Keywords: recurrent neural networks, neural ODE, kernel method, theory of deep learning, generalization bounds
TL;DR: Via a neural ODE approach, we frame RNN as a kernel method and derive theoretical guarantees on generalization and stability.
Abstract: Building on the interpretation of a recurrent neural network (RNN) as a continuous-time neural differential equation, we show, under appropriate conditions, that the solution of a RNN can be viewed as a linear function of a specific feature set of the input sequence, known as the signature. This connection allows us to frame a RNN as a kernel method in a suitable reproducing kernel Hilbert space. As a consequence, we obtain theoretical guarantees on generalization and stability for a large class of recurrent networks. Our results are illustrated on simulated datasets.
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