Construct · 1 fields · 1 citations

contrastive divergence: a standalone construct

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Machine learning

an approximate learning rule for products of experts and Boltzmann-type models that compares data statistics with those after only a few sampling steps.

In machine learning, contrastive divergence refers to an approximate learning rule for products of experts and Boltzmann-type models that compares data statistics with those after only a few sampling steps. This meaning comes from Hinton (2002) in Neural Computation.[1]

Unit
Recovery means
Measured by

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Nearby constructs

divergence4 fields · 4 citationsjingle · one word, four constructs learning6 fields · 14 citationsjingle · one name, six constructs

References

Hinton, G. E. (2002). Training Products of Experts by Minimizing Contrastive Divergence. Neural Computation, 14(8), 1771-1800. https://doi.org/10.1162/089976602760128018

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