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In Machine Translation (MT), one of most important research fields of AI, models based on Recurrent Neural Networks (RNN) show state-of-the-art performance in recent years, and many researchers keep working on improving RNN-based models to achieve better accuracy in translation tasks.
The control scheme is based on recurrent neural networks emulator and controller with decoupled adaptive rates.
Weather regimes are based on recurrent patterns of pressure and circulation anomalies over vast geographical areas, also referred to as large-scale teleconnection patterns.
The efficacy outcome was based on recurrent VTE and death due to VTE during the 3-month treatment period.
This paper presents two new design procedures for synthesizing autoassociative memory and heteroassociative memory based on recurrent neural networks with different external inputs and mixed delays.
We proposed the first models based on recurrent neural networks (more specifically Long Short-Term Memory - LSTM) for classifying relations from clinical notes.
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The approach is based on a recurrent NARX model obtained by linear combination of a recurrent neural network (RNN) output and the real data output.
At the core of this system is a deep learning technology based on convolutional recurrent video prediction, or dynamic neural advection (DNA).
The neural controller is based on a Recurrent High Order Neural Network (RHONN), trained with an Extended Kalman Filter (EKF).
The neural-optimal control module is based on a recurrent Jordan neural network architecture that is trained using optimal policies produced by a dynamic optimal control module.
It is shown that such an approach leads to the simple varying delay detection and random delay identification algorithms based on ordinary recurrent estimation technique.
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Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com