手法を比較

	シーケンス・ツー・シーケンスモデル	ランダムフォレスト	Multi-Head Self-Attention
分野	深層学習	機械学習	深層学習
系統	Machine learning	Machine learning	Machine learning
提唱年	2014	2001	2017
提唱者	Sutskever, I.; Cho, K.	Breiman, L.	Vaswani, A. et al.
種類	Encoder-decoder neural network (deep learning)	Ensemble (bagging of decision trees)	Attention mechanism (Transformer core)
原典	Sutskever, I., Vinyals, O. & Le, Q. V. (2014). Sequence to Sequence Learning with Neural Networks. NeurIPS.	Breiman, L. (2001). Random Forests. Machine Learning, 45, 5–32.	Vaswani, A. et al. (2017). Attention Is All You Need. NeurIPS.
別名	Dizi-Dizi Modeli (Seq2Seq — Encoder-Decoder), encoder-decoder model, seq2seq, sequence to sequence learning	Rastgele Orman (Random Forest), rastgele orman, random decision forest, bagged tree ensemble	Öz-Dikkat ve Çok Başlı Dikkat (Multi-Head Self-Attention), öz-dikkat, multi-head attention, scaled dot-product attention
関連	5	4	5
概要	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.	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.	Multi-head self-attention, introduced by Vaswani and colleagues in 2017, is the mechanism that lets every position in a sequence compute its relationship to all other positions in parallel. It is the core of the Transformer architecture and the foundation underneath BERT, GPT, and T5.