/002Today.txt
|
||||||
|
\begin{frame}[label=Today]
|
||||||
|
\frametitle{Today}
|
||||||
|
\begin{itemize}
|
||||||
|
|
||||||
|
\item First: A short summary of each Topic
|
||||||
|
|
||||||
|
\item Then time for questions/Talk with your supervisor about each topic that sounds interesting
|
||||||
|
|
||||||
|
\item Your own topics are always welcome;)
|
||||||
|
|
||||||
|
|
||||||
|
\end{itemize}
|
||||||
|
\end{frame}
|
||||||
|
|
||||||
|
|
||||||
|
%from file ../knn1//data/003Anomaly Detection.txt
|
||||||
|
\begin{frame}[label=Anomaly Detection]
|
||||||
|
\frametitle{Anomaly Detection}
|
||||||
|
\begin{columns}[c] % align columns
|
||||||
|
\begin{column}{0.48\textwidth}%.48
|
||||||
|
\begin{itemize}
|
||||||
|
|
||||||
|
\item Im working on Anomaly Detection
|
||||||
|
|
||||||
|
\item That means characterising an often very complex distributions, to find events that dont match the expected distribution
|
||||||
|
|
||||||
|
|
||||||
|
\end{itemize}
|
||||||
|
\end{column}%
|
||||||
|
\hfill%
|
||||||
|
\begin{column}{0.48\textwidth}%.48
|
||||||
|
\begin{figure}[H]
|
||||||
|
\centering
|
||||||
|
\includegraphics[width=0.9\textwidth]{../prep/03Anomaly_Detection/circle.pdf}
|
||||||
|
\label{fig:prep03Anomaly_Detectioncirclepdf}
|
||||||
|
\end{figure}
|
||||||
|
|
||||||
|
|
||||||
|
\end{column}%
|
||||||
|
\hfill%
|
||||||
|
\end{columns}
|
||||||
|
|
||||||
|
\end{frame}
|
||||||
|
|
||||||
|
|
||||||
|
%from file ../knn1//data/004knn.txt
|
||||||
|
\begin{frame}[label=knn]
|
||||||
|
\frametitle{knn}
|
||||||
|
\begin{itemize}
|
||||||
|
|
||||||
|
\item kNN algorithm can also be used for AD
|
||||||
|
|
||||||
|
\item if the k closest point is further away, a sample is considered more anomalous
|
||||||
|
|
||||||
|
\item $r=\frac{k}{2N\cdot pdf}$
|
||||||
|
|
||||||
|
\item Powerful method, as it can model the pdf directly
|
||||||
|
|
||||||
|
|
||||||
|
\end{itemize}
|
||||||
|
\end{frame}
|
||||||
|
|
||||||
|
|
||||||
|
%from file ../knn1//data/005Better knn.txt
|
||||||
|
\begin{frame}[label=Better knn]
|
||||||
|
\frametitle{Better knn}
|
||||||
|
\begin{itemize}
|
||||||
|
|
||||||
|
\item The model (mostly) ignores every known sample except one
|
||||||
|
|
||||||
|
\item So there are extensions
|
||||||
|
|
||||||
|
\item $avg=\frac{1}{N} \sum_i knn_i(x)$
|
||||||
|
|
||||||
|
\item $wavg=\frac{1}{N} \sum_i \frac{knn_i(x)}{i}$
|
||||||
|
|
||||||
|
|
||||||
|
\end{itemize}
|
||||||
|
\end{frame}
|
||||||
|
|
||||||
|
|
||||||
|
%from file ../knn1//data/006Comparison.txt
|
||||||
|
