Σύγκριση μεθόδων

	LSTM	Μετασχηματιστής (Επεξεργασία Φυσικής Γλώσσας)
Πεδίο	Βαθιά Μάθηση	Βαθιά Μάθηση
Οικογένεια	Machine learning	Machine learning
Έτος προέλευσης	1997	2017
Δημιουργός	Hochreiter, S. & Schmidhuber, J.	Vaswani, A. et al.
Τύπος	Recurrent neural network (gated memory cell)	Attention-based deep neural network
Θεμελιώδης πηγή	Hochreiter, S. & Schmidhuber, J. (1997). Long Short-Term Memory. Neural Computation, 9(8), 1735–1780.	Vaswani, A. et al. (2017). Attention Is All You Need. NeurIPS.
Εναλλακτικές ονομασίες	LSTM (Uzun Kısa Dönem Bellek Ağı), long short-term memory, LSTM network, recurrent neural network with memory cells	Transformer Modeli (NLP), attention-based language model, self-attention network, transformer NLP
Συναφείς	5	4
Σύνοψη	LSTM (Long Short-Term Memory) is a recurrent neural network architecture, introduced by Sepp Hochreiter and Jürgen Schmidhuber in 1997, that can learn long-term dependencies in sequential data and is widely used for time-series and sequence prediction. It keeps an internal memory that lets information persist across many time steps.	The Transformer is an attention-based deep learning model, introduced by Vaswani and colleagues in 2017, that performs text classification, named-entity recognition, and language modelling by letting every token in a sequence attend directly to every other token. It replaced earlier recurrent designs with a self-attention mechanism that processes whole sequences in parallel.