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We decompose the evidence lower bound to show the existence of a term measuring the total correlation between latent variables.
Information theoretical analysis of multivariate correlation
Satosi Watanabe · 1960
Earlier work this paper cites.
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Jürgen Schmidhuber · 1992
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Earlier work this paper cites.
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Earlier work this paper cites.
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Earlier work this paper cites.
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Earlier work this paper cites.
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Earlier work this paper cites.
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