\begin{frame}[label=Comparison]
|
||||||
|
%\frametitle{Comparison}
|
||||||
|
|
||||||
|
|
||||||
|
\begin{tabular}{llllll}
|
||||||
|
\hline
|
||||||
|
Dataset & wavg & avg & 1 & 3 & 5 \\
|
||||||
|
\hline
|
||||||
|
$vertebral$ & $\textbf{0.4506}$ & $\textbf{0.4506}$ & $\textbf{0.4667}$ & $\textbf{0.4667}$ & $\textbf{0.45}$ \\
|
||||||
|
... & & & & & \\
|
||||||
|
$thyroid$ & $\textbf{0.9138}$ & $\textbf{0.9151}$ & $\textbf{0.8763}$ & $\textbf{0.9086}$ & $\textbf{0.914}$ \\
|
||||||
|
$Iris\_setosa$ & $\textbf{0.9333}$ & $\textbf{0.9333}$ & $\textbf{0.9333}$ & $\textbf{0.9}$ & $\textbf{0.9}$ \\
|
||||||
|
$breastw$ & $\textbf{0.9361}$ & $\textbf{0.9361}$ & $\textbf{0.9211}$ & $\textbf{0.9248}$ & $\textbf{0.9286}$ \\
|
||||||
|
$wine$ & $\textbf{0.95}$ & $\textbf{0.95}$ & $\textbf{0.9}$ & $\textbf{0.95}$ & $\textbf{0.95}$ \\
|
||||||
|
$pendigits$ & $\textbf{0.9487}$ & $\textbf{0.9487}$ & $\textbf{0.9391}$ & $\textbf{0.9295}$ & $\textbf{0.9359}$ \\
|
||||||
|
$segment$ & $\textbf{0.9747}$ & $\textbf{0.9747}$ & $\textbf{0.9495}$ & $\textbf{0.9545}$ & $\textbf{0.9394}$ \\
|
||||||
|
$banknote-authentication$ & $\textbf{0.9777}$ & $\textbf{0.9776}$ & $\textbf{0.9408}$ & $\textbf{0.943}$ & $\textbf{0.9583}$ \\
|
||||||
|
$vowels$ & $\textbf{0.9998}$ & $\textbf{0.9972}$ & $\textbf{0.99}$ & $\textbf{0.92}$ & $\textbf{0.93}$ \\
|
||||||
|
$Ecoli$ & $\textbf{1.0}$ & $\textbf{1.0}$ & $\textbf{0.9}$ & $\textbf{1.0}$ & $\textbf{1.0}$ \\
|
||||||
|
$$ & $$ & $$ & $$ & $$ & $$ \\
|
||||||
|
$Average$ & $\textbf{0.7528} $ & $\textbf{0.7520} $ & $0.7325 $ & $0.7229 $ & $0.7157 $ \\
|
||||||
|
\hline
|
||||||
|
\end{tabular}
|
||||||
|
|
||||||
|
|
||||||
|
|
||||||
|
\end{frame}
|
||||||
|
|
||||||
|
|
||||||
|
%from file ../knn1//data/007What to do?.txt
|
||||||
|
\begin{frame}[label=What to do?]
|
||||||
|
\frametitle{What to do?}
|
||||||
|
\begin{itemize}
|
||||||
|
|
||||||
|
\item Evaluation as anomaly detector is complicated
|
||||||
|
|
||||||
|
\begin{itemize}
|
||||||
|
|
||||||
|
\item Requires known anomalies
|
||||||
|
|
||||||
|
|
||||||
|
\end{itemize}
|
||||||
|
\item $\Rightarrow$So evaluate as density estimator
|
||||||
|
|
||||||
|
\begin{itemize}
|
||||||
|
|
||||||
|
\item Does not require anomalies
|
||||||
|
|
||||||
|
\item Allows generating infinite amounts of training data
|
||||||
|
|
||||||
|
|
||||||
|
\end{itemize}
|
||||||
|
|
||||||
|
\end{itemize}
|
||||||
|
\end{frame}
|
||||||
|
|
||||||
|
|
||||||
|
%from file ../knn1//data/008What to do?.txt
|
||||||
|
\begin{frame}[label=What to do?]
