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The workshop is postponed to 2024
Recent works in deep learning have demonstrated how to integrate deep
learning techniques and inverse problems. The contributions are twofold:
from one hand, deep learning algorithms can leverage large collections
of training data to directly compute regularized reconstructions. From
the other hand, deep learning algorithms can benefit from the vast
inverse problem literature and the existing amount of contributions to
the theory of inverse problems.
The goal of this workshop is to gather mathematicians, computer
scientists, and engineers from both inverse problems and machine learning
communities.
Talks will take place in the conference room of the mathematics
laboratory (LJAD).
