Variance Based Samples Weighting for Supervised Deep Learning. - Paul Novello; Gaël Poëtte; David Lugato; Pietro Marco Congedo (CoRR, 2021)

Making Sense of Dependence: Efficient Black-box Explanations Using Dependence Measure. - Paul Novello; Thomas Fel; David Vigouroux (NeurIPS, 2022)

Robust One-Class Classification with Signed Distance Function using 1-Lipschitz Neural Networks. - Louis Béthune; Paul Novello; Guillaume Coiffier; Thibaut Boissin; Mathieu Serrurier; Quentin Vincenot; Andres Troya-Galvis (ICML, 2023)

Unlocking Feature Visualization for Deep Network with MAgnitude Constrained Optimization. - Thomas Fel; Thibaut Boissin; Victor Boutin; Agustin Picard; Paul Novello; Julien Colin; Drew Linsley; Tom Rousseau; Rémi Cadène; Lore Goetschalckx; Laurent Gardes; Thomas Serre (NeurIPS, 2023)

Unlocking Feature Visualization for Deeper Networks with MAgnitude Constrained Optimization. - Thomas Fel; Thibaut Boissin; Victor Boutin; Agustin Martin Picard; Paul Novello; Julien Colin; Drew Linsley; Tom Rousseau; Rémi Cadène; Laurent Gardes; Thomas Serre (CoRR, 2023)

GROOD: GRadient-aware Out-Of-Distribution detection in interpolated manifolds. - Mostafa ElAraby; Sabyasachi Sahoo; Yann Pequignot; Paul Novello; Liam Paull (CoRR, 2023)

Goal-Oriented Sensitivity Analysis of Hyperparameters in Deep Learning. - Paul Novello; Gaël Poëtte; David Lugato; Pietro Marco Congedo (J. Sci. Comput., 2023)

Out-of-Distribution Detection Should Use Conformal Prediction (and Vice-versa?). - Paul Novello; Joseba Dalmau; Léo Andéol (CoRR, 2024)

Improving Out-of-Distribution Detection by Combining Existing Post-hoc Methods. - Paul Novello; Yannick Prudent; Joseba Dalmau; Corentin Friedrich; Yann Pequignot (CoRR, 2024)

Accelerating hypersonic reentry simulations using deep learning-based hybridization (with guarantees). - Paul Novello; Gaël Poëtte; David Lugato; Simon Peluchon; Pietro Marco Congedo (J. Comput. Phys., 2024)