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Communication Dans Un Congrès Année : 2023

Tensor-based two-layer decoupling of multivariate polynomial maps

Résumé

In this paper, we introduce a new decomposition of multivariate maps that generalizes the decoupling problem recently proposed in the system identification community. In the context of neural networks, this decomposition can be seen as a two-layer feedforward network with flexible activation functions. We show that for such maps the Jacobian and Hessian tensors admit Para Tuck and CP decompositions respectively. We propose a methodology to perform the two-layer decoupling of the given polynomial maps based on joint Para Tuck and CP decomposition, by combining first and second-order information.
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Dates et versions

hal-04360913 , version 1 (13-03-2024)

Identifiants

Citer

Konstantin Usevich, Yassine Zniyed, Mariya Ishteva, Philippe Dreesen, André de Almeida. Tensor-based two-layer decoupling of multivariate polynomial maps. 31st European Signal Processing Conference, EUSIPCO 2023, Sep 2023, Helsinki, Finland. ⟨10.23919/EUSIPCO58844.2023.10289900⟩. ⟨hal-04360913⟩
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