What is recorded here?
Presented dropout as stochastic removal of units and connections during training, with a single-network approximation to model averaging at test time, and evaluated it across several supervised-learning benchmarks.
Source-linked catalogue note. A detailed explanatory guide has not yet been prepared for this entry.
Scientific scope
The record is the exact 2014 JMLR five-author article. Earlier unpublished use, DropConnect, batch normalization, Bayesian interpretations and claims that dropout always improves generalization remain separate.
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People and systems
Nitish Srivastava; Geoffrey Hinton; Alex Krizhevsky; Ilya Sutskever; Ruslan Salakhutdinov
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
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English catalogue narrative
Srivastava and colleagues: dropout for neural-network regularization
Presented dropout as stochastic removal of units and connections during training, with a single-network approximation to model averaging at test time, and evaluated it across several supervised-learning benchmarks.
Scientific scope
The record is the exact 2014 JMLR five-author article. Earlier unpublished use, DropConnect, batch normalization, Bayesian interpretations and claims that dropout always improves generalization remain separate.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
Nitish Srivastava; Geoffrey Hinton; Alex Krizhevsky; Ilya Sutskever; Ruslan Salakhutdinov
The named authors contributed to this scoped work; individual effort shares and later applications are not assigned by the byline.