Emmanuel de Bézenac

39 papers A* 14Misc 1Journal 18Unranked 5

Year	Rank	Type	Title / Venue / Authors
2025	A*	`conf`	Learning a Neural Solver for Parametric PDEs to Enhance Physics-Informed Methods. ICLR Lise Le Boudec, Emmanuel de Bézenac, Louis Serrano, Ramon Daniel Regueiro-Espino, Yuan Yin, Patrick Gallinari
2024	A*	`conf`	An operator preconditioning perspective on training in physics-informed machine learning. ICLR Tim De Ryck, Florent Bonnet, Siddhartha Mishra, Emmanuel de Bézenac
2024	A*	`conf`	Beyond Regular Grids: Fourier-Based Neural Operators on Arbitrary Domains. ICML Levi E. Lingsch, Mike Yan Michelis, Emmanuel de Bézenac, Sirani M. Perera, Robert K. Katzschmann, Siddhartha Mishra
2024	J	`jnl`	Learning a Neural Solver for Parametric PDE to Enhance Physics-Informed Methods. CoRR Lise Le Boudec, Emmanuel de Bézenac, Louis Serrano, Ramon Daniel Regueiro-Espino, Yuan Yin, Patrick Gallinari
2024	A*	`conf`	Poseidon: Efficient Foundation Models for PDEs. NeurIPS Maximilian Herde, Bogdan Raonic, Tobias Rohner, Roger Käppeli, Roberto Molinaro, Emmanuel de Bézenac, Siddhartha Mishra
2024	J	`jnl`	Poseidon: Efficient Foundation Models for PDEs. CoRR Maximilian Herde, Bogdan Raonic, Tobias Rohner, Roger Käppeli, Roberto Molinaro, Emmanuel de Bézenac, Siddhartha Mishra
2023	J	`jnl`	An operator preconditioning perspective on training in physics-informed machine learning. CoRR Tim De Ryck, Florent Bonnet, Siddhartha Mishra, Emmanuel de Bézenac
2023	J	`jnl`	Are Neural Operators Really Neural Operators? Frame Theory Meets Operator Learning. CoRR Francesca Bartolucci, Emmanuel de Bézenac, Bogdan Raonic, Roberto Molinaro, Siddhartha Mishra, Rima Alaifari
2023	A*	`conf`	Convolutional Neural Operators for robust and accurate learning of PDEs. NeurIPS Bogdan Raonic, Roberto Molinaro, Tim De Ryck, Tobias Rohner, Francesca Bartolucci, Rima Alaifari, Siddhartha Mishra, Emmanuel de Bézenac
2023	J	`jnl`	Convolutional Neural Operators. CoRR Bogdan Raonic, Roberto Molinaro, Tobias Rohner, Siddhartha Mishra, Emmanuel de Bézenac
2023	A*	`conf`	Module-wise Training of Neural Networks via the Minimizing Movement Scheme. NeurIPS Skander Karkar, Ibrahim Ayed, Emmanuel de Bézenac, Patrick Gallinari
2023	J	`jnl`	Module-wise Training of Neural Networks via the Minimizing Movement Scheme. CoRR Skander Karkar, Ibrahim Ayed, Emmanuel de Bézenac, Patrick Gallinari
2023	A*	`conf`	Representation Equivalent Neural Operators: a Framework for Alias-free Operator Learning. NeurIPS Francesca Bartolucci, Emmanuel de Bézenac, Bogdan Raonic, Roberto Molinaro, Siddhartha Mishra, Rima Alaifari
2023	A*	`conf`	Unifying GANs and Score-Based Diffusion as Generative Particle Models. NeurIPS Jean-Yves Franceschi, Mike Gartrell, Ludovic Dos Santos, Thibaut Issenhuth, Emmanuel de Bézenac, Mickaël Chen, Alain Rakotomamonjy
2023	J	`jnl`	Unifying GANs and Score-Based Diffusion as Generative Particle Models. CoRR Jean-Yves Franceschi, Mike Gartrell, Ludovic Dos Santos, Thibaut Issenhuth, Emmanuel de Bézenac, Mickaël Chen, Alain Rakotomamonjy
2022	A*	`conf`	A Neural Tangent Kernel Perspective of GANs. ICML Jean-Yves Franceschi, Emmanuel de Bézenac, Ibrahim Ayed, Mickaël Chen, Sylvain Lamprier, Patrick Gallinari
2022	A*	`conf`	Mapping conditional distributions for domain adaptation under generalized target shift. ICLR Matthieu Kirchmeyer, Alain Rakotomamonjy, Emmanuel de Bézenac, Patrick Gallinari
2022	J	`jnl`	Modelling spatiotemporal dynamics from Earth observation data with neural differential equations. Mach. Learn. Ibrahim Ayed, Emmanuel de Bézenac, Arthur Pajot, Patrick Gallinari
