1 déc. 1997 - LSTM, a key innovation in recurrent neural networks (Sepp Hochreiter et al)

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Sepp Hochreiter and Jürgen Schmidhuber introduce Long Short-Term Memory (LSTM), a groundbreaking architecture for recurrent neural networks (RNNs) designed to address the vanishing gradient problem in sequence-to-sequence learning tasks. LSTM networks incorporate memory cells and gating mechanisms that enable them to learn long-range dependencies, making them particularly effective for applications in natural language processing, speech recognition, and time series analysis. LSTMs become a widely adopted and influential RNN architecture in the field of deep learning.

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