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Substance Use & Misuse, 1998
(1998). Self-Recurrent Neural Network. Substance Use & Misuse: Vol. 33, No. 2, pp. 495-501.
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(1998). Self-Recurrent Neural Network. Substance Use & Misuse: Vol. 33, No. 2, pp. 495-501.
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Recurrent Neural Network Architectures
2017In this chapter, we present three different recurrent neural network architectures that we employ for the prediction of real-valued time series. All the models reviewed in this chapter can be trained through the previously discussed backpropagation through time procedure.
Bianchi, Filippo Maria +4 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), 2016Storytelling 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 +2 more
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MARKOV RECURRENT NEURAL NETWORKS
2018 IEEE 28th International Workshop on Machine Learning for Signal Processing (MLSP), 2018Deep 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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Recurrent Neural Networks Are Universal Approximators
International Journal of Neural Systems, 2006Recurrent Neural Networks (RNN) have been developed for a better understanding and analysis of open dynamical systems. Still the question often arises if RNN are able to map every open dynamical system, which would be desirable for a broad spectrum of applications.
Anton Maximilian Schäfer +1 more
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Recurrent neural networks for syllabification
Speech Communication, 1993Abstract An important procedure in many prosodic analysis systems is locating syllables. The location of syllables is used in the identification of stress and of pitch accents, which in turn form the basis for the analysis of rhythm and intonation. This paper presents a novel syllabification system utilising recurrent neural networks which operates ...
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Randomized Recurrent Neural Networks.
2018Neural Networks (NNs) with random weights represent nowadays a topic of consolidated use in the Machine Learning research community. In this contribution we focus in particular on recurrent NN models, which in a randomized setting represent a case of particular interest per se, entailing a number of intriguing research challenges primarily related to ...
Claudio Gallicchio +2 more
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Selective Recurrent Neural Network
Neural Processing Letters, 2012It is known that recurrent neural networks may have difficulties remembering data over long time lags. To overcome this problem, we propose an extended architecture of recurrent neural networks, which is able to deal with long time lags between relevant input signals.
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Recurrent Neural Networks for Edge Intelligence: A Survey
ACM Computing Surveys, 2022Varsha S Lalapura, J Amudha
exaly