Over the last decade, kernel methods for nonlinear processing have successfully been used
in the machine learning community. The primary mathematical tool employed in these …

In-band full-duplex systems allow for more efficient use of temporal and spectral resources
by transmitting and receiving information at the same time and on the same frequency …

Floquet engineering consists in the modification of physical systems by the application of
periodic time-dependent perturbations. The search for the shape of the periodic perturbation …

For audio source separation applications, it is common to estimate the magnitude of the
short-time Fourier transform (STFT) of each source. In order to further synthesize time …

This paper presents a wide framework for non-linear online supervised learning tasks in the
context of complex valued signal processing. The (complex) input data are mapped into a …

Phase retrieval (PR) aims to recover a signal from the magnitudes of a set of inner products.
This problem arises in many audio signal processing applications which operate on a short …

The construction of reduced-order models for parametrized partial differential systems using
proper orthogonal decomposition (POD) is based on the information of the so-called …

In this work, we use deep unfolding to view cascaded non-linear RF systems as model-
based neural networks. This view enables the direct use of a wide range of neural network …

In this paper, we focus on Stochastic Amplitude Flow (SAF) for phase retrieval, a stochastic
gradient descent for the amplitude-based squared loss. While the convergence to a critical …

The geometric measure of entanglement of a multipartite pure state is defined it terms of its
geometric distance from the set of separable pure states. The quantum eigenvalue problem …