Hochreiter & Schmidhuber introduce Long Short-Term Memory (LSTM)
LSTM introduced gated memory cells that let recurrent neural networks learn long-range dependencies, solving the vanishing-gradient problem that crippled earlier RNNs.
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.
Changelog
- 2026-09-29: created
Related events
- Sequence-to-sequence learning and neural attention ★★★★
- 'Attention Is All You Need' introduces the Transformer ★★★★★
Sources (2)
id: 1997-11-15-lstm · updated 2026-09-29 · open in the interactive timeline