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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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A_review_on_the_Long_short_term_memory_model.pdf | Peer-reviewed author version | 351 kB | Adobe PDF | View/Open |
VanHoudt2020_Article_AReviewOnTheLongShort-termMemo.pdf Restricted Access | Published version | 506.22 kB | Adobe PDF | View/Open Request a copy |
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