Learning Long Term Dependencies with Recurrent Neural Networks

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Deep learning in neural networks: An overview (2014, neutral): “Compare some preliminary, NN-specific discussion (Werbos, 1974, Section 5.5.1), a method for multilayer threshold NNs (Bobrowski, 1978), and a computer program for automatically deriving and implementing BP for given differentiable systems (Speelpenning, 1980).”
An Incremental Self-Organizing Architecture for Sensorimotor Learning and Prediction (2017, background): “erties of a system or state transition probabilities are not known [3], [19].”
On Learning to Think: Algorithmic Information Theory for Novel Combinations of Reinforcement Learning Controllers and Recurrent Neural World Models (2015, positive): “Those early CM systems, however, did not yet use powerful RNNs such as LSTM.”
Regularizing Recurrent Networks - On Injected Noise and Norm-based Methods (2014, background): “In an analysis by Schaefer et al. (2008), the authors claim that the widely discussed problem of long-term dependency identiﬁcation in RNNs does not really exist.”
A predictive network architecture for a robust and smooth robot docking behavior (2012, background): “approximation and thereby optimally predict non-linear data [24, 25, 26, 27].”