@ARTICLE{Ackley1985,
  author = {David H. Ackley and Geoffrey E. Hinton and Terrence J. Sejnowski},
  title = {A learning algorithm for boltzmann machines},
  journal = {Cognitive Science},
  year = {1985},
  volume = {9},
  pages = {147 - 169},
  number = {1},
  abstract = {The computational power of massively parallel networks of simple processing
	elements resides in the communication bandwidth provided by the hardware
	connections between elements. These connections can allow a significant
	fraction of the knowledge of the system to be applied to an instance
	of a problem in a very short time. One kind of computation for which
	massively parallel networks appear to be well suited is large constraint
	satisfaction searches, but to use the connections efficiently two
	conditions must be met: First, a search technique that is suitable
	for parallel networks must be found. Second, there must be some way
	of choosing internal representations which allow the preexisting
	hardware connections to be used efficiently for encoding the constraints
	in the domain being searched. We describe a general parallel search
	method, based on statistical mechanics, and we show how it leads
	to a general learning rule for modifying the connection strengths
	so as to incorporate knowledge about a task domain in an efficient
	way. We describe some simple examples in which the learning algorithm
	creates internal representations that are demonstrably the most efficient
	way of using the preexisting connectivity structure.},
  issn = {0364-0213},
  url = {http://www.sciencedirect.com/science/article/B6W48-4DXC45K-R/2/9dd70cd8af63b2156a22866fb1172803}
}
