手法を比較

	Dilated CNN	ランダムフォレスト	シーケンス・ツー・シーケンスモデル
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	2016	2001	2014
提唱者	van den Oord, A. et al.; Bai, S., Kolter, J.Z. & Koltun, V.	Breiman, L.	Sutskever, I.; Cho, K.
種類	Deep learning (dilated 1D convolutional network)	Ensemble (bagging of decision trees)	Encoder-decoder neural network (deep learning)
原典	van den Oord, A. et al. (2016). WaveNet: A Generative Model for Raw Audio. arXiv.	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.
別名	Dilate Edilmiş CNN (WaveNet / TCN), WaveNet, Temporal Convolutional Network, TCN	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
関連	5	4	5
概要	A Dilated CNN is a one-dimensional convolutional network whose receptive field grows exponentially with depth, letting it model long-range structure in time series and audio signals. WaveNet (van den Oord et al., 2016) and the Temporal Convolutional Network of Bai, Kolter and Koltun (2018) are the prominent members of this family.	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.