مقایسهٔ روش‌ها

	شبکه عصبی کانولوشنی گشادشده (Dilated CNN)	واحد بازگشتی دروازه‌ای (GRU)	جنگل تصادفی	مدل توالی به توالی
حوزه	یادگیری عمیق	یادگیری عمیق	یادگیری ماشین	یادگیری عمیق
خانواده	Machine learning	Machine learning	Machine learning	Machine learning
سال پیدایش	2016	2014	2001	2014
پدیدآور	van den Oord, A. et al.; Bai, S., Kolter, J.Z. & Koltun, V.	Cho, K. et al.	Breiman, L.	Sutskever, I.; Cho, K.
نوع	Deep learning (dilated 1D convolutional network)	Gated recurrent neural network unit	Ensemble (bagging of decision trees)	Encoder-decoder neural network (deep learning)
منبع بنیادین	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.	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.
نام‌های دیگر	Dilate Edilmiş CNN (WaveNet / TCN), WaveNet, Temporal Convolutional Network, TCN	Kapılı Tekrarlayan Birim (GRU), gated recurrent unit, gated recurrent network	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
مرتبط	5	5	4	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.	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.	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.