A Boltzmann machine is the name given to a type of stochastic recurrent neural network by Geoffrey
Hinton and Terry Sejnowski. Boltzmann machines can be seen
as the stochastic, generative
counterpart of Hopfield nets. They were one of the first
examples of a neural network capable of learning internal representations, and
are able to represent and (given sufficient time) solve difficult combinatoric
problems. However, due to a number of issues discussed below, Boltzmann
machines with unconstrained connectivity have not proven useful for practical
problems in machine learning or inference. They are still theoretically
intriguing, however, due to the locality and Hebbian
nature of their training algorithm, as well as their parallelism and the
resemblance of their dynamics to simple physical processes. If the connectivity
is constrained, the learning can be made efficient enough to be useful for
practical problems.
They are named after the Boltzmann distribution in statistical
mechanics, which is used in their sampling function.
