Jean-Yves Franceschi

According to our database, Jean-Yves Franceschi authored at least 18 papers between 2018 and 2025.

Collaborative distances:

• Dijkstra number of four.
• Erdős number of four.

2025

On the MIA Vulnerability Gap Between Private GANs and Diffusion Models.

Ilana Sebag, Jean-Yves Franceschi, Alain Rakotomamonjy, Alexandre Allauzen, Jamal Atif

CoRR, September, 2025

Differentially Private Gradient Flow based on the Sliced Wasserstein Distance.

Ilana Sebag, Muni Sreenivas Pydi, Jean-Yves Franceschi, Alain Rakotomamonjy, Mike Gartrell, Jamal Atif, Alexandre Allauzen

Trans. Mach. Learn. Res., 2025

RefinedFields: Radiance Fields Refinement for Planar Scene Representations.

Karim Kassab, Antoine Schnepf, Jean-Yves Franceschi, Laurent Caraffa, Jérémie Mary, Valérie Gouet-Brunet

Trans. Mach. Learn. Res., 2025

Bringing NeRFs to the Latent Space: Inverse Graphics Autoencoder.

Antoine Schnepf, Karim Kassab, Jean-Yves Franceschi, Laurent Caraffa, Flavian Vasile, Jérémie Mary, Andrew I. Comport, Valérie Gouet-Brunet

Proceedings of the Thirteenth International Conference on Learning Representations, 2025

2024

Scaled Inverse Graphics: Efficiently Learning Large Sets of 3D Scenes.

Karim Kassab, Antoine Schnepf, Jean-Yves Franceschi, Laurent Caraffa, Flavian Vasile, Jérémie Mary, Andrew I. Comport, Valérie Gouet-Brunet

CoRR, 2024

Improving Consistency Models with Generator-Induced Coupling.

Thibaut Issenhuth, Ludovic Dos Santos, Jean-Yves Franceschi, Alain Rakotomamonjy

CoRR, 2024

Exploring 3D-aware Latent Spaces for Efficiently Learning Numerous Scenes.

Antoine Schnepf, Karim Kassab, Jean-Yves Franceschi, Laurent Caraffa, Flavian Vasile, Jérémie Mary, Andrew I. Comport, Valérie Gouet-Brunet

CoRR, 2024

2023

Differentially Private Gradient Flow based on the Sliced Wasserstein Distance for Non-Parametric Generative Modeling.

Ilana Sebag, Muni Sreenivas Pydi, Jean-Yves Franceschi, Alain Rakotomamonjy, Mike Gartrell, Jamal Atif, Alexandre Allauzen

CoRR, 2023

EvE: Exploiting Generative Priors for Radiance Field Enrichment.

Karim Kassab, Antoine Schnepf, Jean-Yves Franceschi, Laurent Caraffa, Jérémie Mary, Valérie Gouet-Brunet

CoRR, 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

Proceedings of the Advances in Neural Information Processing Systems 36: Annual Conference on Neural Information Processing Systems 2023, 2023

Continuous PDE Dynamics Forecasting with Implicit Neural Representations.

Yuan Yin, Matthieu Kirchmeyer, Jean-Yves Franceschi, Alain Rakotomamonjy, Patrick Gallinari

Proceedings of the Eleventh International Conference on Learning Representations, 2023

2022

Representation Learning and Deep Generative Modeling in Dynamical Systems. (Apprentissage de représentations et modèles génératifs profonds dans les systèmes dynamiques).

Jean-Yves Franceschi

PhD thesis, 2022

A Neural Tangent Kernel Perspective of GANs.

Jean-Yves Franceschi, Emmanuel de Bézenac, Ibrahim Ayed, Mickaël Chen, Sylvain Lamprier, Patrick Gallinari

Proceedings of the International Conference on Machine Learning, 2022

2021

PDE-Driven Spatiotemporal Disentanglement.

Jérémie Donà, Jean-Yves Franceschi, Sylvain Lamprier, Patrick Gallinari

Proceedings of the 9th International Conference on Learning Representations, 2021

2020

Stochastic Latent Residual Video Prediction.

Jean-Yves Franceschi, Edouard Delasalles, Mickaël Chen, Sylvain Lamprier, Patrick Gallinari

Proceedings of the 37th International Conference on Machine Learning, 2020

2019

Unsupervised Scalable Representation Learning for Multivariate Time Series.

Jean-Yves Franceschi, Aymeric Dieuleveut, Martin Jaggi

Proceedings of the Advances in Neural Information Processing Systems 32: Annual Conference on Neural Information Processing Systems 2019, 2019

2018

Robustness of classifiers to uniform 𝓁_p and Gaussian noise.

Jean-Yves Franceschi, Alhussein Fawzi, Omar Fawzi

CoRR, 2018

Robustness of classifiers to uniform $\ell_p$ and Gaussian noise.

Jean-Yves Franceschi, Alhussein Fawzi, Omar Fawzi

Proceedings of the International Conference on Artificial Intelligence and Statistics, 2018