Keywords
Recurrent neural networkComputer scienceInferenceArtificial intelligenceNotationMachine learningArtificial neural networkArithmetic
Related Publications
A Review of Recurrent Neural Networks: LSTM Cells and Network Architectures
Recurrent neural networks (RNNs) have been widely adopted in research areas concerned with sequential data, such as text, audio, and video. However, RNNs consisting of sigma cel...
Long Short-Term Memory
Learning to store information over extended time intervals by recurrent backpropagation takes a very long time, mostly because of insufficient, decaying error backflow. We brief...
Training Very Deep Networks
Theoretical and empirical evidence indicates that the depth of neural networks is crucial for their success. However, training becomes more difficult as depth increases, and tra...
Informer: Beyond Efficient Transformer for Long Sequence Time-Series Forecasting
Many real-world applications require the prediction of long sequence time-series, such as electricity consumption planning. Long sequence time-series forecasting (LSTF) demands ...
Generalization of Back propagation to Recurrent and Higher Order Neural Networks
A general method for deriving backpropagation algorithms for networks with recurrent and higher order networks is introduced. The propagation of activation in these networks is ...
Publication Info
- Year
- 2020
- Type
- article
- Volume
- 404
- Pages
- 132306-132306
- Citations
- 4806
- Access
- Closed
External Links
Social Impact
Altmetric
PlumX Metrics
Social media, news, blog, policy document mentions
Citation Metrics
4806
OpenAlex
Cite This
A. Sherstinsky
(2020).
Fundamentals of Recurrent Neural Network (RNN) and Long Short-Term Memory (LSTM) network.
Physica D Nonlinear Phenomena
, 404
, 132306-132306.
https://doi.org/10.1016/j.physd.2019.132306
Identifiers
- DOI
- 10.1016/j.physd.2019.132306