7 citations · 11 across the 6 of their papers we have counts for
7 papers
Unlocking Feature Visualization for Deeper Networks with MAgnitude Constrained Optimization
Thomas Fel, Thibaut Boissin, Victor Boutin +9
Feature visualization has gained substantial popularity, particularly after the influential work by Olah et al. in 2017, which established it as a crucial tool for explainability.…
Robust One-Class Classification with Signed Distance Function using 1-Lipschitz Neural Networks
Louis Bethune, Paul Novello, Thibaut Boissin +4
We propose a new method, dubbed One Class Signed Distance Function (OCSDF), to perform One Class Classification (OCC) by provably learning the Signed Distance Function (SDF) to the…
Accelerating hypersonic reentry simulations using deep learning-based hybridization (with guarantees)
Paul Novello, Gaël Poëtte, David Lugato +2
In this paper, we are interested in the acceleration of numerical simulations. We focus on a hypersonic planetary reentry problem whose simulation involves coupling fluid dynamics…
Goal-Oriented Sensitivity Analysis of Hyperparameters in Deep Learning
Paul Novello, Gaël Poëtte, David Lugato +1
Tackling new machine learning problems with neural networks always means optimizing numerous hyperparameters that define their structure and strongly impact their performances. In…
Making Sense of Dependence: Efficient Black-box Explanations Using Dependence Measure
Paul Novello, Thomas Fel, David Vigouroux
This paper presents a new efficient black-box attribution method based on Hilbert-Schmidt Independence Criterion (HSIC), a dependence measure based on Reproducing Kernel Hilbert Sp…
A Taylor Based Sampling Scheme for Machine Learning in Computational Physics
Paul Novello, Gaël Poëtte, David Lugato +1
Machine Learning (ML) is increasingly used to construct surrogate models for physical simulations. We take advantage of the ability to generate data using numerical simulations pro…
Leveraging Local Variation in Data: Sampling and Weighting Schemes for Supervised Deep Learning
Paul Novello, Gaël Poëtte, David Lugato +1
In the context of supervised learning of a function by a neural network, we claim and empirically verify that the neural network yields better results when the distribution of the…