| Goal-oriented sensitivity analysis of hyperparameters in deep learning P Novello, G Poëtte, D Lugato, PM Congedo Journal of Scientific Computing 94 (3), 45, 2023 | 6* | 2023 |
| Making Sense of Dependence: Efficient Black-box Explanations Using Dependence Measure P Novello, T Fel, D Vigouroux Advances in Neural Information Processing Systems (Neurips 2022), 2022 | 5 | 2022 |
| Accelerating hypersonic reentry simulations using deep learning-based hybridization (with guarantees) P Novello, G Poëtte, D Lugato, S Peluchon, PM Congedo arXiv preprint arXiv:2209.13434, 2022 | 1 | 2022 |
| Leveraging local variation in data: sampling and weighting schemes for supervised deep learning. P Novello, G Poëtte, D Lugato, PM Congedo Journal of Machine Learning for Modeling and Computing 3 (1), 2022 | 1* | 2022 |
| An analogy between solving Partial Differential Equations with Monte-Carlo schemes and the Optimisation process in Machine Learning (and few illustrations of its benefits) G Poëtte, D Lugato, P Novello | 1 | 2021 |
| Robust One-Class Classification with Signed Distance Function using 1-Lipschitz Neural Networks L Béthune, P Novello, T Boissin, G Coiffier, M Serrurier, Q Vincenot, ... International Conference on Machine Learning (ICML 2023), 2023 | | 2023 |
| Combining supervised deep learning and scientific computing: some contributions and application to computational fluid dynamics P Novello Ecole Polytechnique, Institut polytechnique de Paris, 2022 | | 2022 |
| A Taylor Based Sampling Scheme for Machine Learning in Computational Physics P Novello, G Poëtte, D Lugato, P Congedo Second Workshop on Machine Learning and the Physical Sciences (NeurIPS 2019), 2021 | | 2021 |
| Uncertainty analysis as an ally for deep-learning-based hybridiza-tion of simulation codes P Novello | | |
