Emmanuel de Bézenac

Publication Activity (10 Years)

Years Active: 2017-2024
Publications (10 Years): 34

Top Topics

Physical Processes

Image Inpainting

Differential Equations

Top Venues

Publications

Maximilian Herde, Bogdan Raonic, Tobias Rohner, Roger Käppeli, Roberto Molinaro, Emmanuel de Bézenac, Siddhartha Mishra
Poseidon: Efficient Foundation Models for PDEs. CoRR (2024)
Tim De Ryck, Florent Bonnet, Siddhartha Mishra, Emmanuel de Bézenac
An operator preconditioning perspective on training in physics-informed machine learning. ICLR (2024)
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)
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 (2023)
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 (2023)
Skander Karkar, Ibrahim Ayed, Emmanuel de Bézenac, Patrick Gallinari
Module-wise Training of Neural Networks via the Minimizing Movement Scheme. NeurIPS (2023)
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)
Bogdan Raonic, Roberto Molinaro, Tobias Rohner, Siddhartha Mishra, Emmanuel de Bézenac
Convolutional Neural Operators. CoRR (2023)
Tim De Ryck, Florent Bonnet, Siddhartha Mishra, Emmanuel de Bézenac
An operator preconditioning perspective on training in physics-informed machine learning. CoRR (2023)
Skander Karkar, Ibrahim Ayed, Emmanuel de Bézenac, Patrick Gallinari
Module-wise Training of Neural Networks via the Minimizing Movement Scheme. CoRR (2023)
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)
Matthieu Kirchmeyer, Alain Rakotomamonjy, Emmanuel de Bézenac, Patrick Gallinari
Mapping conditional distributions for domain adaptation under generalized target shift. ICLR (2022)
Jean-Yves Franceschi, Emmanuel de Bézenac, Ibrahim Ayed, Mickaël Chen, Sylvain Lamprier, Patrick Gallinari
A Neural Tangent Kernel Perspective of GANs. ICML (2022)
Ibrahim Ayed, Emmanuel de Bézenac, Arthur Pajot, Patrick Gallinari
Modelling spatiotemporal dynamics from Earth observation data with neural differential equations. Mach. Learn. 111 (6) (2022)
Skander Karkar, Ibrahim Ayed, Emmanuel de Bézenac, Patrick Gallinari
Module-wise Training of Residual Networks via the Minimizing Movement Scheme. CoRR (2022)
Yuan Yin, Ibrahim Ayed, Emmanuel de Bézenac, Patrick Gallinari
Learning Dynamical Systems across Environments. AAAI Spring Symposium: MLPS (2021)
Jean-Yves Franceschi, Emmanuel de Bézenac, Ibrahim Ayed, Mickaël Chen, Sylvain Lamprier, Patrick Gallinari
A Neural Tangent Kernel Perspective of GANs. CoRR (2021)
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)
Yuan Yin, Ibrahim Ayed, Emmanuel de Bézenac, Nicolas Baskiotis, Patrick Gallinari
LEADS: Learning Dynamical Systems that Generalize Across Environments. CoRR (2021)
Matthieu Kirchmeyer, Alain Rakotomamonjy, Emmanuel de Bézenac, Patrick Gallinari
Mapping conditional distributions for domain adaptation under generalized target shift. CoRR (2021)
Yuan Yin, Ibrahim Ayed, Emmanuel de Bézenac, Nicolas Baskiotis, Patrick Gallinari
LEADS: Learning Dynamical Systems that Generalize Across Environments. NeurIPS (2021)
Emmanuel de Bézenac, Ibrahim Ayed, Patrick Gallinari
CycleGAN Through the Lens of (Dynamical) Optimal Transport. ECML/PKDD (2) (2021)
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)
Vincent Le Guen, Yuan Yin, Jérémie Donà, Ibrahim Ayed, Emmanuel de Bézenac, Nicolas Thome, Patrick Gallinari
Augmenting Physical Models with Deep Networks for Complex Dynamics Forecasting. CoRR (2020)
Skander Karkar, Ibrahim Ayed, Emmanuel de Bézenac, Patrick Gallinari
A Principle of Least Action for the Training of Neural Networks. CoRR (2020)
Ibrahim Ayed, Emmanuel de Bézenac, Arthur Pajot, Patrick Gallinari
Learning the Spatio-Temporal Dynamics of Physical Processes from Partial Observations. ICASSP (2020)
Skander Karkar, Ibrahim Ayed, Emmanuel de Bézenac, Patrick Gallinari
A Principle of Least Action for the Training of Neural Networks. ECML/PKDD (2) (2020)
Richard Kurle, Syama Sundar Rangapuram, Emmanuel de Bézenac, Stephan Günnemann, Jan Gasthaus
Deep Rao-Blackwellised Particle Filters for Time Series Forecasting. NeurIPS (2020)
Emmanuel de Bézenac, Ibrahim Ayed, Patrick Gallinari
Optimal Unsupervised Domain Translation. CoRR (2019)
Arthur Pajot, Emmanuel de Bézenac, Patrick Gallinari
Unsupervised Adversarial Image Inpainting. CoRR (2019)
Arthur Pajot, Emmanuel de Bézenac, Patrick Gallinari
Unsupervised Adversarial Image Reconstruction. ICLR (Poster) (2019)
Ibrahim Ayed, Emmanuel de Bézenac, Arthur Pajot, Julien Brajard, Patrick Gallinari
Learning Dynamical Systems from Partial Observations. CoRR (2019)
Emmanuel de Bézenac, Arthur Pajot, Patrick Gallinari
Deep Learning for Physical Processes: Incorporating Prior Scientific Knowledge. ICLR (Poster) (2018)
Emmanuel de Bézenac, Arthur Pajot, Patrick Gallinari
Deep Learning for Physical Processes: Incorporating Prior Scientific Knowledge. CoRR (2017)