de Bezenac, Emmanuel

21 publications

ICLR 2025 Learning a Neural Solver for Parametric PDEs to Enhance Physics-Informed Methods Lise Le Boudec, Emmanuel de Bezenac, Louis Serrano, Ramon Daniel Regueiro-Espino, Yuan Yin, Patrick Gallinari

ICLR 2024 An Operator Preconditioning Perspective on Training in Physics-Informed Machine Learning Tim De Ryck, Florent Bonnet, Siddhartha Mishra, Emmanuel de Bezenac

ICML 2024 Beyond Regular Grids: Fourier-Based Neural Operators on Arbitrary Domains Levi E. Lingsch, Mike Yan Michelis, Emmanuel De Bezenac, Sirani M. Perera, Robert K. Katzschmann, Siddhartha Mishra

ICLRW 2024 Learning Iterative Algorithms to Solve PDEs. Lise Le Boudec, Emmanuel de Bezenac, Louis Serrano, Yuan Yin, Patrick Gallinari

NeurIPS 2024 Poseidon: Efficient Foundation Models for PDEs Maximilian Herde, Bogdan Raonić, Tobias Rohner, Roger Käppeli, Roberto Molinaro, Emmanuel de Bézenac, Siddhartha Mishra

ICLRW 2023 Convolutional Neural Operators Bogdan Raonic, Roberto Molinaro, Tobias Rohner, Siddhartha Mishra, Emmanuel de Bezenac

NeurIPS 2023 Convolutional Neural Operators for Robust and Accurate Learning of PDEs Bogdan Raonic, Roberto Molinaro, Tim De Ryck, Tobias Rohner, Francesca Bartolucci, Rima Alaifari, Siddhartha Mishra, Emmanuel de Bézenac

NeurIPS 2023 Module-Wise Training of Neural Networks via the Minimizing Movement Scheme Skander Karkar, Ibrahim Ayed, Emmanuel de Bézenac, Patrick Gallinari

NeurIPS 2023 Representation Equivalent Neural Operators: A Framework for Alias-Free Operator Learning Francesca Bartolucci, Emmanuel de Bézenac, Bogdan Raonic, Roberto Molinaro, Siddhartha Mishra, Rima Alaifari

NeurIPS 2023 Unifying GANs and Score-Based Diffusion as Generative Particle Models Jean-Yves Franceschi, Mike Gartrell, Ludovic Dos Santos, Thibaut Issenhuth, Emmanuel de Bézenac, Mickael Chen, Alain Rakotomamonjy

ICML 2022 A Neural Tangent Kernel Perspective of GANs Jean-Yves Franceschi, Emmanuel De Bézenac, Ibrahim Ayed, Mickael Chen, Sylvain Lamprier, Patrick Gallinari

ICLR 2022 Mapping Conditional Distributions for Domain Adaptation Under Generalized Target Shift Matthieu Kirchmeyer, Alain Rakotomamonjy, Emmanuel de Bezenac, Patrick Gallinari

MLJ 2022 Modelling Spatiotemporal Dynamics from Earth Observation Data with Neural Differential Equations Ibrahim Ayed, Emmanuel de Bézenac, Arthur Pajot, Patrick Gallinari

ICLR 2021 Augmenting Physical Models with Deep Networks for Complex Dynamics Forecasting Yuan Yin, Vincent Le Guen, Jérémie Dona, Emmanuel de Bezenac, Ibrahim Ayed, Nicolas Thome, Patrick Gallinari

ECML-PKDD 2021 CycleGAN Through the Lens of (Dynamical) Optimal Transport Emmanuel de Bézenac, Ibrahim Ayed, Patrick Gallinari

NeurIPS 2021 LEADS: Learning Dynamical Systems That Generalize Across Environments Yuan Yin, Ibrahim Ayed, Emmanuel de Bézenac, Nicolas Baskiotis, Patrick Gallinari

ECML-PKDD 2020 A Principle of Least Action for the Training of Neural Networks Skander Karkar, Ibrahim Ayed, Emmanuel de Bézenac, Patrick Gallinari

NeurIPS 2020 Deep Rao-Blackwellised Particle Filters for Time Series Forecasting Richard Kurle, Syama Sundar Rangapuram, Emmanuel de Bézenac, Stephan Günnemann, Jan Gasthaus

NeurIPS 2020 Normalizing Kalman Filters for Multivariate Time Series Analysis Emmanuel de Bézenac, Syama Sundar Rangapuram, Konstantinos Benidis, Michael Bohlke-Schneider, Richard Kurle, Lorenzo Stella, Hilaf Hasson, Patrick Gallinari, Tim Januschowski

ICLR 2019 Unsupervised Adversarial Image Reconstruction Arthur Pajot, Emmanuel de Bezenac, Patrick Gallinari

ICLR 2018 Deep Learning for Physical Processes: Incorporating Prior Scientific Knowledge Emmanuel de Bezenac, Arthur Pajot, Patrick Gallinari