177 citations · 661 across the 41 of their papers we have counts for
10 papers · 1 filter
Weak convergence rates for spectral regularization via sampling inequalities
Sabrina Guastavino, Gabriele Santin, Francesco Marchetti +1
Convergence rates in spectral regularization methods quantify the approximation error in inverse problems as a function of the noise level or the number of sampling points. Classic…
Convergence rates for Tikhonov regularization on compact sets: application to neural networks
Barbara Palumbo, Paolo Massa, Federico Benvenuto
In this work, we consider ill-posed inverse problems in which the forward operator is continuous and weakly closed, and the sought solution belongs to a weakly closed constraint se…
Solving Implicit Inverse Problems with Homotopy-Based Regularization Path
Davide Parodi, Federico Benvenuto, Sara Garbarino +1
Implicit inverse problems, in which noisy observations of a physical quantity are used to infer a nonlinear functional applied to an associated function, are inherently ill posed a…
A hybrid time-frequency parametric modelling of medical ultrasound signal transmission
Chiara Razzetta, Valentina Candiani, Marco Crocco +1
Medical ultrasound imaging is the most widespread real-time non-invasive imaging system and its formulation comprises signal transmission, signal reception, and image formation. Ul…
A fast and convergent combined Newton and gradient descent method for computing steady states of chemical reaction networks
Silvia Berra, Alessandro La Torraca, Federico Benvenuto +1
In this work we present a fast, globally convergent, iterative algorithm for computing the asymptotically stable states of nonlinear large--scale systems of quadratic autonomous Or…
Regularization from examples via neural networks for parametric inverse problems: topology matters
Paolo Massa, Sara Garbarino, Federico Benvenuto
In this work we deal with parametric inverse problems, which consist in recovering a finite number of parameters describing the structure of an unknown object, from indirect measur…
Predictive risk estimation for the Expectation Maximization algorithm with Poisson data
Paolo Massa, Federico Benvenuto
In this work, we introduce a novel estimator of the predictive risk with Poisson data, when the loss function is the Kullback-Leibler divergence, in order to define a regularizatio…
A Parameter Choice Rule for Tikhonov Regularization Based on Predictive Risk
Federico Benvenuto, Bangti Jin
In this work, we propose a new criterion for choosing the regularization parameter in Tikhonov regularization when the noise is white Gaussian. The criterion minimizes a lower boun…
On the connection between supervised learning and linear inverse problems
Sabrina Guastavino, Federico Benvenuto
In this paper we investigate the connection between supervised learning and linear inverse problems. We first show that a linear inverse problem can be view as a function approxima…
Regularization of constrained maximum likelihood iterative algorithms by means of statistical stopping rule
Federico Benvenuto, Michele Piana
In this paper we propose a new statistical stopping rule for constrained maximum likelihood iterative algorithms applied to ill-posed inverse problems. To this aim we extend the de…