Σύγκριση μεθόδων

	Αμφίδρομο RNN	Αυτο-προσοχή πολλαπλών κεφαλών	Μοντέλο Ακολουθίας προς Ακολουθία
Πεδίο	Βαθιά Μάθηση	Βαθιά Μάθηση	Βαθιά Μάθηση
Οικογένεια	Machine learning	Machine learning	Machine learning
Έτος προέλευσης	1997	2017	2014
Δημιουργός	Schuster, M. & Paliwal, K.K.	Vaswani, A. et al.	Sutskever, I.; Cho, K.
Τύπος	Recurrent neural network (sequence model)	Attention mechanism (Transformer core)	Encoder-decoder neural network (deep learning)
Θεμελιώδης πηγή	Schuster, M. & Paliwal, K.K. (1997). Bidirectional Recurrent Neural Networks. IEEE Transactions on Signal Processing, 45(11), 2673–2681.	Vaswani, A. et al. (2017). Attention Is All You Need. NeurIPS.	Sutskever, I., Vinyals, O. & Le, Q. V. (2014). Sequence to Sequence Learning with Neural Networks. NeurIPS.
Εναλλακτικές ονομασίες	Çift Yönlü RNN / BiLSTM / BiGRU, bidirectional recurrent neural network, BiLSTM, BiGRU	Öz-Dikkat ve Çok Başlı Dikkat (Multi-Head Self-Attention), öz-dikkat, multi-head attention, scaled dot-product attention	Dizi-Dizi Modeli (Seq2Seq — Encoder-Decoder), encoder-decoder model, seq2seq, sequence to sequence learning
Συναφείς	5	5	5
Σύνοψη	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.	Multi-head self-attention, introduced by Vaswani and colleagues in 2017, is the mechanism that lets every position in a sequence compute its relationship to all other positions in parallel. It is the core of the Transformer architecture and the foundation underneath BERT, GPT, and T5.	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.