64 citations · 339 across the 45 of their papers we have counts for
57 papers
Memory of recurrent networks: Do we compute it right?
Giovanni Ballarin, Lyudmila Grigoryeva, Juan-Pablo Ortega
Numerical evaluations of the memory capacity (MC) of recurrent neural networks reported in the literature often contradict well-established theoretical bounds. In this paper, we st…
Infinite-dimensional reservoir computing
Lukas Gonon, Lyudmila Grigoryeva, Juan-Pablo Ortega
Reservoir computing approximation and generalization bounds are proved for a new concept class of input/output systems that extends the so-called generalized Barron functionals to…
Learnability of Linear Port-Hamiltonian Systems
Juan-Pablo Ortega, Daiying Yin
A complete structure-preserving learning scheme for single-input/single-output (SISO) linear port-Hamiltonian systems is proposed. The construction is based on the solution, when p…
Expressiveness and Structure Preservation in Learning Port-Hamiltonian Systems
Juan-Pablo Ortega, Daiying Yin
A well-specified parametrization for single-input/single-output (SISO) linear port-Hamiltonian systems amenable to structure-preserving supervised learning is provided. The constru…
The Gaussian kernel on the circle and spaces that admit isometric embeddings of the circle
Nathaël Da Costa, Cyrus Mostajeran, Juan-Pablo Ortega
On Euclidean spaces, the Gaussian kernel is one of the most widely used kernels in applications. It has also been used on non-Euclidean spaces, where it is known that there may be…
Reservoir kernels and Volterra series
Lukas Gonon, Lyudmila Grigoryeva, Juan-Pablo Ortega
A universal kernel is constructed whose sections approximate any causal and time-invariant filter in the fading memory category with inputs and outputs in a finite-dimensional Eucl…