<subsection Graph Autoencoder>
<frame>

<list>
<e>So here ParticleNet + QCDorWhat</e>
<e>=Graph Autoencoder</e>
<e>To do this, require:</e>
<l2st>
<e>An update step</e>
<l3st><e>already described</e></l3st>
<ignore>
<e>Something to make a list of 4 vectors into a graph</e>
<l3st>
<e>use a topK algorithm</e>
<e>connect a 4 vector with its K nearest neigbours</e>
</l3st>
</ignore>
<e>something to reduce the number of nodes</e>
<e>something to increase the number of nodes afterwards again</e>
</l2st>
</list>

</frame>
<frame title="Going from a big graph to a small graph">
<list>
<e>similar to a Pooling operation for a convolutional network</e>
<e>Seems simple enough but if you look at the literature</e>
<l2st>
<e>slow...and the benefits...are less clear (arXiv:1907.09000)</e>
<e>advance...has lagged behind (arXiv:1907.00481)</e>
<e>one cannot simply pool ... (arXiv:1806.08804)</e>
</l2st>
</list>

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<list>
<e>project the graph on a learnable axis</e>
<e>combine neigbour nodes on this axis</e>
<e>relearn the graph or use a graph combination rule</e>
</list>
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<que>
<i f="abiaa" wmode=True></i>
<ignore>maybe instead of multiple images below each other doo this on multiple pages</ignore>
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<e>let each node grow into a learnable graph</e>
<e>combine the new graphs with the existing one</e>
</list>
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<frame><i f="../../mmeet2/imgs/00.pdf"/></frame>
<frame><i f="../../mmeet2/imgs/01.pdf"/></frame>
<frame><i f="../../mmeet2/imgs/02.pdf"/></frame>
<frame><i f="../../mmeet2/imgs/03.pdf"/></frame>
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<frame><i f="../../mmeet2/imgs/06.pdf"/></frame>
<frame><i f="../../mmeet2/imgs/07.pdf"/></frame>
<frame><i f="../../mmeet2/imgs/08.pdf"/></frame>
<frame><i f="../../mmeet2/imgs/06.pdf"/></frame>
<frame><i f="../../mmeet2/imgs/01.pdf"/></frame>

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