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Restricted
Boltzmann Machines provide a simple way to
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learn a layer of
features without any supervision.
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Many layers of
representation can be learned by treating
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the hidden
states of one RBM as the visible data for
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training the next
RBM.
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This creates
good generative models that can then be
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fine-tuned.
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Backpropagation
can fine-tune discrimination.
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Contrastive
wake-sleep can fine-tune generation.
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The same ideas
can be used for non-linear
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dimensionality
reduction.
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This
leads to very effective ways of visualizing sets of
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documents
or searching for similar documents.
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