| Learning dynamical systems from partial observations I Ayed, E de Bézenac, A Pajot, J Brajard, P Gallinari arXiv preprint arXiv:1902.11136, 2019 | 55 | 2019 |
| 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 | 41* | |
| 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 | 15* | 2021 |
| Learning the Spatio-Temporal Dynamics of Physical Processes from Partial Observations I Ayed, E de Bézenac, A Pajot, P Gallinari ICASSP 2020-2020 IEEE International Conference on Acoustics, Speech and …, 2020 | 15* | 2020 |
| EP-Net: Learning Cardiac Electrophysiology Models for Physiology-based Constraints in Data-Driven Predictions I Ayed, N Cedilnik, P Gallinari, M Sermesant International Conference on Functional Imaging and Modeling of the Heart, 55-63, 2019 | 8 | 2019 |
| 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 | 6 | 2022 |
| 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 | 3 | 2021 |
| 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 | 3 | 2021 |
| A Principle of Least Action for the Training of Neural Networks S Karkar, I Ayed, E de Bézenac, P Gallinari ECML 2020, 2020 | 3 | 2020 |
| Deep Learning for Model Correction in Cardiac Electrophysiological Imaging V Kashtanova, I Ayed, A Arrieula, M Potse, P Gallinari, M Sermesant MIDL 2022-Medical Imaging with Deep Learning, 2022 | | 2022 |
| Modelling spatiotemporal dynamics from Earth observation data with neural differential equations I Ayed, E de Bézenac, A Pajot, P Gallinari Machine Learning, 1-32, 2022 | | 2022 |
| Block-wise Training of Residual Networks via the Minimizing Movement Scheme S Karkar, I Ayed, E de Bézenac, P Gallinari | | 2022 |
Patrick GallinariProfessor Sorbonne University / Criteo AI LabGeverifieerd e-mailadres voor sorbonne-universite.fr
