Search Results for author: Jean-Yves Franceschi

Found 9 papers, 6 papers with code

Unifying GANs and Score-Based Diffusion as Generative Particle Models

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.

Continuous PDE Dynamics Forecasting with Implicit Neural Representations

1 code implementation29 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.

A Neural Tangent Kernel Perspective of GANs

1 code implementation10 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).

PDE-Driven Spatiotemporal Disentanglement

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.

Disentanglement

Unsupervised Scalable Representation Learning for Multivariate Time Series

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.

BIG-bench Machine Learning Representation Learning +2

Robustness of classifiers to uniform $\ell\_p$ and Gaussian noise

no code implementations22 Feb 2018 Jean-Yves Franceschi, Alhussein Fawzi, Omar Fawzi

We study the robustness of classifiers to various kinds of random noise models.

Cannot find the paper you are looking for? You can Submit a new open access paper.