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Artificial Intelligence Review
Erschienen in: Artificial Intelligence Review 8/2020

13.05.2020

A review on the long short-term memory model

verfasst von: Greg Van Houdt, Carlos Mosquera, Gonzalo Nápoles

Erschienen in: Artificial Intelligence Review | Ausgabe 8/2020

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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.
Vorheriger Artikel Intelligent modelling to predict heat transfer coefficient of vacuum glass insulation based on thinking evolutionary neural network
Nächster Artikel Multi-label classification and knowledge extraction from oncology-related content on online social networks

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Metadaten
Titel
A review on the long short-term memory model
verfasst von
Greg Van Houdt
Carlos Mosquera
Gonzalo Nápoles
Publikationsdatum
13.05.2020
Verlag
Springer Netherlands
Erschienen in
Artificial Intelligence Review / Ausgabe 8/2020
Print ISSN: 0269-2821
Elektronische ISSN: 1573-7462
DOI
https://doi.org/10.1007/s10462-020-09838-1

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