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Publications associées (543)

MATHICSE Technical Report : Reduced order methods for uncertainty quantification problems

Alfio Quarteroni, Gianluigi Rozza, Peng Chen

This work provides a review on reduced order methods in solving uncertainty quantification problems. A quick introduction of the reduced order methods, including proper orthogonal decomposition and greedy reduced basis methods, are presented along with the ...

MATHICSE2015

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

Selection methods regulate evolution of cooperation in digital evolution

Dario Floreano, Laurent Keller, Pawel Lichocki

A key, yet often neglected, component of digital evolution and evolutionary models is the 'selection method' which assigns fitness (number of offspring) to individuals based on their performance scores (efficiency in performing tasks). Here, we study with ...

Royal Soc2014

Tractable Approaches to Learning and Planning in High Dimensions

Leonidas Lefakis

The continuous increase, witnessed in the last decade, of both the amount of available data and the areas of application of machine learning, has lead to a demand for both learning and planning algorithms that are capable of handling large-scale problems. ...

EPFL2014

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

Unsupervised Texture Segmentation Using Monogenic Curvelets and the Potts Model

Michaël Unser, Martin Kurt Storath

We present a method for the unsupervised segmentation of textured images using Potts functionals, which are a piecewise-constant variant of the Mumford and Shah functionals. We propose a minimization strategy based on the alternating direction method of mu ...

IEEE2014

Genome-wide patterns of adaptation to climate-mediated selective pressures in sheep

Stéphane Joost, Sylvie Stucki

Sheep (Ovis aries) have been well adapted to thrive in a diverse range of climates during the domestication and breed development process. These climate-mediated selective pressures have shown to influence phenotypic variation within and among breeds and, ...

2014

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

Learning decision rules for energy efficient building control

Colin Neil Jones

While rule based control (RBC) is current practice in most building automation systems that issue discrete control signals, recent simulation studies suggest that advanced, optimization based control methods such as hybrid model predictive control (HMPC) c ...

Elsevier2014

Hydraulic-hydrologic model for the Zambezi River using satellite data and artificial intelligence techniques

José Pedro Gamito de Saldanha Calado Matos

The development of hydraulic-hydrologic models is a challenge in the case of large catchment areas with scarce or erroneous measurement data and observations. With his study Mr. Dr. José Pedro Matos made several original contributions in order to overcome ...

EPFL - LCH2014

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