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

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

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

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

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

Efficient Statistical Parameter Selection for Nonlinear Modeling of Process/Performance Variation

Giovanni De Micheli, Pierre-Emmanuel Julien Marc Gaillardon, Hassan Ghasemzadeh Mohammadi

With the growing number of process variation sources in deeply nano-scaled technologies, parameterized device and circuit modeling is becoming very important for chip design and verification. However, the high dimensionality of parameter space, for process ...

Ieee-Inst Electrical Electronics Engineers Inc2016

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

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

Fast Process Variation Analysis in Nano-Scaled Technologies Using Column-Wise Sparse Parameter Selection

Giovanni De Micheli, Pierre-Emmanuel Julien Marc Gaillardon, Majid Yazdani, Hassan Ghasemzadeh Mohammadi

With growing concern about process variation in deeply nano-scaled technologies, parameterized device and circuit modeling is becoming very important for design and verification. However, the high dimensionality of parameter space is a serious modeling cha ...

2014

An Equivalence between the Lasso and Support Vector Machines

Martin Jaggi

We investigate the relation of two fundamental tools in machine learning and signal processing, that is the support vector machine (SVM) for classification, and the Lasso technique used in regression. We show that the resulting optimization problems are eq ...

Chapman and Hall/CRC2014

A Sequential Topic Model for Mining Recurrent Activities from Long Term Video Logs

Jean-Marc Odobez, Rémi Emonet, Jagannadan Varadarajan

This paper introduces a novel probabilistic activity modeling approach that mines recurrent sequential patterns called motifs from documents given as word×time count matrices (e.g., videos). In this model, documents are represented as a mixture of sequenti ...

Springer2013

Combinatorial Selection and Least Absolute Shrinkage via the CLASH Algorithm

Volkan Cevher, Anastasios Kyrillidis

The least absolute shrinkage and selection operator (LASSO) for linear regression exploits the geometric interplay of the

\ell_2

-data error objective and the

\ell_1

-norm constraint to arbitrarily select sparse models. Guiding this uninformed selection ...

IEEE2012

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

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

Comparing molecules and solids across structural and alchemical space

Sparsity-Based Data Reconstruction Models for Biomedical Imaging

Efficient Statistical Parameter Selection for Nonlinear Modeling of Process/Performance Variation

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

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

Fast Process Variation Analysis in Nano-Scaled Technologies Using Column-Wise Sparse Parameter Selection

An Equivalence between the Lasso and Support Vector Machines

A Sequential Topic Model for Mining Recurrent Activities from Long Term Video Logs

Combinatorial Selection and Least Absolute Shrinkage via the CLASH Algorithm

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

Fast Process Variation Analysis in Nano-Scaled Technologies Using Column-Wise Sparse Parameter Selection

A Sequential Topic Model for Mining Recurrent Activities from Long Term Video Logs

Combinatorial Selection and Least Absolute Shrinkage via the CLASH Algorithm

Sparsity-Based Data Reconstruction Models for Biomedical Imaging

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

Efficient Statistical Parameter Selection for Nonlinear Modeling of Process/Performance Variation

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

Comparing molecules and solids across structural and alchemical space

An Equivalence between the Lasso and Support Vector Machines