Publications related to A reliable method of extracting the rheological properties of fruit purees from flow loop data

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

Enhancing statistical performance of data-driven controller tuning via L2-regularization

Alireza Karimi

Noniterative data-driven techniques are design methods that allow optimal feedback control laws to be derived from input-output (I/O) data only, without the need of a model of the process. A drawback of these methods is that, in their standard formulation, ...

Pergamon-Elsevier Science Ltd2014

Sparse regularization for fiber ODF reconstruction: from the suboptimality of $\ell_2$ and $\ell_1$ priors to $\ell_0$

Jean-Philippe Thiran, Dimitri Nestor Alice Van De Ville, Yves Wiaux, Alessandro Daducci

Diffusion MRI is a well established imaging modality providing a powerful way to probe the structure of the white matter non-invasively. Despite its potential, the intrinsic long scan times of these sequences have hampered their use in clinical practice. F ...

Institute of Electrical and Electronics Engineers2014

Stability and Hypothesis Transfer Learning

Ilja Kuzborskij

We consider the transfer learning scenario, where the learner does not have access to the source domain directly, but rather operates on the basis of hypotheses induced from it - the Hypothesis Transfer Learning (HTL) problem. Particularly, we conduct a th ...

2013

On L2-Regularization for Virtual Reference Feedback Tuning

Alireza Karimi

The Virtual Reference Feedback Tuning (VRFT) approach is a design method that allow optimal feedback control laws to be derived from input-output (I/O) data only, without need of a model of the process. A drawback of this methods is that, in its standard f ...

2013

Investigation of droplet shedding in flow focusing geometries using the boundary element method.

François Gallaire, Mathias Nagel

Shedding of droplets in a flow focusing junction is standard technique in two-phase microfluidics. This break-up is driven by a capillary instability. Here we present a depth-averaged approach to simulate these complex phenomena. Since the physical mechani ...

2013

Manifold Sparse Beamforming

Volkan Cevher, Afsaneh Asaei, Baran Gözcü

We consider the minimum variance distortionless response (MVDR) beamforming problems where the array covariance matrix is rank deficient. The conventional approach handles such rank-deficiencies via diagonal loading on the covariance matrix. In this settin ...

2013

Manifold Sparse Beamforming

Volkan Cevher, Afsaneh Asaei, Baran Gözcü

We consider the minimum variance distortionless response (MVDR) beamforming problems where the array covariance matrix is rank deficient. The conventional approach handles such rank-deficiencies via diagonal loading on the covariance matrix. In this settin ...

IEEE2013

The dilaton Wess-Zumino action in higher dimensions

Boaz Keren Zur, Florent Baume

We present a general formula for the Wess-Zumino action associated with the Weyl anomaly, given in a curved background for any even number of dimensions. The result is obtained by considering a finite Weyl transformation of counterterms in dimensional regu ...

Springer2013

Hessian-Based Regularization For 3-D Microscopy Image Restoration

Michaël Unser, Aurélien Bourquard, Stamatios Lefkimmiatis

We investigate a non quadratic regularizer that is based on the Hessian operator for dealing with the restoration of 3-D images in a variational framework. We show that the regularizer under study is a valid extension of the total-variation (TV) functional ...

Ieee2012

A reliable method of extracting the rheological properties of fruit purees from flow loop data

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

Enhancing statistical performance of data-driven controller tuning via L2-regularization

Sparse regularization for fiber ODF reconstruction: from the suboptimality of $\ell_2$ and $\ell_1$ priors to $\ell_0$

Stability and Hypothesis Transfer Learning

On L2-Regularization for Virtual Reference Feedback Tuning

Investigation of droplet shedding in flow focusing geometries using the boundary element method.

Manifold Sparse Beamforming

Manifold Sparse Beamforming

The dilaton Wess-Zumino action in higher dimensions

Hessian-Based Regularization For 3-D Microscopy Image Restoration

On L2-Regularization for Virtual Reference Feedback Tuning

Manifold Sparse Beamforming

The dilaton Wess-Zumino action in higher dimensions

Hessian-Based Regularization For 3-D Microscopy Image Restoration

Sparse regularization for fiber ODF reconstruction: from the suboptimality of $\ell_2$ and $\ell_1$ priors to $\ell_0$

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

Enhancing statistical performance of data-driven controller tuning via L2-regularization

Stability and Hypothesis Transfer Learning

Investigation of droplet shedding in flow focusing geometries using the boundary element method.

Manifold Sparse Beamforming