What is recorded here?
Derived a greedy layer-wise learning procedure for deep belief networks using complementary priors and restricted Boltzmann machines, then fine-tuned the resulting generative model.
Source-linked catalogue note. A detailed explanatory guide has not yet been prepared for this entry.
Scientific scope
The record is the exact three-author 2006 Neural Computation paper. It does not absorb earlier Boltzmann machines and wake–sleep, later deep-learning systems, or claim that its reported digit experiments establish general intelligence.
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People and systems
Geoffrey E. Hinton; Simon Osindero; Yee-Whye Teh
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: Canada, Singapore
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English catalogue narrative
Hinton, Osindero and Teh: fast learning for deep belief nets
Derived a greedy layer-wise learning procedure for deep belief networks using complementary priors and restricted Boltzmann machines, then fine-tuned the resulting generative model.
Scientific scope
The record is the exact three-author 2006 Neural Computation paper. It does not absorb earlier Boltzmann machines and wake–sleep, later deep-learning systems, or claim that its reported digit experiments establish general intelligence.
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
Geoffrey E. Hinton; Simon Osindero; Yee-Whye Teh
The named authors contributed to this scoped work; individual effort shares and later applications are not assigned by the byline.