Automatic differentiation (autodiff) has revolutionized machine learning. Itallows to express
complex computations by composing elementary ones in creativeways and removes the …

Regularization methods are a key tool in the solution of inverse problems. They are used to
introduce prior knowledge and allow a robust approximation of ill-posed (pseudo-) inverses …

A very fast noniterative algorithm is proposed for denoising or smoothing one-dimensional
discrete signals, by solving the total variation regularized least-squares problem or the …

This thoroughly updated new edition presents state of the art sparse and multiscale image
and signal processing. It covers linear multiscale geometric transforms, such as wavelet …

Algorithms for solving variational regularization of ill-posed inverse problems usually involve
operators that depend on a collection of continuous parameters. When the operators enjoy …

Feature extraction plays an essential role in bearing fault detection. However, the measured
vibration signals are complex and non-stationary in nature, and meanwhile impulsive …

Abstract Setting regularization parameters for Lasso-type estimators is notoriously difficult,
though crucial for obtaining the best accuracy. The most popular hyperparameter …

Accurate detection of copy number variations (CNVs) from short-read sequencing data is
challenging due to the uneven distribution of reads and the unbalanced amplitudes of gains …

The total variation image (or signal) denoising model is a variational approach that can be
interpreted, in a Bayesian framework, as a search for the maximum point of the posterior …

We study regularized regression problems where the regularizer is a proper, lower-
semicontinuous, convex and partly smooth function relative to a Riemannian submanifold …