Publications associées à Smoothing technique for nonsmooth composite minimization with linear operator

Representer Theorems for Sparsity-Promoting $ ℓ _{ 1 } $ Regularization

Michaël Unser, Julien René Pierre Fageot, Harshit Gupta

We present a theoretical analysis and comparison of the effect of

ℓ _{ 1 }

versus

ℓ _{ 2 }

regularization for the resolution of ill-posed linear inverse and/or compressed sensing problems. Our formulation covers the most general setting where the s ...

IEEE2016

Comparing molecules and solids across structural and alchemical space

Michele Ceriotti, Sandip De

Evaluating the (dis)similarity of crystalline, disordered and molecular compounds is a critical step in the development of algorithms to navigate automatically the configuration space of complex materials. For instance, a structural similarity metric is cr ...

Royal Society of Chemistry2016

Structure Tensor Total Variation

Michaël Unser, Stamatios Lefkimmiatis

We introduce a novel generic energy functional that we employ to solve inverse imaging problems within a variational framework. The proposed regularization family, termed as structure tensor total variation (STV), penalizes the eigenvalues of the structure ...

SIAM2015

ShapeNet: Convolutional Neural Networks on Non-Euclidean Manifolds

Pierre Vandergheynst, Michael Bronstein

Feature descriptors play a crucial role in a wide range of geometry analysis and processing applications, including shape correspondence, retrieval, and segmentation. In this paper, we propose ShapeNet, a generalization of the popular convolutional neural ...

2015

Process Design and Optimization (MLS-S03): a Journey in Modeling, Optimization and Regression

Julien Léo Billeter

This lecture describes the following topics: • Preamble on Linear Algebra • Dynamic and Static Models • Solving Dynamic and Static Models • Solving Optimization Problems • Solving Regression Problems ...

2015

Sparsistency of $\ell_1$-Regularized $M$-Estimators

Volkan Cevher, Jonathan Mark Scarlett, Yen-Huan Li

We consider the model selection consistency or sparsistency of a broad set of

\ell_1

-regularized

M

-estimators for linear and non-linear statistical models in a unified fashion. For this purpose, we propose the local structured smoothness condition (LSS ...

2015

PURIFY: a new approach to radio-interferometric imaging

Yves Wiaux, Rafael Eduardo Carrillo Rangel, Jason Douglas McEwen

In a recent article series, the authors have promoted convex optimization algorithms for radio-interferometric imaging in the framework of compressed sensing, which leverages sparsity regularization priors for the associated inverse problem and defines a m ...

Wiley-Blackwell2014

Methodologies for predicting natural frequency variation of a suspension bridge

Ian Smith, Ngoc Thanh Trinh, Irwanda Laory

In vibration-based structural health monitoring, changes in the natural frequency of a structure are used to identify changes in the structural conditions due to damage and deterioration. However, natural frequency values also vary with changes in environm ...

Elsevier2014

Crop Backscatter Modeling And Soil Moisture Estimation With Support Vector Regression

Jean-Philippe Thiran, Devis Tuia, Maurice Borgeaud, Jelena Stamenkovic

In this paper, we used an improved version of the Tor Vergata radiative transfer model to simulate the backscattering coefficient for the L-band SAR signals over areas covered with vegetation. Fields of winter wheat, maize and sugar beet observed during th ...

Ieee2014

Correlation Estimation from Compressed Images

Pascal Frossard, Vijayaraghavan Thirumalai

This paper addresses the problem of correlation estimation in sets of compressed images. We consider a framework where images are represented under the form of linear measurements due to low complexity sensing or security requirements. We assume that the i ...

Elsevier2013

Smoothing technique for nonsmooth composite minimization with linear operator

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Representer Theorems for Sparsity-Promoting $ ℓ _{ 1 } $ Regularization

Comparing molecules and solids across structural and alchemical space

Structure Tensor Total Variation

ShapeNet: Convolutional Neural Networks on Non-Euclidean Manifolds

Process Design and Optimization (MLS-S03): a Journey in Modeling, Optimization and Regression

Sparsistency of $\ell_1$-Regularized $M$-Estimators

PURIFY: a new approach to radio-interferometric imaging

Methodologies for predicting natural frequency variation of a suspension bridge

Crop Backscatter Modeling And Soil Moisture Estimation With Support Vector Regression

Correlation Estimation from Compressed Images

PURIFY: a new approach to radio-interferometric imaging

Methodologies for predicting natural frequency variation of a suspension bridge

Correlation Estimation from Compressed Images

Structure Tensor Total Variation

Representer Theorems for Sparsity-Promoting $ ℓ _{ 1 } $ Regularization

ShapeNet: Convolutional Neural Networks on Non-Euclidean Manifolds

Sparsistency of $\ell_1$-Regularized $M$-Estimators

Process Design and Optimization (MLS-S03): a Journey in Modeling, Optimization and Regression

Crop Backscatter Modeling And Soil Moisture Estimation With Support Vector Regression

Comparing molecules and solids across structural and alchemical space