<subsection Scaling>
<frame>

<split>
<que>
<list>
<e>Now lets increase the graph size(gs)</e>
<e>plot auc against it</e>
<e>compare to QCDorWhat results</e>
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</que>
<que>
<i f="lowscale"></i>
</que>
</split>

</frame>
<frame>
<i f="trivscale"></i>
</frame>
<frame>
<i f="compscale"></i>
</frame>
<frame>
<i f="compscale_zoom"></i>
</frame>
<frame>
<split>
<que w="0.4">
<list>
<e>Now instead of training on network with #4*n# nodes, train #n# networks on #4# nodes each and combine them into one #4*n# network</e>
<e>if the network would be supervised, this would hurt, since there is no interaction between particles possible</e>
<e>but here it actually helps</e>
</list>
</que>
<que w="0.58">
<i f="splitscale"></i>
</que>
</split>
</frame>



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