Salīdzināt metodes

	Daudzvalodu rekurentā neironu tīkls	Gated Recurrent Unit (GRU)
Nozare	Dziļā mācīšanās	Dziļā mācīšanās
Saime	Machine learning	Machine learning
Izcelsmes gads	1990–2010s	2014
Autors	Elman, J. L. (RNN); multilingual extension by NLP community	Cho, K., van Merrienboer, B., Gulcehre, C., Bahdanau, D., Bougares, F., Schwenk, H., & Bengio, Y.
Tips	Sequential model (cross-lingual)	Recurrent neural network with gating
Pirmavots	Elman, J. L. (1990). Finding structure in time. Cognitive Science, 14(2), 179–211.	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.
Citi nosaukumi	Multilingual RNN, Cross-lingual RNN, Multi-language RNN, MRNN	GRU, GRU network, gated RNN, GRU cell
Saistītās	5	3
Kopsavilkums	A Multilingual Recurrent Neural Network (Multilingual RNN) applies the standard RNN architecture — which processes sequences step by step while maintaining a hidden state — to data spanning two or more languages. By training on multilingual corpora or sharing parameters across languages, the model learns cross-lingual sequence representations useful for translation, tagging, classification, and language modeling tasks.	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.