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Without a
desired output or reinforcement signal it is
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much less obvious
what the goal is.
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Discover useful
structure in large data sets without
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requiring a
supervisory signal
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Create
representations that are better for subsequent
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supervised
or reinforcement learning
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Build
a density model that can be used to:
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Classify
by seeing which model likes the test case data most
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Monitor
a complex system by noticing improbable states.
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Extract
interpretable factors (causes or constraints)
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Improve learning
speed for high-dimensional inputs
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Allow
features within a layer to learn independently
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Allow
multiple layers to be learned greedily.
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