30 citations · 76 across the 19 of their papers we have counts for
21 papers
OneBatchPAM: A Fast and Frugal K-Medoids Algorithm
Antoine de Mathelin, Nicolas Enrique Cecchi, François Deheeger +2
This paper proposes a novel k-medoids approximation algorithm to handle large-scale datasets with reasonable computational time and memory complexity. We develop a local-search alg…
Deep Out-of-Distribution Uncertainty Quantification via Weight Entropy Maximization
Antoine de Mathelin, François Deheeger, Mathilde Mougeot +1
This paper deals with uncertainty quantification and out-of-distribution detection in deep learning using Bayesian and ensemble methods. It proposes a practical solution to the lac…
A Meta-Generation framework for Industrial System Generation
Fouad Oubari, Raphael Meunier, Rodrigue Décatoire +1
Generative design is an increasingly important tool in the industrial world. It allows the designers and engineers to easily explore vast ranges of design options, providing a chea…
Deep Anti-Regularized Ensembles provide reliable out-of-distribution uncertainty quantification
Antoine de Mathelin, Francois Deheeger, Mathilde Mougeot +1
We consider the problem of uncertainty quantification in high dimensional regression and classification for which deep ensemble have proven to be promising methods. Recent observat…
Fixed-budget online adaptive learning for physics-informed neural networks. Towards parameterized problem inference
Thi Nguyen Khoa Nguyen, Thibault Dairay, Raphaël Meunier +2
Physics-Informed Neural Networks (PINNs) have gained much attention in various fields of engineering thanks to their capability of incorporating physical laws into the models. PINN…
To tree or not to tree? Assessing the impact of smoothing the decision boundaries
Anthea Mérida, Argyris Kalogeratos, Mathilde Mougeot
When analyzing a dataset, it can be useful to assess how smooth the decision boundaries need to be for a model to better fit the data. This paper addresses this question by proposi…