|
||||||
|
\frametitle{What to do?}
|
||||||
|
\begin{itemize}
|
||||||
|
|
||||||
|
\item Collect Extensions of the oc-knn algorithm
|
||||||
|
|
||||||
|
\item Define some distance measure to a known pdf
|
||||||
|
|
||||||
|
\item Generate random datapoints following the pdf
|
||||||
|
|
||||||
|
\item Evaluate which algorithm finds the pdf the best
|
||||||
|
|
||||||
|
|
||||||
|
\end{itemize}
|
||||||
|
\end{frame}
|
||||||
|
|
||||||
|
|
||||||
|
%from file ../knn1//data/009Requirements.txt
|
||||||
|
\begin{frame}[label=Requirements]
|
||||||
|
\frametitle{Requirements}
|
||||||
|
\begin{itemize}
|
||||||
|
|
||||||
|
\item Knowledge of python ( sum([i for i in range(5) if i\%2]) )
|
||||||
|
|
||||||
|
\begin{itemize}
|
||||||
|
|
||||||
|
\item Ideally incl numpy
|
||||||
|
|
||||||
|
|
||||||
|
\end{itemize}
|
||||||
|
\item Basic university level Math (you could argue that $r_k \propto \frac{k}{pdf}$)
|
||||||
|
|
||||||
|
\item Ideally some experience working on a ssh server
|
||||||
|
|
||||||
|
\item $\Rightarrow$Good as a Bachelor Thesis
|
||||||
|
|
||||||
|
\item For a Master Thesis, I would extend this a bit (Could you also find $k$?)
|
||||||
|
|
||||||
|
|
||||||
|
\end{itemize}
|
||||||
|
\end{frame}
|
||||||
|
|
||||||
|
|
||||||
|
%from file ../knn1//data/010Normalising Flows.txt
|
||||||
|
\begin{frame}[label=Normalising Flows]
|
||||||
|
\frametitle{Normalising Flows}
|
||||||
|
\begin{itemize}
|
||||||
|
|
||||||
|
\item Deep Learning Method, in which the output is normalised
|
||||||
|
|
||||||
|
\item $\int f(x) dx=1 \; \forall f(x)$
|
||||||
|
|
||||||
|
\item Can be used to estimate probability density functions
|
||||||
|
|
||||||
|
\item $\Rightarrow$Thus useful for AD
|
||||||
|
|
||||||
|
\item $\int f(h(x)) \|\frac{\delta x}{\delta h}\| dh=1 \; \forall h(x)$
|
||||||
|
|
||||||
|
|
||||||
|
\end{itemize}
|
||||||
|
\end{frame}
|
||||||
|
|
||||||
|
|
||||||
|
%from file ../knn1//data/011Graph Normalising Flows.txt
|
||||||
|
\begin{frame}[label=Graph Normalising Flows]
|
||||||
|
\frametitle{Graph Normalising Flows}
|
||||||
|
\begin{itemize}
|
||||||
|
|
||||||
|
\item How to apply this to graphs?
|
||||||
|
|
||||||
|
\item One Paper (Liu 2019) uses two NN:
|
||||||
|
|
||||||
|
\item Autoencoder graph$\Rightarrow$vector
|
||||||
|
|
||||||
|
\item NF on vector data
|
||||||
|
|
||||||
|
\item which is fine, but also not really graph specific
|
||||||
|
|
||||||
|
\item No interaction between encoding and transformation
|
||||||
|
|
||||||
|
|
||||||
|
\end{itemize}
|
||||||
|
\end{frame}
|
||||||
|
|
||||||
|
|
||||||
|
%from file ../knn1//data/012Graph Normalising Flows.txt
|
||||||
|
\begin{frame}[label=Graph Normalising Flows]
|
||||||
|
\frametitle{Graph Normalising Flows}
|
||||||
|
\begin{itemize}
|
||||||
|
|
||||||
|
\item So why not do this directly?
