Adaptive Synchronization of Neural and Physical Oscillators

By Kenji Doya, S. Yoshizawa. 1991

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On the difficulty of training recurrent neural networks (2012, background): “It has been shown that in practice it can reduce the chance that gradients explode, and even allow training generator models or models that work with unbounded amounts of memory(Pascanu and Jaeger, 2011; Doya and Yoshizawa, 1991).”
Understanding the exploding gradient problem (2012, background): “It has been showed that in practice it can reduce the chance that gradients explode, and even allow training generator models or models that work with unbounded amounts of memory(Pascanu and Jaeger, 2011; Doya and Yoshizawa, 1991).”