方法对比

	Dilated CNN	门控循环单元 (GRU)	序列到序列模型
领域	深度学习	深度学习	深度学习
方法族	Machine learning	Machine learning	Machine learning
起源年份	2016	2014	2014
提出者	van den Oord, A. et al.; Bai, S., Kolter, J.Z. & Koltun, V.	Cho, K. et al.	Sutskever, I.; Cho, K.
类型	Deep learning (dilated 1D convolutional network)	Gated recurrent neural network unit	Encoder-decoder neural network (deep learning)
开创性文献	van den Oord, A. et al. (2016). WaveNet: A Generative Model for Raw Audio. arXiv.	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.
别名	Dilate Edilmiş CNN (WaveNet / TCN), WaveNet, Temporal Convolutional Network, TCN	Kapılı Tekrarlayan Birim (GRU), gated recurrent unit, gated recurrent network	Dizi-Dizi Modeli (Seq2Seq — Encoder-Decoder), encoder-decoder model, seq2seq, sequence to sequence learning
相关	5	5	5
摘要	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.	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.