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| Unità Ricorrente con Gate (GRU)× | Long Short-Term Memory (LSTM)× | |
|---|---|---|
| Campo | Apprendimento profondo | Apprendimento profondo |
| Famiglia | Machine learning | Machine learning |
| Anno di origine≠ | 2014 | 1997 |
| Ideatore≠ | Cho, K., van Merrienboer, B., Gulcehre, C., Bahdanau, D., Bougares, F., Schwenk, H., & Bengio, Y. | Hochreiter, S. & Schmidhuber, J. |
| Tipo≠ | Recurrent neural network with gating | Recurrent neural network with gated memory cells |
| Fonte seminale≠ | Cho, K., van Merrienboer, B., Gulcehre, C., Bahdanau, D., Bougares, F., Schwenk, H., & Bengio, Y. (2014). Learning Phrase Representations using RNN Encoder-Decoder for Statistical Machine Translation. In Proceedings of EMNLP 2014, pp. 1724–1734. link ↗ | Hochreiter, S. & Schmidhuber, J. (1997). Long short-term memory. Neural Computation, 9(8), 1735–1780. DOI ↗ |
| Alias | GRU, GRU network, gated RNN, GRU cell | LSTM, LSTM network, LSTM-RNN, long short-term memory RNN |
| Correlati≠ | 3 | 4 |
| Sintesi≠ | The Gated Recurrent Unit (GRU), introduced by Cho et al. in 2014, is a streamlined recurrent neural network that uses two learned gates — an update gate and a reset gate — to selectively retain or discard information across time steps, enabling effective sequence modelling with fewer parameters than LSTM. | Long Short-Term Memory (LSTM) is a gated recurrent neural network architecture introduced by Hochreiter and Schmidhuber in 1997. It was designed to learn dependencies across long sequences by using dedicated memory cells and three learned gates — forget, input, and output — that control what information is retained, updated, or passed forward at each time step. |
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