|
||||||
|
|
||||||
|
\item $\Rightarrow$Requires differentiating a graph
|
||||||
|
|
||||||
|
\item Why not use only one Network?
|
||||||
|
|
||||||
|
\item Graph$\Rightarrow$Vector$\Rightarrow$pdf
|
||||||
|
|
||||||
|
\item $\Rightarrow$Finds trivial solution, as $<pdf> \propto \frac{1}{\sigma_{Vector}}$
|
||||||
|
|
||||||
|
\item So regularise the standart deviation of the vector space!
|
||||||
|
|
||||||
|
\begin{itemize}
|
||||||
|
|
||||||
|
\item Interplay between encoding and NF
|
||||||
|
|
||||||
|
\item Could also be useful for highdim data
|
||||||
|
|
||||||
|
|
||||||
|
\end{itemize}
|
||||||
|
|
||||||
|
\end{itemize}
|
||||||
|
\end{frame}
|
||||||
|
|
||||||
|
|
||||||
|
%from file ../knn1//data/013Requirements.txt
|
||||||
|
\begin{frame}[label=Requirements]
|
||||||
|
\frametitle{Requirements}
|
||||||
|
\begin{itemize}
|
||||||
|
|
||||||
|
\item Proficient in python ( [i for i in range(1,N) if not [j for j in range(2,i) if not i\%j]] )
|
||||||
|
|
||||||
|
\begin{itemize}
|
||||||
|
|
||||||
|
\item Ideally incl numpy, tensorflow, keras
|
||||||
|
|
||||||
|
|
||||||
|
\end{itemize}
|
||||||
|
\item Some deep learning experience
|
||||||
|
|
||||||
|
\item University level math (google Cholesky Decomposition. Why is this useful for NF?)
|
||||||
|
|
||||||
|
\item Ideally some experience working on a ssh server
|
||||||
|
|
||||||
|
\item A bit more challenging$\Rightarrow$Better as a Master thesis
|
||||||
|
|
||||||
|
\item (Still we would start very slowly of course)
|
||||||
|
|
||||||
|
|
||||||
|
\end{itemize}
|
||||||
|
\end{frame}
|
||||||
|
|
||||||
|
|
||||||
|
%from file ../knn1//data/014Old Thesis Sina.txt
|
||||||
|
\begin{frame}[label=Old Thesis Sina]
|
||||||
|
\frametitle{Sina}
|
||||||
|
\begin{columns}[c] % align columns
|
||||||
|
\begin{column}{0.48\textwidth}%.48
|
||||||
|
\begin{itemize}
|
||||||
|
|
||||||
|
\item Isolation Forest: Different Anomaly Detection Algorithm
|
||||||
|
|
||||||
|
\item Problem: Isolation Forests dont work on categorical data
|
||||||
|
|
||||||
|
\item $\Rightarrow$Extend them to categorical data
|
||||||
|
|
||||||
|
|
||||||
|
\end{itemize}
|
||||||
|
\end{column}%
|
||||||
|
\hfill%
|
||||||
|
\begin{column}{0.48\textwidth}%.48
|
||||||
|
\begin{figure}[H]
|
||||||
|
\centering
|
||||||
|
\includegraphics[width=0.9\textwidth]{../prep/20Old_Thesis_Sina/Bildschirmfoto vom 2022-09-26 16-22-30.png}
|
||||||
|
\label{fig:prep20Old_Thesis_SinaBildschirmfoto vom 2022-09-26 16-22-30png}
|
||||||
|
\end{figure}
|
||||||
|
|
||||||
|
|
||||||
|
\end{column}%
|
||||||
|
\hfill%
|
||||||
|
\end{columns}
|
||||||
|
|
||||||
|
\end{frame}
|
||||||
|
|
||||||
|
|
||||||
|
%from file ../knn1//data/015Old Thesis Britta.txt
|
||||||
|
\begin{frame}[label=Old Thesis Britta]
|
||||||
|
\frametitle{Britta}
|
||||||
|
\begin{columns}[c] % align columns
|
||||||
|
\begin{column}{0.58\textwidth}%.48
