Claim · 2014-09-10 · publication · Stated in source · Supported

The paper presents a general end-to-end approach to sequence learning that, according to the authors, makes minimal assumptions on the sequence structure.

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What it connects

Sequence to Sequence Learning with Neural Networks → states its contribution → The paper presents a general end-to-end approach to sequence learning that, according to the authors, makes minimal assumptions on the sequence structure.

Where it appears

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Claim and evidence

Sequence to Sequence Learning with Neural Networksstates its contribution ↓The paper presents a general end-to-end approach to sequence learning that, according to the authors, makes minimal assumptions on the sequence structure.

Machine-checked, not reviewed by an editorStated in sourceSupported

Supporting evidence: primary · 1 source

Date: 2014-09-10 · 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

Sequence to Sequence Learning with Neural Networks

arXiv · 2014-09-10

Cite the original source

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Source excerpt

“In this paper, we present a general end-to-end approach to sequence learning that makes minimal assumptions on the sequence structure.”

Location in source arXiv v1, PDF p. 1, abstract

Machine-checked · 2026-10-31

Source details and check notes

Authors: Ilya Sutskever; Oriol Vinyals; Quoc V. Le
Version: arXiv:1409.3215v1, submitted 10 September 2014 (UTC). Retrieved for the candidate pack on 2026-10-28 (SHA-256 ca63cbfdacb6788f426cc26cae14b5f670378c2cce53f38e57ef2ec9336e7348). The author list is the byline as printed on the first page.
Version date: 2014-09-10
Canonical address: Open the canonical source ↗
Accessed: 2026-10-28
Language: English (en)
Attribution: Ilya Sutskever, Oriol Vinyals, Quoc V. Le, “Sequence to Sequence Learning with Neural Networks”, 2014-09-10.
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 (20 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. 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 arXiv version quoted, submitted 10 September 2014 (UTC); later versions may word it differently.

Record ID kp-states-sequence-sequence-learning-neural-networks-2014-1