4 papers · 1 filter
Greedy techniques for inverse problems
L. Bruni Bruno, P. Massa, E. Perracchione +1
Inverse imaging problems rely on limited and indirect measurements, making reconstruction highly dependent on both regularization and sample locations. We introduce a novel greedy…
Feature Understanding and Sparsity Enhancement via 2-Layered kernel machines (2L-FUSE)
Fabiana Camattari, Sabrina Guastavino, Francesco Marchetti +1
We propose a novel sparsity enhancement strategy for regression tasks, based on learning a data-adaptive kernel metric, i.e., a shape matrix, through 2-Layered kernel machines. The…
Variably Scaled Kernels for the regularized solution of the parametric Fourier imaging problem
Anna Volpara, Alessandro Lupoli, Emma Perracchione
We address the problem of approximating parametric Fourier imaging problems via interpolation/ extrapolation algorithms that impose smoothing constraints across contiguous values o…
A Recipe for Learning Variably Scaled Kernels via Discontinuous Neural Networks
Gianluca Audone, Francesco Della Santa, Emma Perracchione +1
The efficacy of interpolating via Variably Scaled Kernels (VSKs) is known to be dependent on the definition of a proper scaling function, but no numerical recipes to construct it a…