|
||||||
|
\begin{itemize}
|
||||||
|
|
||||||
|
\item Reidentification: Find known objects in new images
|
||||||
|
|
||||||
|
\item Task: Find if two images of pallet blocks are of the same pallet block
|
||||||
|
|
||||||
|
\item Use AD to represent the pallet blocks
|
||||||
|
|
||||||
|
|
||||||
|
\end{itemize}
|
||||||
|
\end{column}%
|
||||||
|
\hfill%
|
||||||
|
\begin{column}{0.38\textwidth}%.48
|
||||||
|
\begin{figure}[H]
|
||||||
|
\centering
|
||||||
|
\includegraphics[width=0.9\textwidth]{../prep/21Old_Thesis_Britta/Bildschirmfoto vom 2022-09-26 16-23-26.png}
|
||||||
|
\label{fig:prep21Old_Thesis_BrittaBildschirmfoto vom 2022-09-26 16-23-26png}
|
||||||
|
\end{figure}
|
||||||
|
|
||||||
|
|
||||||
|
\end{column}%
|
||||||
|
\hfill%
|
||||||
|
\end{columns}
|
||||||
|
|
||||||
|
\end{frame}
|
||||||
|
|
||||||
|
|
||||||
|
%from file ../knn1//data/016Old Thesis Hsin Ping.txt
|
||||||
|
\begin{frame}[label=Old Thesis Hsin Ping]
|
||||||
|
\frametitle{Hsin Ping}
|
||||||
|
\begin{columns}[c] % align columns
|
||||||
|
\begin{column}{0.48\textwidth}%.48
|
||||||
|
\begin{itemize}
|
||||||
|
|
||||||
|
\item Ensemble: Combination of multiple models
|
||||||
|
|
||||||
|
\item Task: Explain the prediction of a model using the ensemble structure
|
||||||
|
|
||||||
|
|
||||||
|
\end{itemize}
|
||||||
|
\end{column}%
|
||||||
|
\hfill%
|
||||||
|
\begin{column}{0.48\textwidth}%.48
|
||||||
|
\begin{figure}[H]
|
||||||
|
\centering
|
||||||
|
\includegraphics[width=0.9\textwidth]{../prep/22Old_Thesis_Hsin_Ping/Bildschirmfoto vom 2022-09-26 16-24-14.png}
|
||||||
|
\label{fig:prep22Old_Thesis_Hsin_PingBildschirmfoto vom 2022-09-26 16-24-14png}
|
||||||
|
\end{figure}
|
||||||
|
|
||||||
|
|
||||||
|
\end{column}%
|
||||||
|
\hfill%
|
||||||
|
\end{columns}
|
||||||
|
|
||||||
|
\end{frame}
|
||||||
|
|
||||||
|
|
||||||
|
%from file ../knn1//data/017Old Thesis Nikitha.txt
|
||||||
|
\begin{frame}[label=Old Thesis Nikitha]
|
||||||
|
\frametitle{Nikitha}
|
||||||
|
\begin{columns}[c] % align columns
|
||||||
|
\begin{column}{0.48\textwidth}%.48
|
||||||
|
\begin{itemize}
|
||||||
|
|
||||||
|
\item Task: Explore a new kind of ensemble
|
||||||
|
|
||||||
|
\item Instead of many uncorrelated models, let the models interact during training
|
||||||
|
|
||||||
|
|
||||||
|
\end{itemize}
|
||||||
|
\end{column}%
|
||||||
|
\hfill%
|
||||||
|
\begin{column}{0.48\textwidth}%.48
|
||||||
|
\begin{figure}[H]
|
||||||
|
\centering
|
||||||
|
\includegraphics[width=0.9\textwidth]{../prep/23Old_Thesis_Nikitha/Bildschirmfoto vom 2022-09-26 16-25-06.png}
|
||||||
|
\label{fig:prep23Old_Thesis_NikithaBildschirmfoto vom 2022-09-26 16-25-06png}
|
||||||
|
\end{figure}
|
||||||
|
|
||||||
|
|
||||||
|
\end{column}%
|
||||||
|
\hfill%
|
||||||
|
\end{columns}
|
||||||
|
|
||||||
|
\end{frame}
|
||||||
|
|
||||||
|
\begin{frame}
|
||||||
|
Questions?
