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Biologically plausible gated recurrent neural networks for working memory and learning-to-learn. [PDF]

PLoS One
van den Berg AR, Roelfsema PR, Bohte SM.
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Flexible Recurrent Neural Networks

2021
We introduce two methods enabling recurrent neural networks (RNNs) to trade off accuracy for computational cost during the analysis of a sequence. This opens up the possibility to adapt RNNs in real time to changing computational constraints, such as when running on shared hardware with other processes or in mobile edge computing nodes.
Anne Lambert, Françoise Le Bolzer, François Schnitzler   +2 more
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Pruning in Recurrent Neural Networks

1994
Recurrent neural networks are attracting considerable interest within the neural network domain especially because of their potential in such problems as pattern completion and temporal sequence processing (Almeida, 1987; Hertz et al., 1991). As for feed-forward networks, in virtually all problems of interest the proper number of hidden units is not ...
CASTELLANO G, FANELLI A. M, PELILLO, Marcello   +2 more
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Recurrent Neural Network for Storytelling

2016 Joint 8th International Conference on Soft Computing and Intelligent Systems (SCIS) and 17th International Symposium on Advanced Intelligent Systems (ISIS), 2016
Storytelling is the act of passing on what you want to tell other people as so interesting and true-to-life story. As the study in text mining progresses to express words, sentences and paragraphs as vector, it is possible to classify text and generate text using vectors.
YunSeok Choi, Suah Kim, Jee-Hyong Lee 0001   +2 more
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IDENTIFIABILITY OF RECURRENT NEURAL NETWORKS

Econometric Theory, 2003
zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Al-Falou, A. A., Trummer, D.
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Self-Recurrent Neural Network

Substance Use & Misuse, 1998
(1998). Self-Recurrent Neural Network. Substance Use & Misuse: Vol. 33, No. 2, pp. 495-501.
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MARKOV RECURRENT NEURAL NETWORKS

2018 IEEE 28th International Workshop on Machine Learning for Signal Processing (MLSP), 2018
Deep learning has achieved great success in many real-world applications. For speech and language processing, recurrent neural networks are learned to characterize sequential patterns and extract the temporal information based on dynamic states which are evolved through time and stored as an internal memory.
Che-Yu Kuo, Jen-Tzung Chien
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A spiking recurrent neural network

IEEE Computer Society Annual Symposium on VLSI, 2004
A spiking recurrent neural network implementing an associative memory is proposed. The circuit including four integrate-and-fire (IF) and Willshaw-type binary synapses is designed with the AMI 0.5/spl mu/m CMOS process. A large-scale network is simulated with Matlab and its storage capacity is calculated and analyzed.
Yuan Li, John G. Harris
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Dropout for Recurrent Neural Networks

2019
Neural networks are computational structures which can be trained to perform tasks based on training examples or patterns. Recurrent neural networks are a type of network designed to process time-series data. Dropout is a neural network regularization technique.
Nathan Watt, Mathys C. du Plessis
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