Rémi Flamary is currently a Monge Assistant Professor at the Applied Mathematics department and CMAP Laboratory from École Polytechnique.
Research topic | Optimal transport for machine learning
The main objective of this project is to change the way we learn from empirical data using optimal transport. We will first investigate optimal transport for transfer learning with biomedical and astronomical applications. Second, we will adapt the Gromov-Wasserstein distance for structured data and transfer between deep learning models with different architectures.
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