Varieties of Helmholtz Machine

Published: 01 Jan 1996, Last Modified: 18 Feb 2025Neural Networks 1996EveryoneRevisionsBibTeXCC BY-SA 4.0
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. Copyright © 1996 Elsevier Science Ltd.
Loading

OpenReview is a long-term project to advance science through improved peer review with legal nonprofit status. We gratefully acknowledge the support of the OpenReview Sponsors. © 2025 OpenReview