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Publications associées (28)

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Gaussian Process Regression for Materials and Molecules

Michele Ceriotti, David Mark Wilkins

We provide an introduction to Gaussian process regression (GPR) machinelearning methods in computational materials science and chemistry. The focus of the present review is on the regression of atomistic properties: in particular, on the construction of in ...

AMER CHEMICAL SOC2021

On Linear Interpolation in the Latent Space of Deep Generative Models

Quentin Christian Becker, Mike Yan Michelis

The underlying geometrical structure of the latent space in deep generative models is in most cases not Euclidean, which may lead to biases when comparing interpolation capabilities of two models. Smoothness and plausibility of linear interpolations in lat ...

2021

Eigendecomposition-Free Training of Deep Networks for Linear Least-Square Problems

Pascal Fua, Mathieu Salzmann, Zheng Dang, Kwang Moo Yi, Fei Wang, Yinlin Hu

Many classical Computer Vision problems, such as essential matrix computation and pose estimation from 3D to 2D correspondences, can be tackled by solving a linear least-square problem, which can be done by finding the eigenvector corresponding to the smal ...

IEEE COMPUTER SOC2021

A Sensor Fusion Approach to the Estimation of Instantaneous Velocity Using Single Wearable Sensor During Sprint

Kamiar Aminian, Frédéric Meyer, Salil Apte, Farzin Dadashi, Vincent Gremeaux

Power-Force-Velocity profile obtained during a sprint test is crucial for designing personalized training and evaluating injury risks. Estimation of instantaneous velocity is requisite for developing these profiles and the predominant method for this estim ...

2020

Design guidelines for Al-12%Si latent heat storage encapsulations to optimize performance and mitigate degradation

Sophia Haussener, Selmar Rudolf Binder

We provide detailed insight into the growth kinetics of intermetallic layers formed at the interface of molten Al-12%Si with cylindrical encapsulations of 316L stainless steel. This knowledge is relevant for the design and operation of high-temperature lat ...

ELSEVIER2020

Model-informed machine learning for multi-component T2 relaxometry

Jean-Philippe Thiran, Meritxell Bach Cuadra, Tobias Kober, Tom Hilbert, Erick Jorge Canales Rodriguez, Elda Fischi Gomez, Gian Franco Piredda, Thomas Yu, Cristina Granziera, Muhamed Barakovic

Recovering the distribution from multi-echo magnetic resonance (MR) signals is challenging but has high potential as it provides biomarkers characterizing the tissue micro-structure, such as the myelin water fraction (MWF). In this work, we propose to comb ...

2020

Fluorescence lifetime imaging with a megapixel SPAD camera and neural network lifetime estimation

Edoardo Charbon, Claudio Bruschini, Ming-Lo Wu, Kazuhiro Morimoto

Fluorescence lifetime imaging microscopy (FLIM) is a key technology that provides direct insight into cell metabolism, cell dynamics and protein activity. However, determining the lifetimes of different fluorescent proteins requires the detection of a rela ...

NATURE RESEARCH2020

Robust Biophysical Parameter Estimation with a Neural Network Enhanced Hamiltonian Markov Chain Monte Carlo Sampler

Jean-Philippe Thiran, Gabriel Girard, Erick Jorge Canales Rodriguez, Jonathan Rafael Patino Lopez, Marco Pizzolato, Thomas Yu

Probabilistic parameter estimation in model fitting runs the gamut from maximum likelihood or maximum a posteriori point estimates from optimization to Markov Chain Monte Carlo (MCMC) sampling. The latter, while more computationally intensive, generally pr ...

SPRINGER INTERNATIONAL PUBLISHING AG2019

Multiresolution Subdivision Snakes

Michaël Unser, Daniel Andreas Schmitter, Virginie Sophie Uhlmann, Anaïs Laure Marie-Thérèse Badoual

We present a new family of snakes that satisfy the property of multiresolution by exploiting subdivision schemes. We show in a generic way how to construct such snakes based on an admissible subdivision mask. We derive the necessary energy formulations and ...

2017

High-Quality Parallel-Ray x-Ray CT Back Projection Using Optimized Interpolation

Michaël Unser, Michael Thompson McCann

We propose a new, cost-efficient method for computing back projections in parallel-ray X-ray CT. Forward and back projections are the basis of almost all X-ray CT reconstruction methods, but computing these accurately is costly. In the special case of para ...

2017

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