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A recurrent neural network design with gating that can remember information over long sequences — the workhorse of sequence modelling before Transformers.
Hochreiter & Schmidhuber (1997) introduced the LSTM to solve the vanishing-gradient problem that prevented ordinary recurrent networks from learning long-range dependencies. Its gated memory cell lets information persist or be forgotten in a controlled way across many time steps.
LSTMs dominated speech, translation and text modelling for nearly two decades, until self-attention and the Transformer largely replaced them.
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