Learning Representations by Back-propagating Errors¶
Rumelhart, D. E., Hinton, G. E., & Williams, R. J. (1986). Learning Representations by Back-propagating Errors. Nature, 323(6088), 533-536.
Cited by¶
2 citations across 2 artifacts.
Each citation links to the sentence it supports in the citing article.
Primes¶
- Gradient
- Higher Order Function
- In legal theory this maps to the hierarchy of norms; in machine learning, to the chain rule and backpropagation.
This sourceBackpropagation as repeated application of the chain rule — composition over differentiable rules.
Supported in partVerified against the publisher's abstract
This paper's abstract introduces back-propagation as a machine-learning procedure, but it does not itself state the chain rule and says nothing about legal theory.
“We describe a new learning procedure, back-propagation, for networks of neurone-like units. The procedure repeatedly adjusts the weights of the connections in the network so as to minimize a measure of the difference between the actual output vector of the net and the desired output vector.”
- In legal theory this maps to the hierarchy of norms; in machine learning, to the chain rule and backpropagation.
Verification¶
Does it exist? Confirmed. This work's DOI resolves to a registered record, which fixes its identity. That is all it fixes.
Does it back the claim? Read against the text for 1 of 2 citations: 1 supported in part. Each verdict is shown under its citation below, with what in the work backs the sentence.
Support is checked per citation rather than per work — the same source can be cited soundly in one article and wrongly in another. Per-citation recording began recently, so a citation with no recorded check is a gap in the record rather than evidence it went unchecked.
See how references were verified.
Registry ID ref:6f36c808f0f6 · see in the full table