What is recorded here?
Introduced a stochastic parallel-network learning rule derived from statistical-mechanical search, adjusting connection strengths so that the network learns internal representations of task constraints.
Source-linked catalogue note. A detailed explanatory guide has not yet been prepared for this entry.
Scientific scope
The record is the exact 1985 Cognitive Science paper. Hopfield networks, simulated annealing, later restricted Boltzmann machines, deep belief networks and modern energy-based models remain separate.
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People and systems
David H. Ackley; Geoffrey E. Hinton; Terrence J. Sejnowski
The named authors contributed to this scoped work; individual effort shares and later applications are not assigned by the byline.
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Documented countries / regions: United States
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English catalogue narrative
Ackley, Hinton and Sejnowski: a learning algorithm for Boltzmann machines
Introduced a stochastic parallel-network learning rule derived from statistical-mechanical search, adjusting connection strengths so that the network learns internal representations of task constraints.
Scientific scope
The record is the exact 1985 Cognitive Science paper. Hopfield networks, simulated annealing, later restricted Boltzmann machines, deep belief networks and modern energy-based models remain separate.
Reading status
This is a source-linked WRS catalogue note. A full explanatory guide and independent scientific reconstruction have not been completed for this entry. Read the identified original work for its complete narrative and arguments.
People and systems
David H. Ackley; Geoffrey E. Hinton; Terrence J. Sejnowski
The named authors contributed to this scoped work; individual effort shares and later applications are not assigned by the byline.