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Publications associées (32)

Optimal radial basis for density-based atomic representations

Michele Ceriotti, Sergey Pozdnyakov, Jigyasa Nigam, Félix Benedito Clément Musil, Alexander Jan Goscinski

The input of almost every machine learning algorithm targeting the properties of matter at the atomic scale involves a transformation of the list of Cartesian atomic coordinates into a more symmetric representation. Many of the most popular representations ...

2021

On the assessment of arterial compliance from carotid pressure waveform

Nikolaos Stergiopoulos, Georgios Rovas, Vasiliki Bikia, Stamatia Zoi Pagoulatou, Patrick Segers

In a progressively aging population, it is of utmost importance to develop reliable, noninvasive, and cost-effective tools to estimate biomarkers that can be indicative of cardiovascular risk. Various pathophysiological conditions are associated to changes ...

AMER PHYSIOLOGICAL SOC2021

The association of basic and challenging motor capacity with mobility performance and falls in young seniors

Kamiar Aminian, Anisoara Ionescu

Background: Understanding the association between motor capacity (MC) (what people can do in a standardized environment), mobility performance (MP) (what people actually do in real-life) and falls is important for early detection of and counteracting on fu ...

ELSEVIER IRELAND LTD2020

Functional lagged regression with sparse noisy observations

Victor Panaretos, Tomas Rubin

A functional (lagged) time series regression model involves the regression of scalar response time series on a time series of regressors that consists of a sequence of random functions. In practice, the underlying regressor curve time series are not always ...

WILEY2020

Wasserstein Distributionally Robust Learning

Soroosh Shafieezadeh Abadeh

Many decision problems in science, engineering, and economics are affected by uncertainty, which is typically modeled by a random variable governed by an unknown probability distribution. For many practical applications, the probability distribution is onl ...

EPFL2020

Affine Combination of Diffusion Strategies Over Networks

Ali H. Sayed, Jie Chen

Diffusion adaptation is a powerful strategy for distributed estimation and learning over networks. Motivated by the concept of combining adaptive filters, this work proposes a combination framework that aggregates the operation of multiple diffusion strate ...

IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC2020

Random time step probabilistic methods for uncertainty quantification in chaotic and geometric numerical integration

Assyr Abdulle, Giacomo Garegnani

A novel probabilistic numerical method for quantifying the uncertainty induced by the time integration of ordinary differential equations (ODEs) is introduced. Departing from the classical strategy to randomize ODE solvers by adding a random forcing term, ...

2020

Segmentation-driven 6D Object Pose Estimation

Pascal Fua, Mathieu Salzmann, Joachim Ludovic Hugonot, Yinlin Hu

The most recent trend in estimating the 6D pose of rigid objects has been to train deep networks to either directly regress the pose from the image or to predict the 2D locations of 3D keypoints, from which the pose can be obtained using a PnP algorithm. I ...

IEEE2019

Predicting Modafinil-Treatment Response in Poststroke Fatigue Using Brain Morphometry and Functional Connectivity

Bénédicte Marie Maréchal

Background and Purpose- Poststroke fatigue affects a large proportion of stroke survivors and is associated with a poor quality of life. In a recent trial, modafinil was shown to be an effective agent in reducing poststroke fatigue; however, not all patien ...

LIPPINCOTT WILLIAMS & WILKINS2019

Assessment of water resource contamination by pesticides in vegetable-producing areas in Burkina Faso

Luiz Felippe De Alencastro, Edouard René Gilbert Lehmann, Jean-Jacques Stéphane Nfon Dibie, Morgan Fargues

This study proposes a comprehensive approach to investigate water resource contamination by pesticides under the specific climatic and hydrological conditions of the Sudano-Sahelian climate. Samples were collected from tradi- tional wells, boreholes, and a ...

2018

Optimal Rates for Spectral Algorithms with Least-Squares Regression over Hilbert Spaces

Volkan Cevher, Junhong Lin

In this paper, we study regression problems over a separable Hilbert space with the square loss, covering non-parametric regression over a reproducing kernel Hilbert space. We investigate a class of spectral/regularized algorithms, including ridge regressi ...

2020

Methodology And Convergence Rates For Functional Time Series Regression

Victor Panaretos, Tung Huy Pham

The functional linear model extends the notion of linear regression to the case where the response and covariates are iid elements of an infinite-dimensional Hilbert space. The unknown to be estimated is a Hilbert-Schmidt operator, whose inverse is by defi ...

STATISTICA SINICA2018

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