Publications associées à Maximum a posteriori

Recovering Static and Time-Varying Communities Using Persistent Edges

This article focuses on spectral methods for recovering communities in temporal networks. In the case of fixed communities, spectral clustering on the simple time-aggregated graph (i.e., the weighted graph formed by the sum of the interactions over all tem ...

Ieee Computer Soc2024

A Shape Derivative Approach to Domain Simplification

Annalisa Buffa, Jochen Peter Hinz, Ondine Gabrielle Chanon, Alessandra Arrigoni

The objective of this study is to address the difficulty of simplifying the geometric model in which a differential problem is formulated, also called defeaturing, while simultaneously ensuring that the accuracy of the solution is maintained under control. ...

Oxford2023

Analysis-aware defeaturing of complex geometries with Neumann features

Pablo Antolin Sanchez, Ondine Gabrielle Chanon

Local modifications of a computational domain are often performed in order to simplify the meshing process and to reduce computational costs and memory requirements. However, removing geometrical features of a domain often introduces a non-negligible error ...

Hoboken2023

ReLU Neural Network Galerkin BEM

Fernando José Henriquez Barraza

We introduce Neural Network (NN for short) approximation architectures for the numerical solution of Boundary Integral Equations (BIEs for short). We exemplify the proposed NN approach for the boundary reduction of the potential problem in two spatial dime ...

SPRINGER/PLENUM PUBLISHERS2023

An equilibrated flux a posteriori error estimator for defeaturing problems

Annalisa Buffa, Denise Grappein, Rafael Vazquez Hernandez, Ondine Gabrielle Chanon

An a posteriori error estimator based on an equilibrated flux reconstruction is proposed for defeaturing problems in the context of finite element discretizations. Defeaturing consists in the simplification of a geometry by removing features that are consi ...

2023

An Unbiased Method of Measuring the Ratio of Two Data Sets

Jean-Paul Richard Kneib, Huanyuan Shan

In certain cases of astronomical data analysis, the meaningful physical quantity to extract is the ratio R between two data sets. Examples include the lensing ratio, the interloper rate in spectroscopic redshift samples, and the decay rate of gravitational ...

IOP Publishing Ltd2023

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

On double-descent in uncertainty quantification in overparametrized models

Florent Gérard Krzakala, Lenka Zdeborová, Lucas Andry Clarte, Bruno Loureiro, Bruno Loureiro

Uncertainty quantification is a central challenge in reliable and trustworthy machine learning. Naive measures such as last-layer scores are well-known to yield overconfident estimates in the context of overparametrized neural networks. Several methods, ra ...

PMLR Proceedings of Machine Learning Research2023

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Maximum a posteriori

OASIS: An integrated optimisation framework for activity scheduling

Recovering Static and Time-Varying Communities Using Persistent Edges

A Shape Derivative Approach to Domain Simplification

Analysis-aware defeaturing of complex geometries with Neumann features

ReLU Neural Network Galerkin BEM

Divergent X-ray tomography reconstruction and optimisation

An equilibrated flux a posteriori error estimator for defeaturing problems

An Unbiased Method of Measuring the Ratio of Two Data Sets

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

On double-descent in uncertainty quantification in overparametrized models

Graph Chatbot

Chattez avec Graph Search

OASIS: An integrated optimisation framework for activity scheduling

ReLU Neural Network Galerkin BEM

An equilibrated flux a posteriori error estimator for defeaturing problems

Recovering Static and Time-Varying Communities Using Persistent Edges

Analysis-aware defeaturing of complex geometries with Neumann features

An Unbiased Method of Measuring the Ratio of Two Data Sets

A Shape Derivative Approach to Domain Simplification

Divergent X-ray tomography reconstruction and optimisation

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

On double-descent in uncertainty quantification in overparametrized models