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Emmanuel de Bézenac
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2020 – today
- 2024
- [c18]Tim De Ryck, Florent Bonnet
, Siddhartha Mishra, Emmanuel de Bézenac:
An operator preconditioning perspective on training in physics-informed machine learning. ICLR 2024 - [c17]Levi E. Lingsch, Mike Yan Michelis, Emmanuel de Bézenac, Sirani M. Perera, Robert K. Katzschmann, Siddhartha Mishra:
Beyond Regular Grids: Fourier-Based Neural Operators on Arbitrary Domains. ICML 2024 - [i17]Maximilian Herde, Bogdan Raonic, Tobias Rohner, Roger Käppeli, Roberto Molinaro, Emmanuel de Bézenac, Siddhartha Mishra:
Poseidon: Efficient Foundation Models for PDEs. CoRR abs/2405.19101 (2024) - [i16]Lise Le Boudec, Emmanuel de Bézenac, Louis Serrano, Ramon Daniel Regueiro-Espino, Yuan Yin, Patrick Gallinari:
Learning a Neural Solver for Parametric PDE to Enhance Physics-Informed Methods. CoRR abs/2410.06820 (2024) - 2023
- [c16]Francesca Bartolucci, Emmanuel de Bézenac, Bogdan Raonic, Roberto Molinaro, Siddhartha Mishra, Rima Alaifari:
Representation Equivalent Neural Operators: a Framework for Alias-free Operator Learning. NeurIPS 2023 - [c15]Jean-Yves Franceschi, Mike Gartrell, Ludovic Dos Santos, Thibaut Issenhuth, Emmanuel de Bézenac, Mickaël Chen, Alain Rakotomamonjy:
Unifying GANs and Score-Based Diffusion as Generative Particle Models. NeurIPS 2023 - [c14]Skander Karkar, Ibrahim Ayed, Emmanuel de Bézenac, Patrick Gallinari:
Module-wise Training of Neural Networks via the Minimizing Movement Scheme. NeurIPS 2023 - [c13]Bogdan Raonic, Roberto Molinaro, Tim De Ryck, Tobias Rohner, Francesca Bartolucci, Rima Alaifari, Siddhartha Mishra, Emmanuel de Bézenac:
Convolutional Neural Operators for robust and accurate learning of PDEs. NeurIPS 2023 - [i15]Bogdan Raonic, Roberto Molinaro, Tobias Rohner, Siddhartha Mishra, Emmanuel de Bézenac:
Convolutional Neural Operators. CoRR abs/2302.01178 (2023) - [i14]Jean-Yves Franceschi, Mike Gartrell, Ludovic Dos Santos, Thibaut Issenhuth, Emmanuel de Bézenac, Mickaël Chen, Alain Rakotomamonjy:
Unifying GANs and Score-Based Diffusion as Generative Particle Models. CoRR abs/2305.16150 (2023) - [i13]Francesca Bartolucci, Emmanuel de Bézenac, Bogdan Raonic, Roberto Molinaro, Siddhartha Mishra, Rima Alaifari:
Are Neural Operators Really Neural Operators? Frame Theory Meets Operator Learning. CoRR abs/2305.19913 (2023) - [i12]Skander Karkar, Ibrahim Ayed, Emmanuel de Bézenac, Patrick Gallinari:
Module-wise Training of Neural Networks via the Minimizing Movement Scheme. CoRR abs/2309.17357 (2023) - [i11]Tim De Ryck, Florent Bonnet, Siddhartha Mishra, Emmanuel de Bézenac:
An operator preconditioning perspective on training in physics-informed machine learning. CoRR abs/2310.05801 (2023) - 2022
- [j1]Ibrahim Ayed
Modelling spatiotemporal dynamics from Earth observation data with neural differential equations. Mach. Learn. 111(6): 2349-2380 (2022) - [c12]Matthieu Kirchmeyer, Alain Rakotomamonjy, Emmanuel de Bézenac, Patrick Gallinari:
Mapping conditional distributions for domain adaptation under generalized target shift. ICLR 2022 - [c11]Jean-Yves Franceschi, Emmanuel de Bézenac, Ibrahim Ayed, Mickaël Chen, Sylvain Lamprier, Patrick Gallinari:
A Neural Tangent Kernel Perspective of GANs. ICML 2022: 6660-6704 - [i10]Skander Karkar, Ibrahim Ayed, Emmanuel de Bézenac, Patrick Gallinari:
Module-wise Training of Residual Networks via the Minimizing Movement Scheme. CoRR abs/2210.00949 (2022) - 2021
- [b1]Emmanuel de Bézenac:
