Publications by Pierre Bruno Baqué

Related publications (9)

Mean-Field methods for Structured Deep-Learning in Computer Vision

In recent years, Machine Learning based Computer Vision techniques made impressive progress. These algorithms proved particularly efficient for image classification or detection of isolated objects. From a probabilistic perspective, these methods can predi ...

EPFL2018

Shape optimisation of technical devices via gradient descent using convolutional neural network proxies

Pascal Fua, François Fleuret, Pierre Bruno Baqué

The present invention generally relates to a method, a system and a computer program for shape optimisation of a technical device adapted to be exposed to a fluid flowing around a contour of said device with respect to its fluid dynamic parameters. In orde ...

2019

Geodesic Convolutional Neural Network Characterization of Macro-Porous Latent Thermal Energy Storage

Pascal Fua, Sophia Haussener, Pierre Bruno Baqué, Nithin Mallya, Pierre Mathieu Yvernay

High-temperature latent heat thermal energy storage with metallic alloy phase change materials (PCMs) utilize the high latent heat and high thermal conductivity to gain a competitive edge over existing sensible and latent storage technologies. Novel macrop ...

2023

HybridSDF: Combining Deep Implicit Shapes and Geometric Primitives for 3D Shape Representation and Manipulation

Pascal Fua, Pierre Bruno Baqué, Subeesh Vasu, Nicolas Talabot

Deep implicit surfaces excel at modeling generic shapes but do not always capture the regularities present in manufactured objects, which is something simple geometric primitives are particularly good at. In this paper, we propose a representation combinin ...

2022

Masksembles for Uncertainty Estimation

Pascal Fua, Timur Bagautdinov, Pierre Bruno Baqué, Nikita Durasov

Deep neural networks have amply demonstrated their prowess but estimating the reliability of their predictions remains challenging. Deep Ensembles are widely considered as being one of the best methods for generating uncertainty estimates but are very expe ...

2021

Principled Parallel Mean-Field Inference for Discrete Random Fields

Pascal Fua, François Fleuret, Timur Bagautdinov, Pierre Bruno Baqué

Mean-field variational inference is one of the most popular approaches to inference in discrete random fields. Standard mean-field optimization is based on coordinate descent and in many situations can be impractical. Thus, in practice, various parallel te ...

2016

Geodesic Convolutional Shape Optimization

Pascal Fua, François Fleuret, Edoardo Remelli, Pierre Bruno Baqué

Aerodynamic shape optimization has many industrial applications. Existing methods, however, are so computationally demanding that typical engineering practices are to either simply try a limited number of hand-designed shapes or restrict oneself to shapes ...

2018

DeepMesh: Differentiable Iso-Surface Extraction

Pascal Fua, Benoît Alain René Guillard, Edoardo Remelli, Timur Bagautdinov, Pierre Bruno Baqué, Pierre Mathieu Yvernay

2024

MeshSDF: Differentiable Iso-Surface Extraction

Pascal Fua, Benoît Alain René Guillard, Edoardo Remelli, Timur Bagautdinov, Pierre Bruno Baqué

2020