259 citations · 310 across the 3 of their papers we have counts for
3 papers
Unsupervised Adversarial Image Inpainting
Arthur Pajot, Emmanuel de Bezenac, Patrick Gallinari
We consider inpainting in an unsupervised setting where there is neither access to paired nor unpaired training data. The only available information is provided by the uncomplete o…
Learning Dynamical Systems from Partial Observations
Ibrahim Ayed, Emmanuel de Bézenac, Arthur Pajot +2
We consider the problem of forecasting complex, nonlinear space-time processes when observations provide only partial information of on the system's state. We propose a natural dat…
Deep Learning for Physical Processes: Incorporating Prior Scientific Knowledge
Emmanuel de Bezenac, Arthur Pajot, Patrick Gallinari
We consider the use of Deep Learning methods for modeling complex phenomena like those occurring in natural physical processes. With the large amount of data gathered on these phen…