58 citations · 102 across the 11 of their papers we have counts for
15 papers
Making Tensor Decoupling More Robust and Less Parametric
Maxime Rochkoulets, Joppe De Jonghe, Mariya Ishteva
Tensor decoupling algorithms recover compact representations of multivariate functions by combining linear factor matrices with univariate internal nonlinearities. We identify two…
Robust Basis Spline Decoupling for the Compression of Transformer Models
Joppe De Jonghe, Van Tien Pham, Mariya Ishteva
Decoupling is a powerful modeling paradigm for representing multivariate functions as compositions of linear transformations and univariate nonlinear functions. A single-layer deco…
Tensor-based Multi-layer Decoupling
Joppe De Jonghe, Konstantin Usevich, Philippe Dreesen +1
The decoupling of multivariate functions is a powerful modeling paradigm for learning multivariate input-output relations from data. For the single-layer case, established CPD-base…
Adaptive Subspace Modeling With Functional Tucker Decomposition
Noah Steidle, Joppe De Jonghe, Mariya Ishteva
Tensors provide a structured representation for multidimensional data, yet discretization discards the underlying continuous structure when the data originate from continuous proce…
Non-parametric B-spline decoupling of multivariate functions
Joppe De Jonghe, Mariya Ishteva
Many scientific fields and applications require compact representations of multivariate functions. For this problem, decoupling methods are powerful techniques for representing the…
Low-rank tensor recovery for Jacobian-based Volterra identification of parallel Wiener-Hammerstein systems
Konstantin Usevich, Philippe Dreesen, Mariya Ishteva
We consider the problem of identifying a parallel Wiener-Hammerstein structure from Volterra kernels. Methods based on Volterra kernels typically resort to coupled tensor decomposi…