手法を比較

	ゲート付き再帰ユニット (GRU)	アテンションメカニズム
分野	深層学習	深層学習
系統	Machine learning	Machine learning
提唱年	2014	2015
提唱者	Cho, K. et al.	Bahdanau, D.; Luong, M.T.
種類	Gated recurrent neural network unit	Neural attention layer (encoder-decoder)
原典	Cho, K. et al. (2014). Learning Phrase Representations using RNN Encoder–Decoder for Statistical Machine Translation. EMNLP.	Bahdanau, D., Cho, K. & Bengio, Y. (2015). Neural Machine Translation by Jointly Learning to Align and Translate. ICLR.
別名	Kapılı Tekrarlayan Birim (GRU), gated recurrent unit, gated recurrent network	Dikkat Mekanizması (Bahdanau / Luong Attention), dikkat mekanizmasi, neural attention, additive attention
関連	5	5
概要	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 attention mechanism, introduced by Bahdanau, Cho and Bengio in 2015 and refined by Luong, Pham and Manning the same year, lets a sequence decoder dynamically learn which of the encoder's outputs to focus on at each step. Before the Transformer, it substantially improved machine-translation quality by freeing models from compressing an entire input into a single fixed vector.