2022	J	`jnl`	Module-wise Training of Residual Networks via the Minimizing Movement Scheme. CoRR Skander Karkar, Ibrahim Ayed, Emmanuel de Bézenac, Patrick Gallinari
2021	J	`jnl`	A Neural Tangent Kernel Perspective of GANs. CoRR Jean-Yves Franceschi, Emmanuel de Bézenac, Ibrahim Ayed, Mickaël Chen, Sylvain Lamprier, Patrick Gallinari
2021	A*	`conf`	Augmenting Physical Models with Deep Networks for Complex Dynamics Forecasting. ICLR Yuan Yin, Vincent Le Guen, Jérémie Donà, Emmanuel de Bézenac, Ibrahim Ayed, Nicolas Thome, Patrick Gallinari
2021	—	`conf`	CycleGAN Through the Lens of (Dynamical) Optimal Transport. ECML/PKDD (2) Emmanuel de Bézenac, Ibrahim Ayed, Patrick Gallinari
2021	A*	`conf`	LEADS: Learning Dynamical Systems that Generalize Across Environments. NeurIPS Yuan Yin, Ibrahim Ayed, Emmanuel de Bézenac, Nicolas Baskiotis, Patrick Gallinari
2021	J	`jnl`	LEADS: Learning Dynamical Systems that Generalize Across Environments. CoRR Yuan Yin, Ibrahim Ayed, Emmanuel de Bézenac, Nicolas Baskiotis, Patrick Gallinari
2021	—	`conf`	Learning Dynamical Systems across Environments. AAAI Spring Symposium: MLPS Yuan Yin, Ibrahim Ayed, Emmanuel de Bézenac, Patrick Gallinari
2021	J	`jnl`	Mapping conditional distributions for domain adaptation under generalized target shift. CoRR Matthieu Kirchmeyer, Alain Rakotomamonjy, Emmanuel de Bézenac, Patrick Gallinari
2021	—	`—`	Modeling Physical Processes with Deep Learning: A Dynamical Systems Approach. (Modélisation de Processus Physique avec de l'Apprentissage Profond). Emmanuel de Bézenac
2020	—	`conf`	A Principle of Least Action for the Training of Neural Networks. ECML/PKDD (2) Skander Karkar, Ibrahim Ayed, Emmanuel de Bézenac, Patrick Gallinari
2020	J	`jnl`	A Principle of Least Action for the Training of Neural Networks. CoRR Skander Karkar, Ibrahim Ayed, Emmanuel de Bézenac, Patrick Gallinari
2020	J	`jnl`	Augmenting Physical Models with Deep Networks for Complex Dynamics Forecasting. CoRR Vincent Le Guen, Yuan Yin, Jérémie Donà, Ibrahim Ayed, Emmanuel de Bézenac, Nicolas Thome, Patrick Gallinari
2020	A*	`conf`	Deep Rao-Blackwellised Particle Filters for Time Series Forecasting. NeurIPS Richard Kurle, Syama Sundar Rangapuram, Emmanuel de Bézenac, Stephan Günnemann, Jan Gasthaus
2020	Misc	`conf`	Learning the Spatio-Temporal Dynamics of Physical Processes from Partial Observations. ICASSP Ibrahim Ayed, Emmanuel de Bézenac, Arthur Pajot, Patrick Gallinari
2020	A*	`conf`	Normalizing Kalman Filters for Multivariate Time Series Analysis. NeurIPS Emmanuel de Bézenac, Syama Sundar Rangapuram, Konstantinos Benidis, Michael Bohlke-Schneider, Richard Kurle, Lorenzo Stella, Hilaf Hasson, Patrick Gallinari, Tim Januschowski
2019	J	`jnl`	Learning Dynamical Systems from Partial Observations. CoRR Ibrahim Ayed, Emmanuel de Bézenac, Arthur Pajot, Julien Brajard, Patrick Gallinari
2019	J	`jnl`	Optimal Unsupervised Domain Translation. CoRR Emmanuel de Bézenac, Ibrahim Ayed, Patrick Gallinari
2019	J	`jnl`	Unsupervised Adversarial Image Inpainting. CoRR Arthur Pajot, Emmanuel de Bézenac, Patrick Gallinari
2019	—	`conf`	Unsupervised Adversarial Image Reconstruction. ICLR (Poster) Arthur Pajot, Emmanuel de Bézenac, Patrick Gallinari
2018	—	`conf`	Deep Learning for Physical Processes: Incorporating Prior Scientific Knowledge. ICLR (Poster) Emmanuel de Bézenac, Arthur Pajot, Patrick Gallinari
2017	J	`jnl`	Deep Learning for Physical Processes: Incorporating Prior Scientific Knowledge. CoRR Emmanuel de Bézenac, Arthur Pajot, Patrick Gallinari