Journal
ARTIFICIAL INTELLIGENCE REVIEW
Volume 53, Issue 8, Pages 5929-5955Publisher
SPRINGER
DOI: 10.1007/s10462-020-09838-1
Keywords
Recurrent neural networks; Vanishing; exploding gradient; Long short-term memory; Deep learning
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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.
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