مقایسهٔ روش‌ها

	واحد بازگشتی دروازه‌ای (GRU)	سازوکار توجه	شبکه عصبی بازگشتی دوطرفه (Bidirectional RNN)	جنگل تصادفی
حوزه	یادگیری عمیق	یادگیری عمیق	یادگیری عمیق	یادگیری ماشین
خانواده	Machine learning	Machine learning	Machine learning	Machine learning
سال پیدایش	2014	2015	1997	2001
پدیدآور	Cho, K. et al.	Bahdanau, D.; Luong, M.T.	Schuster, M. & Paliwal, K.K.	Breiman, L.
نوع	Gated recurrent neural network unit	Neural attention layer (encoder-decoder)	Recurrent neural network (sequence model)	Ensemble (bagging of decision trees)
منبع بنیادین	Cho, K. et al. (2014). Learning Phrase Representations using RNN Encoder–Decoder for Statistical Machine Translation. EMNLP.	Bahdanau, D., Cho, K. & Bengio, Y. (2015). Neural Machine Translation by Jointly Learning to Align and Translate. ICLR.	Schuster, M. & Paliwal, K.K. (1997). Bidirectional Recurrent Neural Networks. IEEE Transactions on Signal Processing, 45(11), 2673–2681.	Breiman, L. (2001). Random Forests. Machine Learning, 45, 5–32.
نام‌های دیگر	Kapılı Tekrarlayan Birim (GRU), gated recurrent unit, gated recurrent network	Dikkat Mekanizması (Bahdanau / Luong Attention), dikkat mekanizmasi, neural attention, additive attention	Çift Yönlü RNN / BiLSTM / BiGRU, bidirectional recurrent neural network, BiLSTM, BiGRU	Rastgele Orman (Random Forest), rastgele orman, random decision forest, bagged tree ensemble
مرتبط	5	5	5	4
خلاصه	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 attention mechanism, introduced by Bahdanau, Cho and Bengio in 2015 and refined by Luong, Pham and Manning the same year, lets a sequence decoder dynamically learn which of the encoder's outputs to focus on at each step. Before the Transformer, it substantially improved machine-translation quality by freeing models from compressing an entire input into a single fixed vector.	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.	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.