Publications related to Robust myelin water imaging from multi-echo T2 data using second-order Tikhonov regularization with control points

A Linearly Convergent Proximal Gradient Algorithm for Decentralized Optimization

Ali H. Sayed, Kun Yuan, Sulaiman A S A E Alghunaim

Decentralized optimization is a powerful paradigm that finds applications in engineering and learning design. This work studies decentralized composite optimization problems with non-smooth regularization terms. Most existing gradient-based proximal decent ...

NEURAL INFORMATION PROCESSING SYSTEMS (NIPS)2019

Sparse wars: A survey and comparative study of spherical deconvolution algorithms for diffusion MRI

Jean-Philippe Thiran, Erick Jorge Canales Rodriguez, Gabriel Girard, David Paul Roger Romascano, Marco Pizzolato, Jonathan Rafael Patino Lopez, Alessandro Daducci, Muhamed Barakovic, Gaëtan Olivier D Rensonnet

Spherical deconvolution methods are widely used to estimate the brain's white-matter fiber orientations from diffusion MRI data. In this study, eight spherical deconvolution algorithms were implemented and evaluated. These included two model selection tech ...

2019

A Least-Squares/Relaxation Method for the Numerical Solution of the Three-Dimensional Elliptic Monge-Ampere Equation

Alexandre Caboussat, Dimitrios Gourzoulidis

In this article, we address the numerical solution of the Dirichlet problem for the three-dimensional elliptic Monge-Ampere equation using a least-squares/relaxation approach. The relaxation algorithm allows the decoupling of the differential operators fro ...

SPRINGER/PLENUM PUBLISHERS2018

CoCoA: A General Framework for Communication-Efficient Distributed Optimization

Martin Jaggi, Chenxin Ma

The scale of modern datasets necessitates the development of efficient distributed optimization methods for machine learning. We present a general-purpose framework for distributed computing environments, CoCoA, that has an efficient communication scheme a ...

MICROTOME PUBL2018

Peptide-Cation Systems: Conformational Search, Benchmark Evaluation, and Force Field Parameter Adjustment Using Regularized Linear Regression

Markus Schneider

Metal cations often play an important role in shaping the three-dimensional structure of peptides. As an example, the model system AcPheAla5LysH+ is investigated in order to fully understand the forces that stabilize its helical structure. In particular, t ...

EPFL2018

Theory and Algorithms for Hypothesis Transfer Learning

Ilja Kuzborskij

The design and analysis of machine learning algorithms typically considers the problem of learning on a single task, and the nature of learning in such scenario is well explored. On the other hand, very often tasks faced by machine learning systems arrive ...

EPFL2018

Robust and adaptive approaches for Relative Geologic Time Estimation

Mireille El Gheche

For a geoscientist, the Relative Geologic Time (RGT) is an important tool to perform chronostratigraphic analysis. However, automatically estimate an RGT image from a seismic image can be a challenging task where we have to respect seismic features, the de ...

ELSEVIER SCIENCE BV2018

Optimization Of Compound Regularization Parameters Based On Stein'S Unbiased Risk Estimate

Xin Liu, Hanjie Pan, Feng Xue, Jiaqi Liu

Recently, the type of compound regularizers has become a popular choice for signal reconstruction. The estimation quality is generally sensitive to the values of multiple regularization parameters. In this work, based on BDF algorithm, we develop a data-dr ...

Ieee2017

Sparsity-Based Data Reconstruction Models for Biomedical Imaging

Emrah Bostan

We propose new regularization models to solve inverse problems encountered in biomedical imaging applications. In formulating mathematical schemes, we base our approach on the sparse signal processing principles that have emerged as a central paradigm in t ...

EPFL2016

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

Robust myelin water imaging from multi-echo T2 data using second-order Tikhonov regularization with control points

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A Linearly Convergent Proximal Gradient Algorithm for Decentralized Optimization

Sparse wars: A survey and comparative study of spherical deconvolution algorithms for diffusion MRI

A Least-Squares/Relaxation Method for the Numerical Solution of the Three-Dimensional Elliptic Monge-Ampere Equation

CoCoA: A General Framework for Communication-Efficient Distributed Optimization

Peptide-Cation Systems: Conformational Search, Benchmark Evaluation, and Force Field Parameter Adjustment Using Regularized Linear Regression

Theory and Algorithms for Hypothesis Transfer Learning

Robust and adaptive approaches for Relative Geologic Time Estimation

Optimization Of Compound Regularization Parameters Based On Stein'S Unbiased Risk Estimate

Sparsity-Based Data Reconstruction Models for Biomedical Imaging

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

Sparse wars: A survey and comparative study of spherical deconvolution algorithms for diffusion MRI

A Linearly Convergent Proximal Gradient Algorithm for Decentralized Optimization

A Least-Squares/Relaxation Method for the Numerical Solution of the Three-Dimensional Elliptic Monge-Ampere Equation

Theory and Algorithms for Hypothesis Transfer Learning

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

Optimization Of Compound Regularization Parameters Based On Stein'S Unbiased Risk Estimate

Peptide-Cation Systems: Conformational Search, Benchmark Evaluation, and Force Field Parameter Adjustment Using Regularized Linear Regression

Sparsity-Based Data Reconstruction Models for Biomedical Imaging

Robust and adaptive approaches for Relative Geologic Time Estimation

CoCoA: A General Framework for Communication-Efficient Distributed Optimization