Search Results for author: Emmanuel de Bézenac

Found 10 papers, 4 papers with code

A Neural Tangent Kernel Perspective of GANs

2 code implementations10 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).

LEADS: Learning Dynamical Systems that Generalize Across Environments

1 code implementation NeurIPS 2021 Yuan Yin, Ibrahim Ayed, Emmanuel de Bézenac, Nicolas Baskiotis, Patrick Gallinari

Both are sub-optimal: the former disregards the discrepancies between environments leading to biased solutions, while the latter does not exploit their potential commonalities and is prone to scarcity problems.

Deep Rao-Blackwellised Particle Filters for Time Series Forecasting

no code implementations NeurIPS 2020 Richard Kurle, Syama Sundar Rangapuram, Emmanuel de Bézenac, Stephan Günnemann, Jan Gasthaus

We propose a Monte Carlo objective that leverages the conditional linearity by computing the corresponding conditional expectations in closed-form and a suitable proposal distribution that is factorised similarly to the optimal proposal distribution.

Time Series Time Series Forecasting

Augmenting Physical Models with Deep Networks for Complex Dynamics Forecasting

1 code implementation ICLR 2021 Yuan Yin, Vincent Le Guen, Jérémie Dona, Emmanuel de Bézenac, Ibrahim Ayed, Nicolas Thome, Patrick Gallinari

In this work, we introduce the APHYNITY framework, a principled approach for augmenting incomplete physical dynamics described by differential equations with deep data-driven models.

A Principle of Least Action for the Training of Neural Networks

1 code implementation17 Sep 2020 Skander Karkar, Ibrahim Ayed, Emmanuel de Bézenac, Patrick Gallinari

From this observation, we reformulate the learning problem as follows: finding neural networks which solve the task while transporting the data as efficiently as possible.

Learning Theory

Unsupervised Spatiotemporal Data Inpainting

no code implementations25 Sep 2019 Yuan Yin, Arthur Pajot, Emmanuel de Bézenac, Patrick Gallinari

We tackle the problem of inpainting occluded area in spatiotemporal sequences, such as cloud occluded satellite observations, in an unsupervised manner.

Optimal Unsupervised Domain Translation

no code implementations4 Jun 2019 Emmanuel de Bézenac, Ibrahim Ayed, Patrick Gallinari

Domain Translation is the problem of finding a meaningful correspondence between two domains.


Learning Dynamical Systems from Partial Observations

no code implementations26 Feb 2019 Ibrahim Ayed, Emmanuel de Bézenac, Arthur Pajot, Julien Brajard, Patrick Gallinari

We consider the problem of forecasting complex, nonlinear space-time processes when observations provide only partial information of on the system's state.

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