Publications associées à Experimental study on prediction model for maximum rebound ratio

OASIS: Optimisation-based Activity Scheduling with Integrated Simultaneous choice dimensions

Michel Bierlaire, Timothy Michael Hillel, Janody Pougala

Activity-based models offer the potential of a far deeper understanding of daily mobility behaviour than trip-based models. However, activity-based models used both in research and practice have often relied on applying sequential choice models between sub ...

2023

Near-bed stratification controls bottom hypoxia in ice-covered alpine lakes

Damien Bouffard, Camille Roland Marie Minaudo, Tomy Doda, Hugo Nicolás Ulloa Sánchez, Hannah Elisa Chmiel, Janine Rüegg, Nicolas Thomas Georges Escoffier, Pascal Perolo

In ice-covered lakes, near-bottom oxygen concentration decreases for most of the wintertime, sometimes down to the point that bottom waters become hypoxic. Studies insofar have reached divergent conclusions on whether climate change limits or reinforces th ...

American Society of Limnology and Oceanography2023

Failure and success of the spectral bias prediction for Laplace Kernel Ridge Regression: the case of low-dimensional data

Matthieu Wyart, Umberto Maria Tomasini, Antonio Sclocchi

Recently, several theories including the replica method made predictions for the generalization error of Kernel Ridge Regression. In some regimes, they predict that the method has a 'spectral bias': decomposing the true function f* on the eigenbasis of the ...

JMLR-JOURNAL MACHINE LEARNING RESEARCH2022

VL4Pose: Active Learning Through Out-Of-Distribution Detection For Pose Estimation

Alexandre Massoud Alahi, Megh Hiren Shukla

Advances in computing have enabled widespread access to pose estimation, creating new sources of data streams. Unlike mock set-ups for data collection, tapping into these data streams through on-device active learning allows us to directly sample from the ...

BMVA Press2022

SafeAMC: Adversarial training for robust modulation classification models

Pascal Frossard, Javier Alejandro Maroto Morales

In communication systems, there are many tasks, like modulation classification, for which Deep Neural Networks (DNNs) have obtained promising performance. However, these models have been shown to be susceptible to adversarial perturbations, namely impercep ...

IEEE2022

Numerical Weather Prediction and Artificial Neural Network Coupling for Wind Energy Forecast

Fernando Porté Agel, Jiannong Fang, Lorenzo Donadio

In the past two decades, wind energy has been under fast development worldwide. The dramatic increase of wind power penetration in electricity production has posed a big challenge to grid integration due to the high uncertainty of wind power. Accurate real ...

2021

Prediction Error–Based Parameter Estimation for Multi-Region MFD Networks

Nikolaos Geroliminis, Isik Ilber Sirmatel

City-level traffic management remains a challenging problem. Model predictive perimeter control approaches employing macroscopic fundamental diagram (MFD) based models of large-scale urban road traffic represent a high-performance solution with substantial ...

2020

On flow duration curve modelling in Alpine catchments

Ana Clara Pereira Barbosa Santos

Mountain regions are considered to be the natural "water towers" of the world due to their importance as sources of many rivers. Reliable tools to estimate the availability and variability of streamflows in such regions are still rare. In this context, the ...

EPFL2019

Sequential model identification of reaction systems-The missing path between the incremental and simultaneous approaches

Dominique Bonvin, Julien Léo Billeter, Sriniketh Srinivasan

Modeling chemical reaction systems is an important but complex task. The identified kinetic model must be able to explain all the underlying rate processes such as chemical reactions and heat and mass transfers. Traditionally, the modeling task is carried ...

2019

Fast and Accurate Uncertainty Estimation in Chemical Machine Learning

Michele Ceriotti, Félix Benedito Clément Musil, Michael John Willatt, Mikhail Langovoy

We present a scheme to obtain an inexpensive and reliable estimate of the uncertainty associated with the predictions of a machine-learning model of atomic and molecular properties. The scheme is based on resampling, with multiple models being generated ba ...

2019