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Estimation par noyau

Publications associées (27)

Worldwide performance evaluation of ground surface reflectance models

Christophe Ballif, Arttu Matias Tuomiranta

To improve the accuracy of bifacial gain estimation, recent radiative models of solar energy systems have abandoned the traditional assumption of isotropic ground-reflected radiance. However, surface reflectance itself is still commonly considered as a con ...

2021

Complex Analysis meets Statistical Mechanics: Applications to Kernel Methods and Conformal Field Theory

Francesco Spadaro

This thesis is centered on questions coming from Machine Learning (ML) and Statistical Field Theory (SFT). In Machine Learning, we consider the subfield of Supervised Learning (SL), and in particular regression tasks where one tries to find a regressor tha ...

EPFL2021

Sparsely Observed Functional Time Series: Theory and Applications

Tomas Rubin

Functional time series is a temporally ordered sequence of not necessarily independent random curves. While the statistical analysis of such data has been traditionally carried out under the assumption of completely observed functional data, it may well ha ...

EPFL2021

Comparison of non-parametric T2 relaxometry methods for myelin water quantification

Jean-Philippe Thiran, Tobias Kober, Alessandro Daducci, Tom Hilbert, Erick Jorge Canales Rodriguez, Nicolas Kunz, Marco Pizzolato, Gian Franco Piredda, Thomas Yu

Multi-component T2 relaxometry allows probing tissue microstructure by assessing compartment-specific T2 relaxation times and water fractions, including the myelin water fraction. Non-negative least squares (NNLS) with zero-order Tikhonov regularization is ...

2021

Computing the daily reproduction number of COVID-19 by inverting the renewal equation using a variational technique

Jean-David Horacio Morel

The COVID-19 pandemic has undergone frequent and rapid changes in its local and global infection rates, driven by governmental measures or the emergence of new viral variants. The reproduction number Rt indicates the average number of cases generated by an ...

National Academy of Sciences2021

Classifying high-dimensional Gaussian mixtures: Where kernel methods fail and neural networks succeed

Lenka Zdeborová, Florent Gérard Krzakala, Maria Refinetti

A recent series of theoretical works showed that the dynamics of neural networks with a certain initialisation are well-captured by kernel methods. Concurrent empirical work demonstrated that kernel methods can come close to the performance of neural netwo ...

PMLR2021

Kernel Density Estimation through Density Constrained Near Neighbor Search

Mikhail Kapralov, Navid Nouri

In this paper we revisit the kernel density estimation problem: given a kernel K(x, y) and a dataset of n points in high dimensional Euclidean space, prepare a data structure that can quickly output, given a query q, a (1 + epsilon)-approximation to mu := ...

IEEE2020