Publications associées à Dictionary Learning with Statistical Sparsity in the Presence of Noise

Publications associées (6)

Leveraging Continuous Time to Understand Momentum When Training Diagonal Linear Networks

Nicolas Henri Bernard Flammarion, Scott William Pesme, Hristo Georgiev Papazov

In this work, we investigate the effect of momentum on the optimisation trajectory of gradient descent. We leverage a continuous-time approach in the analysis of momentum gradient descent with step size

\gamma

and momentum parameter

\beta

that allows u ...

2024

Kernel Methods and Similarity Learning Applied in Computational Chemistry

Raimon Fabregat I De Aguilar-Amat

Over the last two decades, data-powered machine learning (ML) tools have profoundly transformed numerous scientific fields. In computational chemistry, machine learning applications have permitted faster predictions of chemical properties and provided powe ...

EPFL2022

Sparse image reconstruction on the sphere: implications of a new sampling theorem

Jean-Philippe Thiran, Dimitri Nestor Alice Van De Ville, Pierre Vandergheynst, Yves Wiaux, Gilles Puy, Jason Douglas McEwen

A new sampling theorem on the sphere has been developed recently, reducing the number of samples required to represent a band-limited signal by a factor of two for equiangular sampling schemes. For signals sparse in a spatially localised measure, such as i ...

Institute of Electrical and Electronics Engineers2013

Méthode d'étalonnage d'une machine-outil à cinématique parallèle à cinq axes à grands angles d'inclinaison

Hélène Frayssinet Mazerolle

A machine-tool is able to manufacture precisely only when it is calibrated. Calibration consists in correcting the model of the machine so that it represents the real kinematics. On no account calibrating a structure consists in changing some parts of the ...

EPFL2007

Distributed SVM applied to image classification

Pascal Frossard, Effrosyni Kokiopoulou

This paper proposes an algorithm for distributed classification, based on a SVM scheme. The contribution of each support vector is approximated by low complexity distributed thresholding over sub-dictionaries, whose union forms a redundant dictionary of at ...

2006

Image compression using an edge adapted redundant dictionary and wavelets

Pierre Vandergheynst, Lorenzo Granai, Lorenzo Peotta

Low bit rate image coding is an important problem regarding applications such as storage on low memory devices or streaming data on the internet. The state of the art in image compression is to use 2-D wavelets. The advantages of wavelet bases lie in their ...

2006