Multimodal learning: Difference between revisions

Revision as of 18:15, 12 June 2020

Revision as of 08:38, 5 July 2020

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==Background: Boltzmann machine==

A [[Boltzmann machine]] is a type of stochastic neural network invented by [[Geoffrey Hinton]] and [[Terry Sejnowski]] in 1985. Boltzmann machines can be seen as the [[stochastic process|stochastic]], [[generative model|generative]] counterpart of [[Hopfield net]]s. They are named after the [[Boltzmann distribution]] in statistical mechanics. The units in Boltzmann machines are divided into two groups-: visible units and hidden units. General Boltzmann machines allow connection between any units. However, learning is impractical using general Boltzmann Machines because the computational time is exponential to the size of the machine. A more efficient architecture is called '''[[restricted Boltzmann machine]]''' where connection is only allowed between hidden unit and visible unit, which is described in the next section.

===Restricted Boltzmann machine===