Verified claim · AI-ML · 100% confidence
Sequence-to-Sequence Learning (seq2seq) introduced in paper: Sequence to Sequence Learning with Neural Networks (Sutskever, Vinyals, Le, 2014).
Last verified 2026-05-16 · Methodology veritas-v0.1 · ff80a25ed7e83b45
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Structured fields
- Subject
- Sequence-to-Sequence Learning (seq2seq)
- Predicate
introduced_in_paper- Object
- Sequence to Sequence Learning with Neural Networks (Sutskever, Vinyals, Le, 2014)
- Confidence
- 100%
- Tags
- seq2seq · encoder-decoder · lstm · foundational · 2014 · nips · google
Sources (2)
[1] preprint · arXiv (Sutskever, Vinyals, Le) · 2014-09-10
Sequence to Sequence Learning with Neural Networks“We present a general end-to-end approach to sequence learning that makes minimal assumptions on the sequence structure. Our method uses a multilayered Long Short-Term Memory (LSTM) to map the input sequence to a vector of a fixed dimensionality, and then another deep LSTM to decode the target sequence from the vector.”
[2] docs · Wikipedia · 2014-12-08
Seq2seqWikipedia is rated by SourceScore — see its reliability →
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// "Sequence-to-Sequence Learning (seq2seq) introduced in paper: Sequence to Sequence Learning with Neural Networks (Sutskever, Vinyals, Le, 2014)."Python
import httpx
r = httpx.get("https://sourcescore.org/api/v1/claims/ff80a25ed7e83b45.json")
envelope = r.json()
print(envelope["claim"]["statement"])
# "Sequence-to-Sequence Learning (seq2seq) introduced in paper: Sequence to Sequence Learning with Neural Networks (Sutskever, Vinyals, Le, 2014)."LangChain (retrieve-then-cite)
from langchain_core.tools import tool
import httpx
@tool
def get_sequence_to_sequence_learning_seq2seq_fact() -> dict:
"""Fetch the verified SourceScore claim for Sequence-to-Sequence Learning (seq2seq)."""
r = httpx.get("https://sourcescore.org/api/v1/claims/ff80a25ed7e83b45.json")
return r.json()