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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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Dropout for Recurrent Neural Networks
2019Neural 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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A spiking recurrent neural network
IEEE Computer Society Annual Symposium on VLSI, 2004A 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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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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Randomized Recurrent Neural Networks. [PDF]
, 2018 Neural 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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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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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 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 Networks for Edge Intelligence
ACM Computing Surveys, 2022Varsha S Lalapura, J Amudha
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A Comprehensive Review of Stability Analysis of Continuous-Time Recurrent Neural Networks
IEEE Transactions on Neural Networks and Learning Systems, 2014Huaguang Zhang +2 more
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