| AUGMENTING PHYSICAL MODELS WITH DEEP NET-WORKS FOR COMPLEX DYNAMICS FORECASTING Y Yin, V Le Guen, J Dona, E de Bézenac, I Ayed, N Thome, P Gallinari | 164* | |
| Learning dynamical systems from partial observations I Ayed, E de Bézenac, A Pajot, J Brajard, P Gallinari arXiv preprint arXiv:1902.11136, 2019 | 97 | 2019 |
| 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 | 28 | 2021 |
| CycleGAN Through the Lens of (Dynamical) Optimal Transport E Bézenac, I Ayed, P Gallinari Joint European Conference on Machine Learning and Knowledge Discovery in …, 2021 | 25* | 2021 |
| Learning the spatio-temporal dynamics of physical processes from partial observations I Ayed, E De Bezenac, A Pajot, P Gallinari ICASSP 2020-2020 IEEE International Conference on Acoustics, Speech and …, 2020 | 25* | 2020 |
| 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 | 23 | 2022 |
| Ep-net: Learning cardiac electrophysiology models for physiology-based constraints in data-driven predictions I Ayed, N Cedilnik, P Gallinari, M Sermesant Functional Imaging and Modeling of the Heart: 10th International Conference …, 2019 | 16 | 2019 |
| EP-Net 2.0: Out-of-domain generalisation for deep learning models of cardiac electrophysiology V Kashtanova, I Ayed, N Cedilnik, P Gallinari, M Sermesant International Conference on Functional Imaging and Modeling of the Heart …, 2021 | 15 | 2021 |
| A Principle of Least Action for the Training of Neural Networks S Karkar, I Ayed, E de Bézenac, P Gallinari ECML 2020, 2020 | 13 | 2020 |
| Deep Learning for Model Correction in Cardiac Electrophysiological Imaging V Kashtanova, I Ayed, A Arrieula, M Potse, P Gallinari, M Sermesant International Conference on Medical Imaging with Deep Learning, 665-675, 2022 | 11* | 2022 |
| Learning dynamical systems from partial observations. CoRR abs/1902.11136 (2019) I Ayed, E de Bézenac, A Pajot, J Brajard, P Gallinari | 5 | 1902 |
| Simultaneous data assimilation and cardiac electrophysiology model correction using differentiable physics and deep learning V Kashtanova, M Pop, I Ayed, P Gallinari, M Sermesant Interface Focus 13 (6), 20230043, 2023 | 4 | 2023 |
| Module-wise Training of Neural Networks via the Minimizing Movement Scheme S Karkar, I Ayed, E de Bézenac, P Gallinari Thirty-seventh Conference on Neural Information Processing Systems (NeurIPS …, 2023 | 2 | 2023 |
| Modelling spatiotemporal dynamics from Earth observation data with neural differential equations I Ayed, E de Bézenac, A Pajot, P Gallinari Machine Learning 111 (6), 2349-2380, 2022 | 2 | 2022 |
| 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 |
| Neural Models for Learning Real World Dynamics and the Neural Dynamics of Learning I Ayed Sorbonne université, 2022 | | 2022 |
| Learning Real World Dynamics with Neural Models and the Neural Dynamics of Learning I Ayed | | |
| LEADS: Learning Dynamical Systems that Generalize Across Environments Supplemental Material Y Yin, I Ayed, E de Bézenac, N Baskiotis, P Gallinari | | |
Patrick GallinariProfessor Sorbonne University / Criteo AI LabVerified email at sorbonne-universite.fr
