Σύγκριση μεθόδων

	Μονάδα Επαναλαμβανόμενης Πύλης (GRU)	Μοντέλο Ακολουθίας προς Ακολουθία
Πεδίο	Βαθιά Μάθηση	Βαθιά Μάθηση
Οικογένεια	Machine learning	Machine learning
Έτος προέλευσης	2014	2014
Δημιουργός	Cho, K. et al.	Sutskever, I.; Cho, K.
Τύπος	Gated recurrent neural network unit	Encoder-decoder neural network (deep learning)
Θεμελιώδης πηγή	Cho, K. et al. (2014). Learning Phrase Representations using RNN Encoder–Decoder for Statistical Machine Translation. EMNLP.	Sutskever, I., Vinyals, O. & Le, Q. V. (2014). Sequence to Sequence Learning with Neural Networks. NeurIPS.
Εναλλακτικές ονομασίες	Kapılı Tekrarlayan Birim (GRU), gated recurrent unit, gated recurrent network	Dizi-Dizi Modeli (Seq2Seq — Encoder-Decoder), encoder-decoder model, seq2seq, sequence to sequence learning
Συναφείς	5	5
Σύνοψη	The Gated Recurrent Unit (GRU) is a gated recurrent neural network cell introduced by Cho and colleagues in 2014 that captures long-range dependencies in sequential data using update and reset gates, achieving performance comparable to LSTM with fewer parameters.	The sequence-to-sequence (Seq2Seq) model, introduced by Sutskever, Vinyals and Le and by Cho and colleagues in 2014, is an encoder-decoder neural network that maps a variable-length input sequence to a variable-length output sequence. It is the foundation of machine translation, text summarization, dialogue systems and code generation.