Claim · 2014-06 · publication · Stated in source · Supported

The paper presents dropout, a technique against overfitting that randomly drops units and their connections from a neural network during training.

One recorded claim. The claim panel gives its qualification, the source passages with their place in each source, and how it was checked.

What it connects

Dropout: a simple way to prevent neural networks from overfitting → states its contribution → The paper presents dropout, a technique against overfitting that randomly drops units and their connections from a neural network during training.

Where it appears

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Claim and evidence

Dropout: a simple way to prevent neural networks from overfittingstates its contribution ↓The paper presents dropout, a technique against overfitting that randomly drops units and their connections from a neural network during training.

Machine-checked, not reviewed by an editorStated in sourceSupported

Supporting evidence: primary · 1 source

Date: 2014-06 · publication Records dated 2014 →

Scope and limitsThe paper's own description of its contribution; not an assessment of it.

The evidence 01

primary sourceSupports the claim

Dropout: a simple way to prevent neural networks from overfitting

Journal of Machine Learning Research · 2014-06

Cite the original source

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Source excerpt

“The key idea is to randomly drop units (along with their connections) from the neural network during training.”

Location in source PDF p. 1, abstract

Machine-checked · 2026-10-31

Source details and check notes

Authors: Nitish Srivastava; Geoffrey Hinton; Alex Krizhevsky; Ilya Sutskever; Ruslan Salakhutdinov
Version: Journal PDF; it prints “Published 6/14” on p. 1 (cross-family check, item A023). Retrieved for the candidate pack on 2026-10-28 (SHA-256 9c196ccbe6c6a595a1adba6cd030d35f7c2e548bbf5e7f1278b0109d8dd9ebaa). The author list is the byline as printed on the first page.
Canonical address: Open the canonical source ↗
Accessed: 2026-10-28
Language: English (en)
Attribution: Nitish Srivastava, Geoffrey Hinton, Alex Krizhevsky, Ilya Sutskever, Ruslan Salakhutdinov, “Dropout: a simple way to prevent neural networks from overfitting”, 2014-06.
Reuse: No open redistribution licence asserted. Bibliographic metadata, editorial paraphrases and short attributed quotations (at most 50 words each). Recheck terms before wider redistribution.
Check notes: Candidate pack passage, key papers (research/key-papers). The quotation (18 words) matched the text retrieved for the candidate pack on 2026-10-28 (research/key-papers/verification.json). Matching ignored differences of whitespace, case, quotation marks, dashes and line-break hyphens, so the source’s typography can differ. A cross-family machine check (2026-10-30, item B036) found the quotation at its locator. No human editorial review.

Status and meaning

Editorial status. Not reviewed by an editor. An exploratory record that no editor has accepted. This status does not say whether a person has read any linked passage. Each evidence record above shows how it was checked.

Relation. A paper or other publication describes its own contribution, quoted from the expression the record names, usually its abstract or introduction. The record is the document’s own description, not an assessment: a claim of priority or novelty it contains is the document’s claim, flagged as such, and says nothing about earlier or later work.

Date. Date of the version quoted, as its source gives it (June 2014); other versions may word it differently.

Record ID kp-states-dropout-simple-way-prevent-neural-2014-1