|
||||||
|
\end{frame}
|
||||||
|
|
||||||
|
|
||||||
|
|
||||||
|
\end{document}
|
||||||
|
|
@ -0,0 +1 @@
|
||||||
|
<titlepage>
|
||||||
|
|
@ -0,0 +1,15 @@
|
||||||
|
First: Find a topic and a supervisor
|
||||||
|
Work one month on this, to make sure
|
||||||
|
<l2st>
|
||||||
|
you still like your topic
|
||||||
|
and you are sure you can handle the topic
|
||||||
|
</l2st>
|
||||||
|
then short presentation in front of our chair (15min, relaxed)
|
||||||
|
<l2st>
|
||||||
|
get some feedback/suggestions
|
||||||
|
</l2st>
|
||||||
|
afterwards register the thesis
|
||||||
|
<l2st>
|
||||||
|
(different for CS/DS students)
|
||||||
|
</l2st>
|
||||||
|
Problem: We are not able to supervise more than 2 students at the same time (CS faculty rules)
|
||||||
|
|
@ -0,0 +1,3 @@
|
||||||
|
First: A short summary of each Topic
|
||||||
|
Then time for questions/Talk with your supervisor about each topic that sounds interesting
|
||||||
|
Your own topics are always welcome;)
|
||||||
Binary file not shown.
|
|
@ -0,0 +1,2 @@
|
||||||
|
Im working on Anomaly Detection
|
||||||
|
That means characterising an often very complex distributions, to find events that dont match the expected distribution
|
||||||
|
|
@ -0,0 +1,4 @@
|
||||||
|
kNN algorithm can also be used for AD
|
||||||
|
if the k closest point is further away, a sample is considered more anomalous
|
||||||
|
$r=\frac{k}{2N\cdot pdf}$
|
||||||
|
Powerful method, as it can model the pdf directly
|
||||||
|
|
@ -0,0 +1,5 @@
|
||||||
|
The model (mostly) ignores every known sample except one
|
||||||
|
So there are extensions
|
||||||
|
$avg=\frac{1}{N} \sum_i knn_i(x)$
|
||||||
|
$wavg=\frac{1}{N} \sum_i \frac{knn_i(x)}{i}$
|
||||||
|
|
||||||
|
|
@ -0,0 +1 @@
|
||||||
|
nothing
|
||||||
|
|
@ -0,0 +1,9 @@
|
||||||
|
Evaluation as anomaly detector is complicated
|
||||||
|
<l2st>
|
||||||
|
Requires known anomalies
|
||||||
|
</l2st>
|
||||||
|
->So evaluate as density estimator
|
||||||
|
<l2st>
|
||||||
|
Does not require anomalies
|
||||||
|
Allows generating infinite amounts of training data
|
||||||
|
</l2st>
|
||||||
|
|
@ -0,0 +1,4 @@
|
||||||
|
Collect Extensions of the oc-knn algorithm
|
||||||
|
Define some distance measure to a known pdf
|
||||||
|
Generate random datapoints following the pdf
|
||||||
|
Evaluate which algorithm finds the pdf the best
|
||||||
|
|
@ -0,0 +1,8 @@
|
||||||
|
Knowledge of python ( sum([i for i in range(5) if i\%2]) )
|
||||||
|
<l2st>
|
||||||
|
Ideally incl numpy
|
||||||
|
</l2st>
|
||||||
|
Basic university level Math (you could argue that $r_k \propto \frac{k}{pdf}$)
|
||||||
|
Ideally some experience working on a ssh server
|
||||||
|
->Good as a Bachelor Thesis
|
||||||
|
For a Master Thesis, I would extend this a bit (Could you also find $k$?)
