Порівняння методів

	Двонаправлений рекурентний нейронний мережевий модуль (Bidirectional RNN)	Механізм уваги	Випадковий ліс
Галузь	Глибоке навчання	Глибоке навчання	Машинне навчання
Родина	Machine learning	Machine learning	Machine learning
Рік появи	1997	2015	2001
Автор методу	Schuster, M. & Paliwal, K.K.	Bahdanau, D.; Luong, M.T.	Breiman, L.
Тип	Recurrent neural network (sequence model)	Neural attention layer (encoder-decoder)	Ensemble (bagging of decision trees)
Основоположне джерело	Schuster, M. & Paliwal, K.K. (1997). Bidirectional Recurrent Neural Networks. IEEE Transactions on Signal Processing, 45(11), 2673–2681.	Bahdanau, D., Cho, K. & Bengio, Y. (2015). Neural Machine Translation by Jointly Learning to Align and Translate. ICLR.	Breiman, L. (2001). Random Forests. Machine Learning, 45, 5–32.
Інші назви	Çift Yönlü RNN / BiLSTM / BiGRU, bidirectional recurrent neural network, BiLSTM, BiGRU	Dikkat Mekanizması (Bahdanau / Luong Attention), dikkat mekanizmasi, neural attention, additive attention	Rastgele Orman (Random Forest), rastgele orman, random decision forest, bagged tree ensemble
Пов'язані	5	5	4
Підсумок	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 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.	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.