Machine learningDeep learning / NLP / CV
Long Short-Term Memory (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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Sources
- Hochreiter, S. & Schmidhuber, J. (1997). Long short-term memory. Neural Computation, 9(8), 1735–1780. DOI: 10.1162/neco.1997.9.8.1735 ↗
- Graves, A., Mohamed, A.-R. & Hinton, G. (2013). Speech recognition with deep recurrent neural networks. Proceedings of ICASSP 2013, pp. 6645–6649. IEEE. DOI: 10.1109/ICASSP.2013.6638947 ↗
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BERT-based ClassificationDomain-adaptive Recurrent Neural NetworkExplainable GRUExplainable LSTMExplainable Recurrent Neural NetworkFine-Tuned GRUFine-Tuned LSTMFine-Tuned Recurrent Neural NetworkGated Recurrent UnitMultilingual LSTMMultilingual Recurrent Neural NetworkMultimodal GRUMultimodal Recurrent Neural NetworkRecurrent Neural NetworkRoBERTa-based ClassificationSelf-supervised GRUSemi-supervised GRUSentence EmbeddingsTransfer Learning with LSTMTransfer Learning with Recurrent Neural NetworkWeakly Supervised GRUWeakly supervised LSTMWeakly supervised recurrent neural network