4 citations · 8 across the 9 of their papers we have counts for
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cs.CV2023★ 4 cited
Unbalancedness in Neural Monge Maps Improves Unpaired Domain Translation
Luca Eyring, Dominik Klein, Théo Uscidda +4
In optimal transport (OT), a Monge map is known as a mapping that transports a source distribution to a target distribution in the most cost-efficient way. Recently, multiple neura…
stat.ML2023
GENOT: Entropic (Gromov) Wasserstein Flow Matching with Applications to Single-Cell Genomics
Dominik Klein, Théo Uscidda, Fabian Theis +1
Single-cell genomics has significantly advanced our understanding of cellular behavior, catalyzing innovations in treatments and precision medicine. However, single-cell sequencing…
cs.LG2023★ 4 cited
The Monge Gap: A Regularizer to Learn All Transport Maps
Théo Uscidda, Marco Cuturi
Optimal transport (OT) theory has been been used in machine learning to study and characterize maps that can push-forward efficiently a probability measure onto another. Recent wor…