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Emmanuel de Bézenac
Emmanuel de Bézenac
在 sam.math.ethz.ch 的电子邮件经过验证
标题
引用次数
引用次数
年份
Deep Learning for Physical Processes: Incorporating Prior Scientific Knowledge
E de Bézenac, A Pajot, P Gallinari
arXiv preprint arXiv:1711.07970, 2017
3592017
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
1602021
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
1152020
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
342020
Unsupervised adversarial image reconstruction
A Pajot, E De Bézenac, P Gallinari
International conference on learning representations, 2019
332019
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
262021
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
202022
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
202021
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
132021
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
112024
Unsupervised adversarial image inpainting
A Pajot, E de Bezenac, P Gallinari
arXiv preprint arXiv:1912.12164, 2019
112019
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
102023
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
42023
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
22024
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
12022
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
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