| Deep Learning for Physical Processes: Incorporating Prior Scientific Knowledge E de Bézenac, A Pajot, P Gallinari arXiv preprint arXiv:1711.07970, 2017 | 359 | 2017 |
| Augmenting physical models with deep networks for complex dynamics forecasting Y Yin, V Le Guen, J Dona, E de Bézenac, I Ayed, N Thome, P Gallinari Journal of Statistical Mechanics: Theory and Experiment 2021 (12), 124012, 2021 | 160 | 2021 |
| Learning dynamical systems from partial observations I Ayed, E de Bézenac, A Pajot, J Brajard, P Gallinari arXiv preprint arXiv:1902.11136, 2019 | 118* | 2019 |
| Normalizing kalman filters for multivariate time series analysis E de Bézenac, SS Rangapuram, K Benidis, M Bohlke-Schneider, R Kurle, ... Advances in Neural Information Processing Systems 33, 2995-3007, 2020 | 115 | 2020 |
| Convolutional neural operators for robust and accurate learning of PDEs B Raonic, R Molinaro, T De Ryck, T Rohner, F Bartolucci, R Alaifari, ... Advances in Neural Information Processing Systems 36, 2024 | 69* | 2024 |
| Deep rao-blackwellised particle filters for time series forecasting R Kurle, SS Rangapuram, E de Bézenac, S Günnemann, J Gasthaus Advances in Neural Information Processing Systems 33, 15371-15382, 2020 | 34 | 2020 |
| Unsupervised adversarial image reconstruction A Pajot, E De Bézenac, P Gallinari International conference on learning representations, 2019 | 33 | 2019 |
| Representation equivalent neural operators: a framework for alias-free operator learning F Bartolucci, E de Bézenac, B Raonic, R Molinaro, S Mishra, R Alaifari Advances in Neural Information Processing Systems 36, 2024 | 32* | 2024 |
| LEADS: Learning dynamical systems that generalize across environments Y Yin, I Ayed, E de Bézenac, N Baskiotis, P Gallinari Advances in Neural Information Processing Systems 34, 7561-7573, 2021 | 26 | 2021 |
| Cyclegan through the lens of (dynamical) optimal transport E de Bézenac, I Ayed, P Gallinari Machine Learning and Knowledge Discovery in Databases. Research Track …, 2021 | 25* | 2021 |
| A neural tangent kernel perspective of GANs JY Franceschi, E De Bézenac, I Ayed, M Chen, S Lamprier, P Gallinari International Conference on Machine Learning, 6660-6704, 2022 | 20 | 2022 |
| Mapping conditional distributions for domain adaptation under generalized target shift M Kirchmeyer, A Rakotomamonjy, E de Bezenac, P Gallinari arXiv preprint arXiv:2110.15057, 2021 | 20 | 2021 |
| A principle of least action for the training of neural networks S Karkar, I Ayed, E Bézenac, P Gallinari Machine Learning and Knowledge Discovery in Databases: European Conference …, 2021 | 13 | 2021 |
| Unifying gans and score-based diffusion as generative particle models JY Franceschi, M Gartrell, L Dos Santos, T Issenhuth, E de Bézenac, ... Advances in Neural Information Processing Systems 36, 2024 | 11 | 2024 |
| Unsupervised adversarial image inpainting A Pajot, E de Bezenac, P Gallinari arXiv preprint arXiv:1912.12164, 2019 | 11 | 2019 |
| An operator preconditioning perspective on training in physics-informed machine learning T De Ryck, F Bonnet, S Mishra, E de Bézenac arXiv preprint arXiv:2310.05801, 2023 | 10 | 2023 |
| A structured matrix method for nonequispaced neural operators L Lingsch, M Michelis, SM Perera, RK Katzschmann, SA Mishra Preprint at https://doi. org/10.48550/arXiv 2305, 2023 | 4 | 2023 |
| Module-wise training of neural networks via the minimizing movement scheme S Karkar, I Ayed, E de Bézenac, P Gallinari Advances in Neural Information Processing Systems 36, 2024 | 2 | 2024 |
| Block-wise Training of Residual Networks via the Minimizing Movement Scheme S Karkar, I Ayed, E de Bézenac, P Gallinari 1st International Workshop on Practical Deep Learning in the Wild at 26th …, 2022 | 1 | 2022 |
| Poseidon: Efficient Foundation Models for PDEs M Herde, B Raonić, T Rohner, R Käppeli, R Molinaro, E de Bézenac, ... arXiv preprint arXiv:2405.19101, 2024 | | 2024 |
