Deep Learning for Physical Processes: Incorporating Prior Scientific Knowledge. - Emmanuel de Bézenac; Arthur Pajot; Patrick Gallinari (ICLR (Poster), 2018)

Unsupervised Adversarial Image Reconstruction. - Arthur Pajot; Emmanuel de Bézenac; Patrick Gallinari (ICLR (Poster), 2019)

Learning Dynamical Systems from Partial Observations. - Ibrahim Ayed; Emmanuel de Bézenac; Arthur Pajot; Julien Brajard; Patrick Gallinari (CoRR, 2019)

Optimal Unsupervised Domain Translation. - Emmanuel de Bézenac; Ibrahim Ayed; Patrick Gallinari (CoRR, 2019)

Unsupervised Adversarial Image Inpainting. - Arthur Pajot; Emmanuel de Bézenac; Patrick Gallinari (CoRR, 2019)

Learning the Spatio-Temporal Dynamics of Physical Processes from Partial Observations. - Ibrahim Ayed; Emmanuel de Bézenac; Arthur Pajot; Patrick Gallinari (ICASSP, 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 (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)

A Principle of Least Action for the Training of Neural Networks. - Skander Karkar; Ibrahim Ayed; Emmanuel de Bézenac; Patrick Gallinari (ECML/PKDD (2), 2020)

Learning Dynamical Systems across Environments. - Yuan Yin; Ibrahim Ayed; Emmanuel de Bézenac; Patrick Gallinari (AAAI Spring Symposium: MLPS, 2021)

Augmenting Physical Models with Deep Networks for Complex Dynamics Forecasting. - Yuan Yin; Vincent Le Guen; Jérémie Donà; Emmanuel de Bézenac; Ibrahim Ayed; Nicolas Thome; Patrick Gallinari (ICLR, 2021)

LEADS: Learning Dynamical Systems that Generalize Across Environments. - Yuan Yin; Ibrahim Ayed; Emmanuel de Bézenac; Nicolas Baskiotis; Patrick Gallinari (NeurIPS, 2021)

CycleGAN Through the Lens of (Dynamical) Optimal Transport. - Emmanuel de Bézenac; Ibrahim Ayed; Patrick Gallinari (ECML/PKDD (2), 2021)

Modeling Physical Processes with Deep Learning: A Dynamical Systems Approach. (Modélisation de Processus Physique avec de l'Apprentissage Profond). - Emmanuel de Bézenac (2021)

Mapping conditional distributions for domain adaptation under generalized target shift. - Matthieu Kirchmeyer; Alain Rakotomamonjy; Emmanuel de Bézenac; Patrick Gallinari (ICLR, 2022)

A Neural Tangent Kernel Perspective of GANs. - Jean-Yves Franceschi; Emmanuel de Bézenac; Ibrahim Ayed; Mickaël Chen; Sylvain Lamprier; Patrick Gallinari (ICML, 2022)

Module-wise Training of Residual Networks via the Minimizing Movement Scheme. - Skander Karkar; Ibrahim Ayed; Emmanuel de Bézenac; Patrick Gallinari (CoRR, 2022)

Modelling spatiotemporal dynamics from Earth observation data with neural differential equations. - Ibrahim Ayed; Emmanuel de Bézenac; Arthur Pajot; Patrick Gallinari (Mach. Learn., 2022)

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; Mickaël Chen; Alain Rakotomamonjy (NeurIPS, 2023)

Module-wise Training of Neural Networks via the Minimizing Movement Scheme. - Skander Karkar; Ibrahim Ayed; Emmanuel de Bézenac; Patrick Gallinari (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)

Convolutional Neural Operators. - Bogdan Raonic; Roberto Molinaro; Tobias Rohner; Siddhartha Mishra; Emmanuel de Bézenac (CoRR, 2023)

Are Neural Operators Really Neural Operators? Frame Theory Meets Operator Learning. - Francesca Bartolucci; Emmanuel de Bézenac; Bogdan Raonic; Roberto Molinaro; Siddhartha Mishra; Rima Alaifari (CoRR, 2023)

An operator preconditioning perspective on training in physics-informed machine learning. - Tim De Ryck; Florent Bonnet; Siddhartha Mishra; Emmanuel de Bézenac (ICLR, 2024)

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

Poseidon: Efficient Foundation Models for PDEs. - Maximilian Herde; Bogdan Raonic; Tobias Rohner; Roger Käppeli; Roberto Molinaro; Emmanuel de Bézenac; Siddhartha Mishra (NeurIPS, 2024)

Learning a Neural Solver for Parametric PDE to Enhance Physics-Informed Methods. - Lise Le Boudec; Emmanuel de Bézenac; Louis Serrano; Ramon Daniel Regueiro-Espino; Yuan Yin; Patrick Gallinari (CoRR, 2024)

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