Varieties of Helmholtz Machine

Dayan, P; Hinton, GE

doi:10.1016/S0893-6080(96)00009-3

Item

ITEM ACTIONSEXPORT

Add to Basket

Local TagsRelease HistoryDetailsSummary

Released

Journal Article

Varieties of Helmholtz Machine

MPS-Authors

There are no MPG-Authors in the publication available

External Resource

https://www.sciencedirect.com/science/article/pii/S0893608096000093
(Publisher version)

Fulltext (restricted access)

There are currently no full texts shared for your IP range.

Fulltext (public)

There are no public fulltexts stored in PuRe

Supplementary Material (public)

There is no public supplementary material available

Citation

Dayan, P., & Hinton, G. (1996). Varieties of Helmholtz Machine. Neural networks, 9(8), 1385-1403. doi:10.1016/S0893-6080(96)00009-3.

Cite as: https://hdl.handle.net/21.11116/0000-0002-D683-8

Abstract

The Helmholtz machine is a new unsupervised learning architecture that uses top-down connections to build probability density models of input and bottom-up connections to build inverses to those models. The wake-sleep learning algorithm for the machine involves just the purely local delta rule. This paper suggests a number of different varieties of Helmholtz machines, each with its own strengths and weaknesses, and relates them to cortical information processing.