مقایسهٔ روش‌ها

	شبکه عصبی کانولوشنی گشادشده (Dilated CNN)	شبکه عصبی بازگشتی دوطرفه (Bidirectional RNN)	واحد بازگشتی دروازه‌ای (GRU)	مدل توالی به توالی
حوزه	یادگیری عمیق	یادگیری عمیق	یادگیری عمیق	یادگیری عمیق
خانواده	Machine learning	Machine learning	Machine learning	Machine learning
سال پیدایش	2016	1997	2014	2014
پدیدآور	van den Oord, A. et al.; Bai, S., Kolter, J.Z. & Koltun, V.	Schuster, M. & Paliwal, K.K.	Cho, K. et al.	Sutskever, I.; Cho, K.
نوع	Deep learning (dilated 1D convolutional network)	Recurrent neural network (sequence model)	Gated recurrent neural network unit	Encoder-decoder neural network (deep learning)
منبع بنیادین	van den Oord, A. et al. (2016). WaveNet: A Generative Model for Raw Audio. arXiv.	Schuster, M. & Paliwal, K.K. (1997). Bidirectional Recurrent Neural Networks. IEEE Transactions on Signal Processing, 45(11), 2673–2681.	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.
نام‌های دیگر	Dilate Edilmiş CNN (WaveNet / TCN), WaveNet, Temporal Convolutional Network, TCN	Çift Yönlü RNN / BiLSTM / BiGRU, bidirectional recurrent neural network, BiLSTM, BiGRU	Kapılı Tekrarlayan Birim (GRU), gated recurrent unit, gated recurrent network	Dizi-Dizi Modeli (Seq2Seq — Encoder-Decoder), encoder-decoder model, seq2seq, sequence to sequence learning
مرتبط	5	5	5	5
خلاصه	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.	A Bidirectional RNN, introduced by Schuster and Paliwal in 1997, processes a sequence in both forward and backward directions so that every position has access to its full surrounding context. With LSTM or GRU cells (BiLSTM/BiGRU) it is the standard approach for named-entity recognition, sequence labelling, and speech recognition.	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.