|
||||||
|
|
@ -0,0 +1,5 @@
|
||||||
|
Deep Learning Method, in which the output is normalised
|
||||||
|
$\int f(x) dx=1 \; \forall f(x)$
|
||||||
|
Can be used to estimate probability density functions
|
||||||
|
->Thus useful for AD
|
||||||
|
$\int f(h(x)) \|\frac{\delta h}{\delta x}\| dx=1 \; \forall h(x)$
|
||||||
|
|
@ -0,0 +1,6 @@
|
||||||
|
How to apply this to graphs?
|
||||||
|
One Paper (Liu 2019) uses two NN:
|
||||||
|
Autoencoder graph->vector
|
||||||
|
NF on vector data
|
||||||
|
which is fine, but also not really graph specific
|
||||||
|
No interaction between encoding and transformation
|
||||||
|
|
@ -0,0 +1,10 @@
|
||||||
|
So why not do this directly?
|
||||||
|
->Requires differentiating a graph
|
||||||
|
Why not use only one Network?
|
||||||
|
Graph->Vector->pdf
|
||||||
|
->Finds trivial solution, as $<pdf> \propto \frac{1}{\sigma_{Vector}}$
|
||||||
|
So regularise the standart deviation of the vector space!
|
||||||
|
<l2st>
|
||||||
|
Interplay between encoding and NF
|
||||||
|
Could also be useful for highdim data
|
||||||
|
</l2st>
|
||||||
|
|
@ -0,0 +1,9 @@
|
||||||
|
Proficient in python ( [i for i in range(1,N) if not [j for j in range(2,i) if not i\%j]] )
|
||||||
|
<l2st>
|
||||||
|
Ideally incl numpy, tensorflow, keras
|
||||||
|
</l2st>
|
||||||
|
Some deep learning experience
|
||||||
|
University level math (google Cholesky Decomposition. Why is this useful for NF?)
|
||||||
|
Ideally some experience working on a ssh server
|
||||||
|
A bit more challenging->Better as a Master thesis
|
||||||
|
(Still we would start very slowly of course)
|
||||||
Binary file not shown.
|
After Width: | Height: | Size: 18 KiB |
|
|
@ -0,0 +1,3 @@
|
||||||
|
Isolation Forest: Different Anomaly Detection Algorithm
|
||||||
|
Problem: Isolation Forests dont work on categorical data
|
||||||
|
->Extend them to categorical data
|
||||||
Binary file not shown.
|
After Width: | Height: | Size: 979 KiB |
|
|
@ -0,0 +1,3 @@
|
||||||
|
Reidentification: Find known objects in new images
|
||||||
|
Task: Find if two images of pallet blocks are of the same pallet block
|
||||||
|
Use AD to represent the pallet blocks
|
||||||
Binary file not shown.
|
After Width: | Height: | Size: 43 KiB |
|
|
@ -0,0 +1,2 @@
|
||||||
|
Ensemble: Combination of multiple models
|
||||||
|
Task: Explain the prediction of a model using the ensemble structure
|
||||||
Binary file not shown.
|
After Width: | Height: | Size: 47 KiB |
|
|
@ -0,0 +1,2 @@
|
||||||
|
Task: Explore a new kind of ensemble
|
||||||
|
Instead of many uncorrelated models, let the models interact during training
|
||||||
Binary file not shown.
|
After Width: | Height: | Size: 20 KiB |
Binary file not shown.
|
After Width: | Height: | Size: 989 KiB |
Binary file not shown.
|
After Width: | Height: | Size: 265 KiB |
Loading…
Reference in New Issue