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Emmanuel de Bézenac
Emmanuel de Bézenac
Unknown affiliation
Verified email at lip6.fr
Title
Cited by
Cited by
Year
Deep Learning for Physical Processes: Incorporating Prior Scientific Knowledge
E de Bézenac, A Pajot, P Gallinari
arXiv preprint arXiv:1711.07970, 2017
1852017
Learning dynamical systems from partial observations
I Ayed, E de Bézenac, A Pajot, J Brajard, P Gallinari
arXiv preprint arXiv:1902.11136, 2019
512019
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
292021
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
282020
Unsupervised adversarial image reconstruction
A Pajot, E De Bézenac, P Gallinari
International conference on learning representations, 2018
252018
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
162020
Optimal unsupervised domain translation
E de Bézenac, I Ayed, P Gallinari
arXiv preprint arXiv:1906.01292, 2019
112019
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
92020
A neural tangent kernel perspective of gans
JY Franceschi, E de Bézenac, I Ayed, M Chen, S Lamprier, P Gallinari
arXiv preprint arXiv:2106.05566, 2021
52021
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
32021
A principle of least action for the training of neural networks
S Karkar, I Ayed, E Bézenac, P Gallinari
Joint European Conference on Machine Learning and Knowledge Discovery in …, 2020
32020
Learning Partially Observed PDE Dynamics with Neural Networks
I Ayed, E de Bézenac, A Pajot, P Gallinari
32018
Unsupervised adversarial image inpainting
A Pajot, E de Bezenac, P Gallinari
arXiv preprint arXiv:1912.12164, 2019
22019
Towards a hybrid approach to physical process modeling
E De Bézenac, A Pajot, P Gallinari
Technical report, 2017
22017
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, 2021
12021
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
12021
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
Modeling physical processes with deep learning: a dynamical systems approach
E Bézenac
Sorbonne université, 2021
2021
A NEURAL TANGENT KERNEL PERSPECTIVE OF GANS
I GdR, JY Franceschi, E de Bézenac, I Ayed, M Chen, S Lamprier, ...
2021
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