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describing multimodal large language models |
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{{excerpt|Transformer (machine learning model)|Multimodality}}
==Multimodal large language models==
{{excerpt|Large language model|Multimodality}}
== Multimodal deep Boltzmann machines ==
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. Each unit is like a neuron with a binary output that represents whether it's activated or not.<ref>{{Cite web |last=Dey |first=Victor |date=2021-09-03 |title=Beginners Guide to Boltzmann Machine |url=https://analyticsindiamag.com/beginners-guide-to-boltzmann-machines/ |access-date=2024-03-02 |website=Analytics India Magazine |language=en-US}}</ref> 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{{Citation needed|date=November 2022}}. 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.
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