方法对比

	门控循环单元 (GRU)	注意力机制	随机森林	序列到序列模型
领域	深度学习	深度学习	机器学习	深度学习
方法族	Machine learning	Machine learning	Machine learning	Machine learning
起源年份	2014	2015	2001	2014
提出者	Cho, K. et al.	Bahdanau, D.; Luong, M.T.	Breiman, L.	Sutskever, I.; Cho, K.
类型	Gated recurrent neural network unit	Neural attention layer (encoder-decoder)	Ensemble (bagging of decision trees)	Encoder-decoder neural network (deep learning)
开创性文献	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.	Breiman, L. (2001). Random Forests. Machine Learning, 45, 5–32.	Sutskever, I., Vinyals, O. & Le, Q. V. (2014). Sequence to Sequence Learning with Neural Networks. NeurIPS.
别名	Kapılı Tekrarlayan Birim (GRU), gated recurrent unit, gated recurrent network	Dikkat Mekanizması (Bahdanau / Luong Attention), dikkat mekanizmasi, neural attention, additive attention	Rastgele Orman (Random Forest), rastgele orman, random decision forest, bagged tree ensemble	Dizi-Dizi Modeli (Seq2Seq — Encoder-Decoder), encoder-decoder model, seq2seq, sequence to sequence learning
相关	5	5	4	5
摘要	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.	Random Forest is an ensemble learning method, introduced by Leo Breiman in 2001, that grows many decision trees on bootstrap samples of the data and combines their votes to produce strong classification and regression. By pooling many slightly different trees, it produces more accurate and more stable predictions than any single tree.	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.