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Do deep learning algorithms run into trouble when tasked with classifying high dimensional input into one of many categories? By many I mean thousands or millions. If it does, how could one deal with this problem? Any references?

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  • $\begingroup$ by deep learning do you mean neural networks? $\endgroup$ – Franck Dernoncourt Dec 30 '16 at 17:25
  • $\begingroup$ @FranckDernoncourt yeah, sorry I'm not up to speed on the jargon with these things $\endgroup$ – Taylor Dec 30 '16 at 17:35
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    $\begingroup$ In science, when categorizing with millions of possible categories, usually some hierarchcal system is built, like in biological systematics. There will be two cases: Building such a system, or cateforizing into a known system. Which case is yours? $\endgroup$ – kjetil b halvorsen Dec 30 '16 at 18:47
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    $\begingroup$ @kjetilbhalvorsen thanks that's helpful. I don't really have one, I am just being curious at the moment. I know the output of say a CNN will give you a vector of probabilities over your categories, so I am curious if and why things become hard to discern when looking at examples besides this MNIST dataset. The more bins you divide up $1$ into, the smaller the differences in their volume. just wondering $\endgroup$ – Taylor Dec 30 '16 at 19:02
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If it does, how could one deal with this problem? Any references?

You can use hierarchical softmax, importance sampling, noise contrastive estimation, or negative sampling: they are commonly used in language modeling, for example.

FYI:

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