26 citations · 27 across the 11 of their papers we have counts for
12 papers
Robustness to noise and erasures of Gabor frames in finite dimensions
Palina Salanevich, Nigel Q. D. Strachan
In this paper, we investigate the robustness of structured frames to measurement noise and erasures, with the focus on Gabor frames with arbitrary sets of time-frequency s…
Phasebook: A Survey of Selected Open Problems in Phase Retrieval
Marc Allain, Selin Aslan, Wim Coene +13
Phase retrieval is an inverse problem that, on one hand, is crucial in many applications across imaging and physics, and, on the other hand, leads to deep research questions in the…
Spectral method for low-dose Poisson and Bernoulli phase retrieval
Sjoerd Dirksen, Felix Krahmer, Patricia Römer +1
We consider the problem of phaseless reconstruction from measurements with Poisson or Bernoulli distributed noise. This is of particular interest in biological imaging experiments…
PtyGenography: using generative models for regularization of the phase retrieval problem
Selin Aslan, Tristan van Leeuwen, Allard Mosk +1
In phase retrieval and similar inverse problems, the stability of solutions across different noise levels is crucial for applications. One approach to promote it is using signal pr…
On best approximation by multivariate ridge functions with applications to generalized translation networks
Paul Geuchen, Palina Salanevich, Olov Schavemaker +1
In this paper, we prove sharp upper and lower bounds for the approximation of Sobolev functions by sums of multivariate ridge functions, i.e., for approximation by functions of the…
Injectivity of Multi-window Gabor Phase Retrieval
Palina Salanevich
In many signal processing problems arising in practical applications, we wish to reconstruct an unknown signal from its phaseless measurements with respect to a frame. This inverse…
On Graph Uncertainty Principle and Eigenvector Delocalization
Elizaveta Rebrova, Palina Salanevich
Uncertainty principles present an important theoretical tool in signal processing, as they provide limits on the time-frequency concentration of a signal. In many real-world applic…
Efficient uniform approximation using Random Vector Functional Link networks
Palina Salanevich, Olov Schavemaker
A Random Vector Functional Link (RVFL) network is a depth-2 neural network with random inner weights and biases. Only the outer weights of such an architecture are to be learned, s…
On audio enhancement via online non-negative matrix factorization
Andrew Sack, Wenzhao Jiang, Michael Perlmutter +2
We propose a method for noise reduction, the task of producing a clean audio signal from a recording corrupted by additive noise. Many common approaches to this problem are based u…
Random Vector Functional Link Networks for Function Approximation on Manifolds
Deanna Needell, Aaron A. Nelson, Rayan Saab +2
The learning speed of feed-forward neural networks is notoriously slow and has presented a bottleneck in deep learning applications for several decades. For instance, gradient-base…
Extreme Singular Values of Random Time-Frequency Structured Matrices
Palina Salanevich
In this paper, we investigate extreme singular values of the analysis matrix of a Gabor frame with a random window . Columns of such matrices are time and frequency shi…
Robust Phase Retrieval Algorithm for Time-Frequency Structured Measurements
Goetz E. Pfander, Palina Salanevich
We address the problem of signal reconstruction from intensity measurements with respect to a measurement frame. This non-convex inverse problem is known as phase retrieval. The ca…