In this tutorial paper, we first define mean squared error, variance, covariance, and bias of
both random variables and classification/predictor models. Then, we formulate the true and …

Canonical correlation analysis is a family of multivariate statistical methods for the analysis
of paired sets of variables. Since its proposition, canonical correlation analysis has, for …

Generative Adversarial Networks (GANs) have achieved remarkable results in the task of
generating realistic natural images. In most successful applications, GAN models share two …

Linear dimensionality reduction methods are a cornerstone of analyzing high dimensional
data, due to their simple geometric interpretations and typically attractive computational …

Sparse estimation methods are aimed at using or obtaining parsimonious representations of
data or models. They were first dedicated to linear variable selection but numerous …

We consider the problem of optimizing the sum of a smooth convex function and a non-
smooth convex function using proximal-gradient methods, where an error is present in the …

Visual recognition research often assumes a sufficient resolution of the region of interest
(ROI). That is usually violated in practice, inspiring us to explore the Very Low Resolution …

In recent years, blind source separation (BSS) has received much attention in the
hyperspectral unmixing field due to the fact that it allows the simultaneous estimation of both …

Abstract “Blendshapes”, a simple linear model of facial expression, is the prevalent
approach to realistic facial animation. It has driven animated characters in Hollywood films …

We analyze stochastic algorithms for optimizing nonconvex, nonsmooth finite-sum problems,
where the nonsmooth part is convex. Surprisingly, unlike the smooth case, our knowledge of …