Please use this identifier to cite or link to this item: http://hdl.handle.net/1942/31139
Title: A review on the long short-term memory model
Authors: VAN HOUDT, Greg 
Mosquera, Carlos
NAPOLES RUIZ, Gonzalo 
Issue Date: 2020
Publisher: Springer Nature
Source: ARTIFICIAL INTELLIGENCE REVIEW, 53, p. 5929-5955
Abstract: Long Short-Term Memory (LSTM) has transformed both machine learning and neurocomputing fields. According to several online sources, this model has improved Google's speech recognition, greatly improved machine translations on Google Translate, and the answers of Amazon's Alexa. This neural system is also employed by Facebook, reaching over 4 billion LSTM-based translations per day as of 2017. Interestingly, recurrent neural networks had shown a rather discrete performance until LSTM showed up. One reason for the success of this recurrent network lies in its ability to handle the exploding / vanishing gradient problem, which stands as a difficult issue to be circumvented when training recurrent or very deep neural networks. In this paper, we present a comprehensive review that covers LSTM's formulation and training, relevant applications reported in the literature and code resources implementing this model for a toy example.
Keywords: Recurrent neural networks;Vanishing/exploding gradient;Long short-term memory;Deep learning
Document URI: http://hdl.handle.net/1942/31139
ISSN: 0269-2821
e-ISSN: 1573-7462
DOI: 10.1007/s10462-020-09838-1
ISI #: WOS:000532798400001
Rights: © Springer Nature B.V. 2020
Category: A1
Type: Journal Contribution
Validations: ecoom 2021
Appears in Collections:Research publications

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