Helmholtz machine

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The Helmholtz machine (named after Hermann von Helmholtz and his concept of Helmholtz free energy) is a type of artificial neural network that can account for the hidden structure of a set of data by being trained to create a generative model of the original set of data.[1] The hope is that by learning economical representations of the data, the underlying structure of the generative model should reasonably approximate the hidden structure of the data set. A Helmholtz machine contains two networks, a bottom-up recognition network that takes the data as input and produces a distribution over hidden variables, and a top-down "generative" network that generates values of the hidden variables and the data itself. At the time, Helmholtz machines were one of a handful of learning architectures that used feedback as well as feedforward to ensure quality of learned models.[2]

Helmholtz machines are usually trained using an unsupervised learning algorithm, such as the wake-sleep algorithm.[3] They are a precursor to variational autoencoders, which are instead trained using backpropagation. Helmholtz machines may also be used in applications requiring a supervised learning algorithm (e.g. character recognition, or position-invariant recognition of an object within a field).

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  1. ^ Peter, Dayan; Hinton, Geoffrey E.; Neal, Radford M.; Zemel, Richard S. (1995). "The Helmholtz machine". Neural Computation. 7 (5): 889–904. doi:10.1162/neco.1995.7.5.889. hdl:21.11116/0000-0002-D6D3-E. PMID 7584891. S2CID 1890561. Closed access icon
  2. ^ Luttrell S.P. (1994). A Bayesian analysis of self-organizing maps. Neural Computation. 1994 Sep 1;6(5):767-94.[1]
  3. ^ Hinton, Geoffrey E.; Dayan, Peter; Frey, Brendan J.; Neal, Radford (1995-05-26). "The wake-sleep algorithm for unsupervised neural networks". Science. 268 (5214): 1158–1161. Bibcode:1995Sci...268.1158H. doi:10.1126/science.7761831. PMID 7761831. S2CID 871473. Closed access icon

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