Hochreiter & Schmidhuber introduce Long Short-Term Memory (LSTM)
Confirmed
Importance: major (4 of 5)The takeaway
LSTM introduced gated memory cells that let recurrent neural networks learn long-range dependencies, solving the vanishing-gradient problem that crippled earlier RNNs.
Status
- Claim
Confirmed
- Our reporting
- High confidence
- Importance
- Major (4 of 5)
- Last verified
- 29 September 2026
Your AI and this story
- GPT-6 AstraIn its training data
- Claude Opus 5.5In its training data
- Gemini 4 HydrogenIn its training data
- Grok 4.7In its training data
It happened before the cutoffs of all four assistants, so it can be in their training data.
Key facts
- Published in Neural Computation 9(8), November 1997
- Authors: Sepp Hochreiter and Jürgen Schmidhuber
- Forget gates were added later (Gers et al., 2000)
- Powered speech recognition and machine translation systems in the 2010s
What happened
The paper proposed a recurrent architecture with a constant-error carousel and multiplicative gates controlling information flow.
Why it matters
LSTMs dominated sequence modeling (speech, translation, handwriting) until the Transformer, and underpinned early seq2seq systems like Google Translate’s 2016 neural system.
Sources
2 sources from 2 sites. Numbers match the chips in the text.
2 sources: 1 primary, 1 reaction
Primary
- Long Short-Term Memory (Neural Computation, DOI)doi.org, paper
Reactions
- Wikipedia: Long short-term memoryen.wikipedia.org, discussion
Changes
- Filed