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Communication Dans Un Congrès Année : 2021

Framing RNN as a kernel method: A neural ODE approach

Résumé

Building on the interpretation of a recurrent neural network (RNN) as a continuoustime 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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Dates et versions

hal-03943120 , version 1 (10-02-2023)

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Adeline Fermanian, Pierre Marion, Jean-Philippe Vert, Gérard Biau. Framing RNN as a kernel method: A neural ODE approach. Thirty-fifth Conference on Neural Information Processing Systems, Dec 2021, Virtual-only, United States. ⟨hal-03943120⟩
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