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• W3039893874 abstract "Recurrent neural networks (RNN) as used in machine learning are commonly formulated in discrete time, i.e. as recursive maps. This brings a lot of advantages for training models on data, e.g. for the purpose of time series prediction or dynamical systems identification, as powerful and efficient inference algorithms exist for discrete time systems and numerical integration of differential equations is not necessary. On the other hand, mathematical analysis of dynamical systems inferred from data is often more convenient and enables additional insights if these are formulated in continuous time, i.e. as systems of ordinary (or partial) differential equations (ODE). Here we show how to perform such a translation from discrete to continuous time for a particular class of ReLU-based RNN. We prove three theorems on the mathematical equivalence between the discrete and continuous time formulations under a variety of conditions, and illustrate how to use our mathematical results on different machine learning and nonlinear dynamical systems examples." @default.
• W3039893874 created "2020-07-10" @default.
• W3039893874 creator A5056788018 @default.
• W3039893874 creator A5075158815 @default.
• W3039893874 date "2020-07-01" @default.
• W3039893874 modified "2023-09-26" @default.
• W3039893874 title "Transformation of ReLU-based recurrent neural networks from discrete-time to continuous-time" @default.
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• W3039893874 hasPublicationYear "2020" @default.
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