Transcription of On the difficulty of training Recurrent Neural Networks
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On the difficulty of training Recurrent Neural Networks
On the difficulty of training Recurrent Neural NetworksRazvan de MontrealTomas UniversityYoshua de MontrealAbstractThere are two widely known issues with prop-erly training Recurrent Neural Networks , thevanishingand theexplodinggradient prob-lems detailed in Bengioet al.(1994). Inthis paper we attempt to improve the under-standing of the underlying issues by explor-ing these problems from an analytical, a geo-metric and a dynamical systems analysis is used to justify a simple yet ef-fective solution. We propose a gradient normclipping strategy to deal with exploding gra-dients and a soft constraint for the vanishinggradients problem. We validate empiricallyour hypothesis and proposed solutions in theexperimental IntroductionA Recurrent Neural network (RNN), Fig.
A recurrent neural network (RNN), e.g. Fig. 1, is a neural network model proposed in the 80’s (Rumelhart et al., 1986; Elman, 1990; Werbos, 1988) for modeling time series. The structure of the network is similar to that of a standard multilayer perceptron, with the dis-tinction that we allow connections among hidden units associated with a ...
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