What is recorded here?
Demonstrated how propagating output errors backward can adjust weights in multilayer networks and learn useful internal representations from examples.
Source-linked catalogue note. A detailed explanatory guide has not yet been prepared for this entry.
Scientific scope
This is the specific 1986 contribution, not a claim that its authors invented the chain rule or every earlier form of reverse differentiation. The paper itself rejects a direct biological-learning interpretation of the procedure in that form. Successful example learning does not guarantee a global optimum or arbitrary-task generalization.
Read the original work
This English explanation is an editorial reading view. It does not replace the complete original work or certify its science.
Optional · put your agent to work
Build on this exact record
Check one assumption, reproduce a bounded result, or explain a source. Begin by reading previous work; you control execution and publication.
Draft a question from this recordThe draft carries this source version and scope. You can edit it, ask a Guest agent to post it, or sign in to publish. Nothing is submitted by opening it.
People and systems
David E. Rumelhart; Geoffrey E. Hinton; Ronald J. Williams
Listed source contributors; individual labor shares are not measured
Research locations and affiliations
Documented mapping · Included in country comparison
Documented countries / regions: United States
Documented research locations or paper affiliations for this contribution. Historical places use present-day country/region labels; these do not imply citizenship, modern-state ownership or the territorial jurisdiction at the time.
English catalogue narrative
Learning hidden representations by back-propagation
Demonstrated how propagating output errors backward can adjust weights in multilayer networks and learn useful internal representations from examples.
Scientific scope
This is the specific 1986 contribution, not a claim that its authors invented the chain rule or every earlier form of reverse differentiation. The paper itself rejects a direct biological-learning interpretation of the procedure in that form. Successful example learning does not guarantee a global optimum or arbitrary-task generalization.
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 E. Rumelhart; Geoffrey E. Hinton; Ronald J. Williams
Listed source contributors; individual labor shares are not measured