Machine learningDeep learning / NLP / CV
Recurrent Neural Network
A Recurrent Neural Network (RNN) is a class of neural network designed to process sequential data by maintaining a hidden state that carries information across time steps. Introduced in its modern form by Rumelhart et al. (1986) and further shaped by Elman (1990), RNNs became the dominant architecture for sequence modelling in NLP, speech, and time-series analysis before the rise of attention-based models.
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Sources
- Elman, J. L. (1990). Finding structure in time. Cognitive Science, 14(2), 179–211. DOI: 10.1207/s15516709cog1402_1 ↗
- Rumelhart, D. E., Hinton, G. E., & Williams, R. J. (1986). Learning representations by back-propagating errors. Nature, 323(6088), 533–536. DOI: 10.1038/323533a0 ↗
Related methods
Referenced by
BERT-based ClassificationDeep Reinforcement LearningDomain-adaptive Recurrent Neural NetworkEcho State NetworkExplainable Recurrent Neural NetworkFine-Tuned GRUFine-Tuned Recurrent Neural NetworkFine-Tuned Word2VecGated Recurrent UnitGraph Attention NetworkLong Short-Term MemoryMulti-layer PerceptronMultilayer PerceptronMultilingual Recurrent Neural NetworkMultimodal Recurrent Neural NetworkNeural ODEReinforcement LearningSelf-supervised Word2VecTopic ModelingTransfer Learning with Recurrent Neural NetworkTransfer Learning with Word2VecWavelet Neural NetworkWeakly Supervised GRUWeakly supervised LSTMWeakly supervised recurrent neural network