Modeling Physical Processes with Deep Learning: A Dynamical Systems Approach. (Modélisation de Processus Physique avec de l'Apprentissage Profond). Sorbonne University, France, 2021 - [c10]Yuan Yin, Ibrahim Ayed, Emmanuel de Bézenac, Patrick Gallinari:
Learning Dynamical Systems across Environments. AAAI Spring Symposium: MLPS 2021 - [c9]Yuan Yin, Vincent Le Guen, Jérémie Donà, Emmanuel de Bézenac, Ibrahim Ayed, Nicolas Thome, Patrick Gallinari:
Augmenting Physical Models with Deep Networks for Complex Dynamics Forecasting. ICLR 2021 - [c8]Yuan Yin, Ibrahim Ayed, Emmanuel de Bézenac, Nicolas Baskiotis, Patrick Gallinari:
LEADS: Learning Dynamical Systems that Generalize Across Environments. NeurIPS 2021: 7561-7573 - [c7]Emmanuel de Bézenac, Ibrahim Ayed, Patrick Gallinari:
CycleGAN Through the Lens of (Dynamical) Optimal Transport. ECML/PKDD (2) 2021: 132-147 - [i9]Yuan Yin
LEADS: Learning Dynamical Systems that Generalize Across Environments. CoRR abs/2106.04546 (2021) - [i8]Jean-Yves Franceschi, Emmanuel de Bézenac, Ibrahim Ayed, Mickaël Chen, Sylvain Lamprier, Patrick Gallinari:
A Neural Tangent Kernel Perspective of GANs. CoRR abs/2106.05566 (2021) - [i7]Matthieu Kirchmeyer, Alain Rakotomamonjy, Emmanuel de Bézenac, Patrick Gallinari:
Mapping conditional distributions for domain adaptation under generalized target shift. CoRR abs/2110.15057 (2021) - 2020
- [c6]Ibrahim Ayed, Emmanuel de Bézenac, Arthur Pajot, Patrick Gallinari:
Learning the Spatio-Temporal Dynamics of Physical Processes from Partial Observations. ICASSP 2020: 3232-3236 - [c5]Emmanuel de Bézenac, Syama Sundar Rangapuram, Konstantinos Benidis, Michael Bohlke-Schneider, Richard Kurle, Lorenzo Stella, Hilaf Hasson, Patrick Gallinari, Tim Januschowski:
Normalizing Kalman Filters for Multivariate Time Series Analysis. NeurIPS 2020 - [c4]Richard Kurle, Syama Sundar Rangapuram, Emmanuel de Bézenac, Stephan Günnemann, Jan Gasthaus:
Deep Rao-Blackwellised Particle Filters for Time Series Forecasting. NeurIPS 2020 - [c3]Skander Karkar, Ibrahim Ayed, Emmanuel de Bézenac, Patrick Gallinari:
A Principle of Least Action for the Training of Neural Networks. ECML/PKDD (2) 2020: 101-117 - [i6]Skander Karkar, Ibrahim Ayed, Emmanuel de Bézenac, Patrick Gallinari:
A Principle of Least Action for the Training of Neural Networks. CoRR abs/2009.08372 (2020) - [i5]Vincent Le Guen, Yuan Yin
Augmenting Physical Models with Deep Networks for Complex Dynamics Forecasting. CoRR abs/2010.04456 (2020)
2010 – 2019
- 2019
- [c2]Arthur Pajot, Emmanuel de Bézenac, Patrick Gallinari:
Unsupervised Adversarial Image Reconstruction. ICLR (Poster) 2019 - [i4]Ibrahim Ayed, Emmanuel de Bézenac, Arthur Pajot, Julien Brajard, Patrick Gallinari:
Learning Dynamical Systems from Partial Observations. CoRR abs/1902.11136 (2019) - [i3]Emmanuel de Bézenac, Ibrahim Ayed, Patrick Gallinari:
Optimal Unsupervised Domain Translation. CoRR abs/1906.01292 (2019) - [i2]Arthur Pajot, Emmanuel de Bézenac, Patrick Gallinari:
Unsupervised Adversarial Image Inpainting. CoRR abs/1912.12164 (2019) - 2018
- [c1]Emmanuel de Bézenac, Arthur Pajot, Patrick Gallinari:
Deep Learning for Physical Processes: Incorporating Prior Scientific Knowledge. ICLR (Poster) 2018 - 2017
- [i1]Emmanuel de Bézenac, Arthur Pajot, Patrick Gallinari:
Deep Learning for Physical Processes: Incorporating Prior Scientific Knowledge. CoRR abs/1711.07970 (2017)
Coauthor Index
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last updated on 2024-11-20 21:57 CET by the dblp team
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