no code implementations • 18 Mar 2024 • Antoine Schnepf, Karim Kassab, Jean-Yves Franceschi, Laurent Caraffa, Flavian vasile, Jeremie Mary, Andrew Comport, Valérie Gouet-Brunet
We present a method enabling the scaling of NeRFs to learn a large number of semantically-similar scenes.
no code implementations • 13 Dec 2023 • Ilana Sebag, Muni Sreenivas Pydi, Jean-Yves Franceschi, Alain Rakotomamonjy, Mike Gartrell, Jamal Atif, Alexandre Allauzen
Safeguarding privacy in sensitive training data is paramount, particularly in the context of generative modeling.
no code implementations • 1 Dec 2023 • Karim Kassab, Antoine Schnepf, Jean-Yves Franceschi, Laurent Caraffa, Jeremie Mary, Valérie Gouet-Brunet
We carry out extensive experiments and verify the merit of our method on synthetic data and real tourism photo collections.
1 code implementation • NeurIPS 2023 • Jean-Yves Franceschi, Mike Gartrell, Ludovic Dos Santos, Thibaut Issenhuth, Emmanuel de Bézenac, Mickaël Chen, Alain Rakotomamonjy
Particle-based deep generative models, such as gradient flows and score-based diffusion models, have recently gained traction thanks to their striking performance.
1 code implementation • 29 Sep 2022 • Yuan Yin, Matthieu Kirchmeyer, Jean-Yves Franceschi, Alain Rakotomamonjy, Patrick Gallinari
Effective data-driven PDE forecasting methods often rely on fixed spatial and / or temporal discretizations.
1 code implementation • 10 Jun 2021 • Jean-Yves Franceschi, Emmanuel de Bézenac, Ibrahim Ayed, Mickaël Chen, Sylvain Lamprier, Patrick Gallinari
We propose a novel theoretical framework of analysis for Generative Adversarial Networks (GANs).
1 code implementation • ICLR 2021 • Jérémie Donà, Jean-Yves Franceschi, Sylvain Lamprier, Patrick Gallinari
A recent line of work in the machine learning community addresses the problem of predicting high-dimensional spatiotemporal phenomena by leveraging specific tools from the differential equations theory.
1 code implementation • ICML 2020 • Jean-Yves Franceschi, Edouard Delasalles, Mickaël Chen, Sylvain Lamprier, Patrick Gallinari
Designing video prediction models that account for the inherent uncertainty of the future is challenging.
Ranked #1 on Video Prediction on Cityscapes 128x128 (Pred metric)
2 code implementations • NeurIPS 2019 • Jean-Yves Franceschi, Aymeric Dieuleveut, Martin Jaggi
Time series constitute a challenging data type for machine learning algorithms, due to their highly variable lengths and sparse labeling in practice.
no code implementations • 22 Feb 2018 • Jean-Yves Franceschi, Alhussein Fawzi, Omar Fawzi
We study the robustness of classifiers to various kinds of random noise models.