LSTM
1997Recurrent unit with gated memory cells (Hochreiter & Schmidhuber, "Long Short-Term Memory," Neural Computation 9(8), 1997) that solved the vanishing-gradient problem in training recurrent networks over long sequences, making deep sequence models practical for the first time.
Originators
- Hochreiter, S.
- Schmidhuber, J.
Landmark Paper
Checked 2026-09-19 — interim signal only, see docs/BASIC_ROADMAP.md Phase 10
Connections
- is component of Sequence to Sequence Learningbasis: reasoned
Sutskever, Vinyals & Le's Seq2Seq (ref [35]) uses multilayered LSTM cells as the encoder and decoder; LSTM is the specific recurrent unit the framework is built from.
- historically preceded Mambabasis: reasoned
Appendix A of the Mamba paper: "Gating originally referred to the gating mechanisms of RNNs such as the LSTM (Hochreiter and Schmidhuber 1997)... Theorem 1 is an improvement of [LSSL]... generalizing to the ZOH discretization and input-dependent gates." Mamba's own Theorem 1 shows its selection mechanism recovers a gated-RNN recurrence as a special case, with LSTM named as the specific example of classical RNN gating.